Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal ...Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.展开更多
Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes...Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).展开更多
Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,a...Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,and stage of diagnosis,which directly impacts clinical decision-making.Various biological entities,including genes,proteins,mRNAs,miRNAs,and metabolites,contribute to cancer development.The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers.This integrative approach supports the notion that cancer is fundamentally driven by such alterations,enabling the discovery ofmolecular signatures for precision oncology.This reviewexplores the role of AI-drivenmulti-omics analyses in cancer medicine,emphasizing their potential to identify novel biomarkers and therapeutic targets,enhance understanding of Tumor biology,and address integration challenges in clinical workflows.Network biology analyzes identified ERBB2,KRAS,and TP53 as top hub genes in lung cancer based on Maximal Clique Centrality(MCC)scores.In contrast,TP53,ERBB2,ESR1,MYC,and BRCA1 emerged as central regulators in breast cancer,linked to cell proliferation,hormonal signaling,and genomic stability.The review also discusses how specific Artificial Intelligence(AI)algorithms can streamline the integration of heterogeneous datasets,facilitate the interpretation of the tumor microenvironment,and support data-driven clinical strategies.展开更多
The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyeffic...The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.展开更多
The development of human-robot interaction has been continu-ously increasing for the last decades.Through this development,it has become simpler and safe interactions using a remotely controlled telepresence robot in ...The development of human-robot interaction has been continu-ously increasing for the last decades.Through this development,it has become simpler and safe interactions using a remotely controlled telepresence robot in an insecure and hazardous environment.The audio-video communication connection or data transmission stability has already been well handled by fast-growing technologies such as 5G and 6G.However,the design of the phys-ical parameters,e.g.,maneuverability,controllability,and stability,still needs attention.Therefore,the paper aims to present a systematic,controlled design and implementation of a telepresence mobile robot.The primary focus of this paper is to perform the computational analysis and experimental implementa-tion design with sophisticated position control,which autonomously controls the robot’s position and speed when reaching an obstacle.A system model and a position controller design are developed with root locus points.The design robot results are verified experimentally,showing the robot’s agreement and control in the desired position.The robot was tested by considering various parameters:driving straight ahead,right turn,self-localization and complex path.The results prove that the proposed approach is flexible and adaptable and gives a better alternative.The experimental results show that the proposed method significantly minimizes the obstacle hits.展开更多
This paper presents Dual Adaptive Neural Topology(Dual ANT),a distributed dual-network metaadaptive framework that enhances ant-colony-based multi-agent coordination with online introspection,adaptive parameter contro...This paper presents Dual Adaptive Neural Topology(Dual ANT),a distributed dual-network metaadaptive framework that enhances ant-colony-based multi-agent coordination with online introspection,adaptive parameter control,and privacy-preserving interactions.This approach improves standard Ant Colony Optimization(ACO)with two lightweight neural components:a forward network that estimates swarm efficiency in real time and an inverse network that converts these descriptors into parameter adaptations.To preserve the privacy of individual trajectories in shared pheromone maps,we introduce a locally differentially private pheromone update mechanism that adds calibrated noise to each agent’s pheromone deposit while preserving the efficacy of the global pheromone signal.The resulting systemenables agents to dynamically and autonomously adapt their coordination strategies under challenging and dynamic conditions,including varying obstacle layouts,uncertain target locations,and time-varying disturbances.Extensive simulations of large grid-based search tasks demonstrated that Dual ANT achieved faster convergence,higher robustness,and improved scalability compared to advanced baselines such asMulti-StrategyACO and Hierarchical ACO.The meta-adaptive feedback loop compensates for the performance degradation caused by privacy noise and prevents premature stagnation by triggering Levy flight exploration only when necessary.展开更多
The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integra...The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.展开更多
Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted featur...Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.展开更多
Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
The concept of Human Activity Recognition(HAR)is integral to applications based on Internet of Things(IoT)-enabled devices,particularly in healthcare,fitness tracking,and smart environments.The streams of data from we...The concept of Human Activity Recognition(HAR)is integral to applications based on Internet of Things(IoT)-enabled devices,particularly in healthcare,fitness tracking,and smart environments.The streams of data from wearable sensors are rich in information,yet their high dimensionality and variability pose a significant challenge to proper classification.To address this problem,this paper proposes hybrid architectures that integrate traditional machine learning models with a deep neural network(DNN)to deliver improved performance and enhanced capabilities for HAR tasks.Multi-sensor HAR data were used to systematically test several hybrid models,including:RF+DNN(Random Forest+Deep Neural Network),XGB+DNN(XGBoost+DNN),GB+DNN(Gradient Boosting+DNN),KNN+DNN(K-Nearest Neighbors+DNN),and DT+DNN(Decision Tree+DNN).The RF+DNN model was the most accurate,achieving a 97.03%score with excellent precision,recall,and F1-score.These findings demonstrate that hybrid machine learning and deep learning systems have a promising future in IoT-based HAR applications.The model provides a novel solution for developing smart and trustworthy monitoring systems that support real-time analytics,patient surveillance,and other IoT applications.展开更多
Accurate,up to date,and quick information related to any disaster supports disaster management team/authorities to perform quick,easy,and cost-effective response to enhance rescue operations to alleviate the possible ...Accurate,up to date,and quick information related to any disaster supports disaster management team/authorities to perform quick,easy,and cost-effective response to enhance rescue operations to alleviate the possible loss of lives,financial risks,and properties.Due to damaged infrastructure in disaster-affected areas,social media is the only way to share/exchange real time information.Therefore,‘X’(formerly Twitter)has become a major platform for disseminating real-time information during disaster events or emergencies,i.e.,floods and earthquake.Rapid identification of actionable content is critical for effective humanitarian response;however,the brief and noisy nature of tweets makes automated classification challenging.To tackle this problem,this study proposes a hybrid classification framework that integrates term frequency–inverse document frequency(TF-IDF)features with graph convolutional networks(GCNs)to enhance disaster-related tweet analysis.The proposed model performs three classification tasks:identifying disaster-related tweets(achieving 94.47%accuracy),categorizing disaster types(earthquake,flood,and non-disaster)with 91.78%accuracy,and detecting aid requests such as food,donations,and medical assistance(94.64%accuracy).By combining the statistical strengths of TF-IDF with the relational learning capabilities of GCNs,the model attains high accuracy while maintaining computational efficiency and interpretability.The results demonstrate the framework’s strong potential for real-time disaster response,offering valuable insights to support emergency management systems and humanitarian decision-making.展开更多
In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we devel...In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.展开更多
Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex dataset...Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.展开更多
Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationall...Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.展开更多
Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(I...Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(ITS).Integrating CPS-ITS and IoT provides real-time Vehicle-to-Infrastructure(V2I)communication,supporting better traffic management,safety,and efficiency.These technological innovations generate complex problems that need to be addressed,uniquely about data routing and Task Scheduling(TS)in ITS.Attempts to solve those problems were primarily based on traditional and experimental methods,and the solutions were not so successful due to the dynamic nature of ITS.This is where the scope of Machine learning(ML)and Swarm Intelligence(SI)has significantly impacted dealing with these challenges;in this line,this research paper presents a novel method for TS and data routing in the CPS-ITS.This paper proposes using a cutting-edge ML algorithm for data transmission from CPS-ITS.This ML has Gated Linear Unit-approximated Reinforcement Learning(GLRL).Greedy Iterative-Particle Swarm Optimization(GI-PSO)has been recommended to develop the Particle Swarm Optimization(PSO)for TS.The primary objective of this study is to enhance the security and effectiveness of ITS systems that utilize CPS-ITS.This study trained and validated the models using a network simulation dataset of 50 nodes from numerous ITS environments.The experiments demonstrate that the proposed GLRL reduces End-toEnd Delay(EED)by 12%,enhances data size use from 83.6%to 88.6%,and achieves higher bandwidth allocation,particularly in high-demand scenarios such as multimedia data streams where adherence improved to 98.15%.Furthermore,the GLRL reduced Network Congestion(NC)by 5.5%,demonstrating its efficiency in managing complex traffic conditions across several environments.The model passed simulation tests in three different environments:urban(UE),suburban(SE),and rural(RE).It met the high bandwidth requirements,made task scheduling more efficient,and increased network throughput(NT).This proved that it was robust and flexible enough for scalable ITS applications.These innovations provide robust,scalable solutions for real-time traffic management,ultimately improving safety,reducing NC,and increasing overall NT.This study can affect ITS by developing it to be more responsive,safe,and effective and by creating a perfect method to set up UE,SE,and RE.展开更多
The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obsta...The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obstacle avoidance.Selecting the correct components is a critical part of the design process.However,this can be a particularly difficult task,especially for novices as there are several technologies and components available on the market,each with their own individual advantages and disadvantages.For example,satellite-based navigation components should be avoided when designing indoor UAVs.Incorporating them in the design brings no added value to the final product and will simply lead to increased cost and power consumption.Another issue is the number of vendors on the market,each trying to sell their hardware solutions which often incorporate similar technologies.The aim of this paper is to serve as a guide,proposing various methods to support the selection of fit-for-purpose technologies and components whilst avoiding system layout conflicts.The paper presents a study of the various navigation technologies and supports engineers in the selection of specific hardware solutions based on given requirements.The selection methods are based on easy-to-follow flow charts.A comparison of the various hardware components specifications is also included as part of this work.展开更多
Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study h...Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study has executed the forecastingmodel for predicting the possible number of COVID-19 cases in Saudi Arabia till a defined period.Towards this intent,the study analyzed different age groups of patients(child,adult,elderly)who were affected by COVID-19.The analysis was done city-wise and also included the number of recoveries recorded in different cities.Furthermore,the study also discusses the impact of COVID-19 on the economy.For conducting the stated analysis,the authors have created a list of factors that are known to cause the spread of COVID-19.As an effective countermeasure to contain the spread of Coronavirus in Saudi Arabia,this study also proposes to identify the most effective Computer Science technique that can be used by healthcare professionals.For this,the study employs the Fuzzy-Analytic Hierarchy Process integrated with the Technique for Order Performance by Similar to Ideal Solution(F.AHP.TOPSIS).After prioritizing the various Computer Science techniques,the ranking order that was obtained for the different techniques/tools to contain COVID-19 was:A4>A1>A2>A5>A3.Since the Blockchain technique obtained the highest priority,the study recommends that it must be used extensively as an efficacious and accurate means to combat COVID-19.展开更多
Ever since its outbreak in the Wuhan city of China,COVID-19 pandemic has engulfed more than 211 countries in the world,leaving a trail of unprecedented fatalities.Even more debilitating than the infection itself,were ...Ever since its outbreak in the Wuhan city of China,COVID-19 pandemic has engulfed more than 211 countries in the world,leaving a trail of unprecedented fatalities.Even more debilitating than the infection itself,were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus.Such enforced alienation affected both the mental and social condition of people significantly.Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement.However,COVID-19 brought all such communication to a grinding halt.Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets.The pandemic has shoved the entire planet into an unstable state.The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia.To achieve this objective,the study analyzes two perspectives:the early approach,and the late approach of COVID-19 and the consequent effects on different aspects of the society.We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society.Findings of this research study indicate that financial resources were the worst affected.Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people.Yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.展开更多
The basic unit in life is cell.?It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these p...The basic unit in life is cell.?It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these proteins. But proteins in different organelles or subcellular locations have different functions. Facing?the avalanche of protein sequences generated in the postgenomic age, we are challenged to develop high throughput tools for identifying the subcellular localization of proteins based on their sequence information alone. Although considerable efforts have been made in this regard, the problem is far apart from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for drug targets. Using the ML-GKR (Multi-Label Gaussian Kernel Regression) method,?we developed a new predictor called “pLoc-mGpos” by in-depth extracting the key information from GO (Gene Ontology) into the Chou’s general PseAAC (Pseudo Amino Acid Composition)?for predicting the subcellular localization of Gram-positive bacterial proteins with both single and multiple location sites. Rigorous cross-validation on a same stringent benchmark dataset indicated that the proposed pLoc-mGpos predictor is remarkably superior to “iLoc-Gpos”, the state-of-the-art predictor for the same purpose.?To maximize the convenience of most experimental scientists, a user-friendly web-server for the new powerful predictor has been established at http://www.jci-bioinfo.cn/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.展开更多
The massive technological advancements around the world have created significant challenging competition among companies where each of the companies tries to attract the customers using different techniques. One of th...The massive technological advancements around the world have created significant challenging competition among companies where each of the companies tries to attract the customers using different techniques. One of the recent tech- niques is Augmented Reality (AR). The AR is a new technology which is capable of presenting possibilities that are difficult for other technologies to offer and meet. Nowadays, numerous augmented reality applications have been used in the industry of different kinds and disseminated all over the world. AR will really alter the way individuals view the world. The AR is yet in its initial phases of research and development at different colleges and high-tech institutes. Throughout the last years, AR apps became transportable and generally available on various devices. Besides, AR be- gins to occupy its place in our audio-visual media and to be used in various fields in our life in tangible and exciting ways such as news, sports and is used in many domains in our life such as electronic commerce, promotion, design, and business. In addition, AR is used to facilitate the learning whereas it enables students to access location-specific infor- mation provided through various sources. Such growth and spread of AR applications pushes organizations to compete one another, every one of them exerts its best to gain the customers. This paper provides a comprehensive study of AR including its history, architecture, applications, current challenges and future trends.展开更多
文摘Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.
基金funded by the Northern Border University,Arar,KSA,under the project number“NBU-FFR-2025-3555-07”.
文摘Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).
基金funded by KAU Endowment(WAQF)at King Abdulaziz University,Jeddah,Saudi Arabia.
文摘Cancer deaths and new cases worldwide are projected to rise by 47%by 2040,with transitioning countries experiencing an even higher increase of up to 95%.Tumor severity is profoundly influenced by the timing,accuracy,and stage of diagnosis,which directly impacts clinical decision-making.Various biological entities,including genes,proteins,mRNAs,miRNAs,and metabolites,contribute to cancer development.The emergence of multi-omics technologies has transformed cancer research by revealing molecular alterations across multiple biological layers.This integrative approach supports the notion that cancer is fundamentally driven by such alterations,enabling the discovery ofmolecular signatures for precision oncology.This reviewexplores the role of AI-drivenmulti-omics analyses in cancer medicine,emphasizing their potential to identify novel biomarkers and therapeutic targets,enhance understanding of Tumor biology,and address integration challenges in clinical workflows.Network biology analyzes identified ERBB2,KRAS,and TP53 as top hub genes in lung cancer based on Maximal Clique Centrality(MCC)scores.In contrast,TP53,ERBB2,ESR1,MYC,and BRCA1 emerged as central regulators in breast cancer,linked to cell proliferation,hormonal signaling,and genomic stability.The review also discusses how specific Artificial Intelligence(AI)algorithms can streamline the integration of heterogeneous datasets,facilitate the interpretation of the tumor microenvironment,and support data-driven clinical strategies.
基金funded by the deanship of scientific research(DSR),King Abdukaziz University,Jeddah,under grant No.(G-1436-611-225)。
文摘The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient communication.This study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication performance.Unlike traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter communication.We propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase shifts.By optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy efficiency.Notably,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS deployments.Our results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G communication.This work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.
基金supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University under the research project (PSAU/2023/01/23001).
文摘The development of human-robot interaction has been continu-ously increasing for the last decades.Through this development,it has become simpler and safe interactions using a remotely controlled telepresence robot in an insecure and hazardous environment.The audio-video communication connection or data transmission stability has already been well handled by fast-growing technologies such as 5G and 6G.However,the design of the phys-ical parameters,e.g.,maneuverability,controllability,and stability,still needs attention.Therefore,the paper aims to present a systematic,controlled design and implementation of a telepresence mobile robot.The primary focus of this paper is to perform the computational analysis and experimental implementa-tion design with sophisticated position control,which autonomously controls the robot’s position and speed when reaching an obstacle.A system model and a position controller design are developed with root locus points.The design robot results are verified experimentally,showing the robot’s agreement and control in the desired position.The robot was tested by considering various parameters:driving straight ahead,right turn,self-localization and complex path.The results prove that the proposed approach is flexible and adaptable and gives a better alternative.The experimental results show that the proposed method significantly minimizes the obstacle hits.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,under project number NBU-FFR-2026-2441-02.
文摘This paper presents Dual Adaptive Neural Topology(Dual ANT),a distributed dual-network metaadaptive framework that enhances ant-colony-based multi-agent coordination with online introspection,adaptive parameter control,and privacy-preserving interactions.This approach improves standard Ant Colony Optimization(ACO)with two lightweight neural components:a forward network that estimates swarm efficiency in real time and an inverse network that converts these descriptors into parameter adaptations.To preserve the privacy of individual trajectories in shared pheromone maps,we introduce a locally differentially private pheromone update mechanism that adds calibrated noise to each agent’s pheromone deposit while preserving the efficacy of the global pheromone signal.The resulting systemenables agents to dynamically and autonomously adapt their coordination strategies under challenging and dynamic conditions,including varying obstacle layouts,uncertain target locations,and time-varying disturbances.Extensive simulations of large grid-based search tasks demonstrated that Dual ANT achieved faster convergence,higher robustness,and improved scalability compared to advanced baselines such asMulti-StrategyACO and Hierarchical ACO.The meta-adaptive feedback loop compensates for the performance degradation caused by privacy noise and prevents premature stagnation by triggering Levy flight exploration only when necessary.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under Grant No.(GPIP:1074-612-2024).
文摘The surge in smishing attacks underscores the urgent need for robust,real-time detection systems powered by advanced deep learning models.This paper introduces PhishNet,a novel ensemble learning framework that integrates transformer-based models(RoBERTa)and large language models(LLMs)(GPT-OSS 120B,LLaMA3.370B,and Qwen332B)to enhance smishing detection performance significantly.To mitigate class imbalance,we apply synthetic data augmentation using T5 and leverage various text preprocessing techniques.Our system employs a duallayer voting mechanism:weighted majority voting among LLMs and a final ensemble vote to classify messages as ham,spam,or smishing.Experimental results show an average accuracy improvement from 96%to 98.5%compared to the best standalone transformer,and from 93%to 98.5%when compared to LLMs across datasets.Furthermore,we present a real-time,user-friendly application to operationalize our detection model for practical use.PhishNet demonstrates superior scalability,usability,and detection accuracy,filling critical gaps in current smishing detection methodologies.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant no.(GPIP:1074-612-2024).
文摘Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R909)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The concept of Human Activity Recognition(HAR)is integral to applications based on Internet of Things(IoT)-enabled devices,particularly in healthcare,fitness tracking,and smart environments.The streams of data from wearable sensors are rich in information,yet their high dimensionality and variability pose a significant challenge to proper classification.To address this problem,this paper proposes hybrid architectures that integrate traditional machine learning models with a deep neural network(DNN)to deliver improved performance and enhanced capabilities for HAR tasks.Multi-sensor HAR data were used to systematically test several hybrid models,including:RF+DNN(Random Forest+Deep Neural Network),XGB+DNN(XGBoost+DNN),GB+DNN(Gradient Boosting+DNN),KNN+DNN(K-Nearest Neighbors+DNN),and DT+DNN(Decision Tree+DNN).The RF+DNN model was the most accurate,achieving a 97.03%score with excellent precision,recall,and F1-score.These findings demonstrate that hybrid machine learning and deep learning systems have a promising future in IoT-based HAR applications.The model provides a novel solution for developing smart and trustworthy monitoring systems that support real-time analytics,patient surveillance,and other IoT applications.
文摘Accurate,up to date,and quick information related to any disaster supports disaster management team/authorities to perform quick,easy,and cost-effective response to enhance rescue operations to alleviate the possible loss of lives,financial risks,and properties.Due to damaged infrastructure in disaster-affected areas,social media is the only way to share/exchange real time information.Therefore,‘X’(formerly Twitter)has become a major platform for disseminating real-time information during disaster events or emergencies,i.e.,floods and earthquake.Rapid identification of actionable content is critical for effective humanitarian response;however,the brief and noisy nature of tweets makes automated classification challenging.To tackle this problem,this study proposes a hybrid classification framework that integrates term frequency–inverse document frequency(TF-IDF)features with graph convolutional networks(GCNs)to enhance disaster-related tweet analysis.The proposed model performs three classification tasks:identifying disaster-related tweets(achieving 94.47%accuracy),categorizing disaster types(earthquake,flood,and non-disaster)with 91.78%accuracy,and detecting aid requests such as food,donations,and medical assistance(94.64%accuracy).By combining the statistical strengths of TF-IDF with the relational learning capabilities of GCNs,the model attains high accuracy while maintaining computational efficiency and interpretability.The results demonstrate the framework’s strong potential for real-time disaster response,offering valuable insights to support emergency management systems and humanitarian decision-making.
基金The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the project number(PSAU/2024/01/32082).
文摘In Human–Robot Interaction(HRI),generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging,especially in unstructured environments.In this study,we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation.The approach employs transformer models and adversarial training to map high-level intent to robotic motion.Information from multiple data sources,such as voice traits,hand and body keypoints,visual observations,and recorded paths,is integrated simultaneously.These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation.Based on this design,two different learning strategies are investigated.In the first step,grammar-constrained Linear Temporal Logic(LTL)expressions are created from multimodal human inputs.These expressions are subsequently decoded into robot trajectories.The second method generates trajectories directly from symbolic intent and linguistic data,bypassing an intermediate logical representation.Transformer encoders combine multiple types of information,and autoregressive transformer decoders generate motion sequences.Adding smoothness and speed limits during training increases the likelihood of physical feasibility.To improve the realism and stability of the generated trajectories during training,an adversarial discriminator is also included to guide them toward the distribution of actual robot motion.Tests on the NATSGLD dataset indicate that the complete system exhibits stable training behaviour and performance.In normalised coordinates,the logic-based pipeline has an Average Displacement Error(ADE)of 0.040 and a Final Displacement Error(FDE)of 0.036.The adversarial generator makes substantially more progress,reducing ADE to 0.021 and FDE to 0.018.Visual examination confirms that the generated trajectories closely align with observed motion patterns while preserving smooth temporal dynamics.
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.
基金funded by Taif University,Taif,Saudi Arabia,project number(TU-DSPP-2024-17)。
文摘Advanced technologies like Cyber-Physical Systems(CPS)and the Internet of Things(IoT)have supported modernizing and automating the transportation region through the introduction of Intelligent Transportation Systems(ITS).Integrating CPS-ITS and IoT provides real-time Vehicle-to-Infrastructure(V2I)communication,supporting better traffic management,safety,and efficiency.These technological innovations generate complex problems that need to be addressed,uniquely about data routing and Task Scheduling(TS)in ITS.Attempts to solve those problems were primarily based on traditional and experimental methods,and the solutions were not so successful due to the dynamic nature of ITS.This is where the scope of Machine learning(ML)and Swarm Intelligence(SI)has significantly impacted dealing with these challenges;in this line,this research paper presents a novel method for TS and data routing in the CPS-ITS.This paper proposes using a cutting-edge ML algorithm for data transmission from CPS-ITS.This ML has Gated Linear Unit-approximated Reinforcement Learning(GLRL).Greedy Iterative-Particle Swarm Optimization(GI-PSO)has been recommended to develop the Particle Swarm Optimization(PSO)for TS.The primary objective of this study is to enhance the security and effectiveness of ITS systems that utilize CPS-ITS.This study trained and validated the models using a network simulation dataset of 50 nodes from numerous ITS environments.The experiments demonstrate that the proposed GLRL reduces End-toEnd Delay(EED)by 12%,enhances data size use from 83.6%to 88.6%,and achieves higher bandwidth allocation,particularly in high-demand scenarios such as multimedia data streams where adherence improved to 98.15%.Furthermore,the GLRL reduced Network Congestion(NC)by 5.5%,demonstrating its efficiency in managing complex traffic conditions across several environments.The model passed simulation tests in three different environments:urban(UE),suburban(SE),and rural(RE).It met the high bandwidth requirements,made task scheduling more efficient,and increased network throughput(NT).This proved that it was robust and flexible enough for scalable ITS applications.These innovations provide robust,scalable solutions for real-time traffic management,ultimately improving safety,reducing NC,and increasing overall NT.This study can affect ITS by developing it to be more responsive,safe,and effective and by creating a perfect method to set up UE,SE,and RE.
文摘The autonomous navigation of an Unmanned Aerial Vehicle(UAV)relies heavily on the navigation sensors.The UAV’s level of autonomy depends upon the various navigation systems,such as state measurement,mapping,and obstacle avoidance.Selecting the correct components is a critical part of the design process.However,this can be a particularly difficult task,especially for novices as there are several technologies and components available on the market,each with their own individual advantages and disadvantages.For example,satellite-based navigation components should be avoided when designing indoor UAVs.Incorporating them in the design brings no added value to the final product and will simply lead to increased cost and power consumption.Another issue is the number of vendors on the market,each trying to sell their hardware solutions which often incorporate similar technologies.The aim of this paper is to serve as a guide,proposing various methods to support the selection of fit-for-purpose technologies and components whilst avoiding system layout conflicts.The paper presents a study of the various navigation technologies and supports engineers in the selection of specific hardware solutions based on given requirements.The selection methods are based on easy-to-follow flow charts.A comparison of the various hardware components specifications is also included as part of this work.
文摘Ever since its outbreak inWuhan,COVID-19 has cloaked the entireworld in a pall of despondency and uncertainty.The present study describes the exploratory analysis of all COVID cases in Saudi Arabia.Besides,the study has executed the forecastingmodel for predicting the possible number of COVID-19 cases in Saudi Arabia till a defined period.Towards this intent,the study analyzed different age groups of patients(child,adult,elderly)who were affected by COVID-19.The analysis was done city-wise and also included the number of recoveries recorded in different cities.Furthermore,the study also discusses the impact of COVID-19 on the economy.For conducting the stated analysis,the authors have created a list of factors that are known to cause the spread of COVID-19.As an effective countermeasure to contain the spread of Coronavirus in Saudi Arabia,this study also proposes to identify the most effective Computer Science technique that can be used by healthcare professionals.For this,the study employs the Fuzzy-Analytic Hierarchy Process integrated with the Technique for Order Performance by Similar to Ideal Solution(F.AHP.TOPSIS).After prioritizing the various Computer Science techniques,the ranking order that was obtained for the different techniques/tools to contain COVID-19 was:A4>A1>A2>A5>A3.Since the Blockchain technique obtained the highest priority,the study recommends that it must be used extensively as an efficacious and accurate means to combat COVID-19.
基金Funding for this study was received from the Ministry of Education andDeanship of Scientific Research at King Abdulaziz University, Kingdom of Saudi Arabia underthe Grant No. IFPHI-267-611-2020.
文摘Ever since its outbreak in the Wuhan city of China,COVID-19 pandemic has engulfed more than 211 countries in the world,leaving a trail of unprecedented fatalities.Even more debilitating than the infection itself,were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus.Such enforced alienation affected both the mental and social condition of people significantly.Social interactions and congregations are not only integral part of work life but also form the basis of human evolvement.However,COVID-19 brought all such communication to a grinding halt.Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets.The pandemic has shoved the entire planet into an unstable state.The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia.To achieve this objective,the study analyzes two perspectives:the early approach,and the late approach of COVID-19 and the consequent effects on different aspects of the society.We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society.Findings of this research study indicate that financial resources were the worst affected.Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people.Yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.
文摘The basic unit in life is cell.?It contains many protein molecules located at its different organelles. The growth and reproduction of a cell as well as most of its other biological functions are performed via these proteins. But proteins in different organelles or subcellular locations have different functions. Facing?the avalanche of protein sequences generated in the postgenomic age, we are challenged to develop high throughput tools for identifying the subcellular localization of proteins based on their sequence information alone. Although considerable efforts have been made in this regard, the problem is far apart from being solved yet. Most existing methods can be used to deal with single-location proteins only. Actually, proteins with multi-locations may have some special biological functions that are particularly important for drug targets. Using the ML-GKR (Multi-Label Gaussian Kernel Regression) method,?we developed a new predictor called “pLoc-mGpos” by in-depth extracting the key information from GO (Gene Ontology) into the Chou’s general PseAAC (Pseudo Amino Acid Composition)?for predicting the subcellular localization of Gram-positive bacterial proteins with both single and multiple location sites. Rigorous cross-validation on a same stringent benchmark dataset indicated that the proposed pLoc-mGpos predictor is remarkably superior to “iLoc-Gpos”, the state-of-the-art predictor for the same purpose.?To maximize the convenience of most experimental scientists, a user-friendly web-server for the new powerful predictor has been established at http://www.jci-bioinfo.cn/pLoc-mGpos/, by which users can easily get their desired results without the need to go through the complicated mathematics involved.
文摘The massive technological advancements around the world have created significant challenging competition among companies where each of the companies tries to attract the customers using different techniques. One of the recent tech- niques is Augmented Reality (AR). The AR is a new technology which is capable of presenting possibilities that are difficult for other technologies to offer and meet. Nowadays, numerous augmented reality applications have been used in the industry of different kinds and disseminated all over the world. AR will really alter the way individuals view the world. The AR is yet in its initial phases of research and development at different colleges and high-tech institutes. Throughout the last years, AR apps became transportable and generally available on various devices. Besides, AR be- gins to occupy its place in our audio-visual media and to be used in various fields in our life in tangible and exciting ways such as news, sports and is used in many domains in our life such as electronic commerce, promotion, design, and business. In addition, AR is used to facilitate the learning whereas it enables students to access location-specific infor- mation provided through various sources. Such growth and spread of AR applications pushes organizations to compete one another, every one of them exerts its best to gain the customers. This paper provides a comprehensive study of AR including its history, architecture, applications, current challenges and future trends.
