This review examines human vulnerabilities in cybersecurity within Microfinance Institutions, analyzing their impact on organizational resilience. Focusing on social engineering, inadequate security training, and weak...This review examines human vulnerabilities in cybersecurity within Microfinance Institutions, analyzing their impact on organizational resilience. Focusing on social engineering, inadequate security training, and weak internal protocols, the study identifies key vulnerabilities exacerbating cyber threats to MFIs. A literature review using databases like IEEE Xplore and Google Scholar focused on studies from 2019 to 2023 addressing human factors in cybersecurity specific to MFIs. Analysis of 57 studies reveals that phishing and insider threats are predominant, with a 20% annual increase in phishing attempts. Employee susceptibility to these attacks is heightened by insufficient training, with entry-level employees showing the highest vulnerability rates. Further, only 35% of MFIs offer regular cybersecurity training, significantly impacting incident reduction. This paper recommends enhanced training frequency, robust internal controls, and a cybersecurity-aware culture to mitigate human-induced cyber risks in MFIs.展开更多
Malaysia,as one of the highest producers of palm oil globally and one of the largest exporters,has a huge potential to use palmoil waste to generate electricity since an abundance of waste is produced during the palmo...Malaysia,as one of the highest producers of palm oil globally and one of the largest exporters,has a huge potential to use palmoil waste to generate electricity since an abundance of waste is produced during the palmoil extraction process.In this paper,we have first examined and compared the use of palmoil waste as biomass for electricity generation in different countries with reference to Malaysia.Some areas with default accessibility in rural areas,like those in Sabah and Sarawak,require a cheap and reliable source of electricity.Palm oil waste possesses the potential to be the source.Therefore,this research examines the cost-effective comparison between electricity generated frompalm oil waste and standalone diesel electric generation in Marudi,Sarawak,Malaysia.This research aims to investigate the potential electricity generation using palm oil waste and the feasibility of implementing the technology in rural areas.To implement and analyze the feasibility,a case study has been carried out in a rural area in Sarawak,Malaysia.The finding shows the electricity cost calculation of small towns like Long Lama,Long Miri,and Long Atip,with ten nearby schools,and suggests that using EFB from palm oil waste is cheaper and reduces greenhouse gas emissions.The study also points out the need to conduct further research on power systems,such as energy storage andmicrogrids,to better understand the future of power systems.By collecting data through questionnaires and surveys,an analysis has been carried out to determine the approximate cost and quantity of palm oil waste to generate cheaper renewable energy.We concluded that electricity generation from palm oil waste is cost-effective and beneficial.The infrastructure can be a microgrid connected to the main grid.展开更多
Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities.This study introduces a neural network-ba...Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities.This study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing conditions.Designed to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real time.The training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent performance.The simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.展开更多
Edge computing(EC)combined with the Internet of Things(IoT)provides a scalable and efficient solution for smart homes.Therapid proliferation of IoT devices poses real-time data processing and security challenges.EC ha...Edge computing(EC)combined with the Internet of Things(IoT)provides a scalable and efficient solution for smart homes.Therapid proliferation of IoT devices poses real-time data processing and security challenges.EC has become a transformative paradigm for addressing these challenges,particularly in intrusion detection and anomaly mitigation.The widespread connectivity of IoT edge networks has exposed them to various security threats,necessitating robust strategies to detect malicious activities.This research presents a privacy-preserving federated anomaly detection framework combined with Bayesian game theory(BGT)and double deep Q-learning(DDQL).The proposed framework integrates BGT to model attacker and defender interactions for dynamic threat level adaptation and resource availability.It also models a strategic layout between attackers and defenders that takes into account uncertainty.DDQL is incorporated to optimize decision-making and aids in learning optimal defense policies at the edge,thereby ensuring policy and decision optimization.Federated learning(FL)enables decentralized and unshared anomaly detection for sensitive data between devices.Data collection has been performed from various sensors in a real-time EC-IoT network to identify irregularities that occurred due to different attacks.The results reveal that the proposed model achieves high detection accuracy of up to 98%while maintaining low resource consumption.This study demonstrates the synergy between game theory and FL to strengthen anomaly detection in EC-IoT networks.展开更多
Pervasive IoT applications enable us to perceive,analyze,control,and optimize the traditional physical systems.Recently,security breaches in many IoT applications have indicated that IoT applications may put the physi...Pervasive IoT applications enable us to perceive,analyze,control,and optimize the traditional physical systems.Recently,security breaches in many IoT applications have indicated that IoT applications may put the physical systems at risk.Severe resource constraints and insufficient security design are two major causes of many security problems in IoT applications.As an extension of the cloud,the emerging edge computing with rich resources provides us a new venue to design and deploy novel security solutions for IoT applications.Although there are some research efforts in this area,edge-based security designs for IoT applications are still in its infancy.This paper aims to present a comprehensive survey of existing IoT security solutions at the edge layer as well as to inspire more edge-based IoT security designs.We first present an edge-centric IoT architecture.Then,we extensively review the edge-based IoT security research efforts in the context of security architecture designs,firewalls,intrusion detection systems,authentication and authorization protocols,and privacy-preserving mechanisms.Finally,we propose our insight into future research directions and open research issues.展开更多
Mobile Cloud Computing （MCC） is emerging as one of the most important branches of cloud computing. In this paper, MCC is defined as cloud computing extended by mobility, and a new ad-hoc infrastructure based on mobi...Mobile Cloud Computing （MCC） is emerging as one of the most important branches of cloud computing. In this paper, MCC is defined as cloud computing extended by mobility, and a new ad-hoc infrastructure based on mobile devices. It provides mobile users with data storage and processing services on a cloud computing platform. Because mobile cloud computing is still in its infancy we aim to clarify confusion that has arisen from different views. Existing works are reviewed, and an overview of recent advances in mobile cloud computing is provided. We investigate representative infrastructures of mobile cloud computing and analyze key components. Moreover, emerging MCC models and services are discussed, and challenging issues are identified that will need to be addressed in future work.展开更多
Big Data applications are pervading more and more aspects of our life, encompassing commercial and scientific uses at increasing rates as we move towards exascale analytics. Examples of Big Data applications include s...Big Data applications are pervading more and more aspects of our life, encompassing commercial and scientific uses at increasing rates as we move towards exascale analytics. Examples of Big Data applications include storing and accessing user data in commercial clouds, mining of social data, and analysis of large-scale simulations and experiments such as the Large Hadron Collider. An increasing number of such data—intensive applications and services are relying on clouds in order to process and manage the enormous amounts of data required for continuous operation. It can be difficult to decide which of the many options for cloud processing is suitable for a given application;the aim of this paper is therefore to provide an interested user with an overview of the most important concepts of cloud computing as it relates to processing of Big Data.展开更多
This paper looks at student's view of the usefulness of a problem solving and programming module in the first year of a 3-year undergraduate program.The School of Science and Technology,University of Northampton,U...This paper looks at student's view of the usefulness of a problem solving and programming module in the first year of a 3-year undergraduate program.The School of Science and Technology,University of Northampton,UK has been investigating,over the last seven years the teaching of problem solving.Including looking at whether a more visual approach has any benefits(the visual programming includes both 2-d and graphical user interfaces).Whilst the authors have discussed the subject problem solving and programming in the past [1] this paper considers the students perspective from research collected/collated by a student researcher under a new initiative within the University.All students interviewed either had completed the module within the two years of the survey or were completing the problem-solving module in their first year.展开更多
The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic tools.In this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplo...The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic tools.In this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature extraction.The methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of features.Additionally,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)systems.To evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are utilized.In 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 vs.pneumonia classification and 97%in distinguishing normal cases.Overall,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.展开更多
Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad h...Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc Networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes altogether.Recognizing the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against BHA.An additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network behaviors.Rigorous testing of our solution has revealed its remarkable ability to enhance communication in VANETs.Specifically,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and security.These results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.展开更多
This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc...This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and Friedman.It articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility patterns.The study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network scenarios.The findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network requirements.This paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.展开更多
Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enabl...Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive tasks.One fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource utilization.Existing approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource availability.These approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider lock-in.Therefore,we present Edge Colla,which is based on the integration of edge resources running across multi-edge deployments.Edge Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize them.Extensive experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.展开更多
Virtual Reality(VR)is a key industry for the development of the digital economy in the future.Mobile VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream ...Virtual Reality(VR)is a key industry for the development of the digital economy in the future.Mobile VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of VR.In this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive transmission.To support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint prediction.The tile-based content is proactively cached in the MEC nodes based on the popularity of the VR content.Second,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client buffer.Then,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are presented.Finally,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.展开更多
At the panel session of the 3rd Global Forum on the Development of Computer Science,attendees had an opportunity to deliberate recent issues affecting computer science departments as a result of the recent growth in t...At the panel session of the 3rd Global Forum on the Development of Computer Science,attendees had an opportunity to deliberate recent issues affecting computer science departments as a result of the recent growth in the field.6 heads of university computer science departments participated in the discussions,including the moderator,Professor Andrew Yao.The first issue was how universities are managing the growing number of applicants in addition to swelling class sizes.Several approaches were suggested,including increasing faculty hiring,implementing scalable teaching tools,and working closer with other departments through degree programs that integrate computer science with other fields.The second issue was about the position and role of computer science within broader science.Participants generally agreed that all fields are increasingly relying on computer science techniques,and that effectively disseminating these techniques to others is a key to unlocking broader scientific progress.展开更多
This paper presents the Advanced Observer Model (AOM), a groundbreaking conceptual framework designed to clarify the complex and often enigmatic nature of quantum mechanics. The AOM serves as a metaphorical lens, brin...This paper presents the Advanced Observer Model (AOM), a groundbreaking conceptual framework designed to clarify the complex and often enigmatic nature of quantum mechanics. The AOM serves as a metaphorical lens, bringing the elusive quantum realm into sharper focus by transforming its inherent uncertainty into a coherent, structured ‘Frame Stream’ that aids in the understanding of quantum phenomena. While the AOM offers conceptual simplicity and clarity, it recognizes the necessity of a rigorous theoretical foundation to address the fundamental uncertainties that lie at the core of quantum mechanics. This paper seeks to illuminate those theoretical ambiguities, bridging the gap between the abstract insights of the AOM and the intricate mathematical foundations of quantum theory. By integrating the conceptual clarity of the AOM with the theoretical intricacies of quantum mechanics, this work aspires to deepen our understanding of this fascinating and elusive field.展开更多
This paper introduces the Advanced Observer Model (AOM), a novel framework that integrates classical mechanics, quantum mechanics, and relativity through the observer’s role in constructing reality. Central to the AO...This paper introduces the Advanced Observer Model (AOM), a novel framework that integrates classical mechanics, quantum mechanics, and relativity through the observer’s role in constructing reality. Central to the AOM is the Static Configuration/Dynamic Configuration (SC/DC) conjugate, which examines physical systems through the interaction between static spatial configurations and dynamic quantum states. The model introduces a Constant Frame Rate (CFR) to quantize time perception, providing a discrete model for time evolution in quantum systems. By modifying the Schrödinger equation with CFR, the AOM bridges quantum and classical physics, offering a unified interpretation where classical determinism and quantum uncertainty coexist. A key feature of the AOM is its energy scaling model, where energy grows exponentially with spatial dimensionality, following the relationshipE∝(π)n. This dimensional scaling connects the discrete time perception of the observer with both quantum and classical energy distributions, providing insights into the nature of higher-dimensional spaces. Additionally, the AOM posits that spacetime curvature arises from quantum interactions, shaped by the observer’s discrete time perception. The model emphasizes the observer’s consciousness as a co-creator of reality, offering new approaches to understanding the quantum-classical transition. While speculative, the AOM opens new avenues for addressing foundational questions in quantum mechanics, relativity, dimensionality, and the nature of reality.展开更多
The research focuses on improving predictive accuracy in the financial sector through the exploration of machine learning algorithms for stock price prediction. The research follows an organized process combining Agil...The research focuses on improving predictive accuracy in the financial sector through the exploration of machine learning algorithms for stock price prediction. The research follows an organized process combining Agile Scrum and the Obtain, Scrub, Explore, Model, and iNterpret (OSEMN) methodology. Six machine learning models, namely Linear Forecast, Naive Forecast, Simple Moving Average with weekly window (SMA 5), Simple Moving Average with monthly window (SMA 20), Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM), are compared and evaluated through Mean Absolute Error (MAE), with the LSTM model performing the best, showcasing its potential for practical financial applications. A Django web application “Predict It” is developed to implement the LSTM model. Ethical concerns related to predictive modeling in finance are addressed. Data quality, algorithm choice, feature engineering, and preprocessing techniques are emphasized for better model performance. The research acknowledges limitations and suggests future research directions, aiming to equip investors and financial professionals with reliable predictive models for dynamic markets.展开更多
Purpose: This study examines the transformative impact of artificial intelligence (AI) in healthcare, focusing on its applications in medical diagnosis, drug discovery, surgery, and disease management while addressing...Purpose: This study examines the transformative impact of artificial intelligence (AI) in healthcare, focusing on its applications in medical diagnosis, drug discovery, surgery, and disease management while addressing ethical, technological, and social concerns. Method: A comprehensive literature review synthesizes research on AI applications, including AI-assisted diagnosis, drug discovery, robot-assisted surgery, stroke management, and artificial neurons. Findings: AI has enabled significant breakthroughs in healthcare, enhancing outcomes in diagnostics, personalized treatments, and surgical procedures. Despite its promise, challenges such as privacy, safety, and equitable access remain critical concerns. Research Limitations: The study relies on existing literature and lacks empirical validation of AI models, with its scope limited by the rapid evolution of AI technologies. Social Implications: The integration of AI raises concerns about privacy, patient rights, and equitable access, particularly in underserved regions, potentially exacerbating healthcare disparities. Practical Implications: The study urges healthcare practitioners to adopt AI tools for improved diagnostics and treatments while advocating for regulatory frameworks to ensure ethical and safe AI integration. Originality: This study offers a comprehensive review of AI’s transformative role in healthcare, emphasizing ethical considerations and providing actionable insights for researchers and practitioners.展开更多
Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a p...Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.展开更多
Context and Objectives: Stomach cancer ranks fifth in incidence and fourth in mortality worldwide. In Senegal, there were 597 new cases in 2020, with a mortality rate of almost 70%. The aim of this study was to develo...Context and Objectives: Stomach cancer ranks fifth in incidence and fourth in mortality worldwide. In Senegal, there were 597 new cases in 2020, with a mortality rate of almost 70%. The aim of this study was to develop a machine-learning model for the prognosis of death from stomach cancer 5 years after treatment. Methods: Our study sample consisted of 262 patients treated for gastric cancer at Aristide le Dantec Hospital and followed postoperatively between 2007 and 2020. We developed a multilayer perceptron with optimal hyperparameters and compared its performance with standard classification algorithms. We also augmented our data with a set of synthetic data generators to evaluate the behaviour of the model when faced with a larger amount of data. Results: Our model obtained an accuracy of 97.5%, outperforming the SVM (93%), RF (93.8%) and KNN (92.7%) models. An improvement of 1.5% in accuracy was achieved with synthetic data. Our study showed that the most pejorative factors in the evolution of the cancer were the appearance of hepatic metastases or adenopathy, smoking, and the infiltrative and stenosing aspects of the tumour on endoscopy. Conclusion: Our model predicted the occurrence of death from gastric cancer with very high accuracy, outperforming standard classification algorithms. The increase in training data produced an improvement in accuracy. Our study will help doctors to personalize the management of gastric cancer patients.展开更多
文摘This review examines human vulnerabilities in cybersecurity within Microfinance Institutions, analyzing their impact on organizational resilience. Focusing on social engineering, inadequate security training, and weak internal protocols, the study identifies key vulnerabilities exacerbating cyber threats to MFIs. A literature review using databases like IEEE Xplore and Google Scholar focused on studies from 2019 to 2023 addressing human factors in cybersecurity specific to MFIs. Analysis of 57 studies reveals that phishing and insider threats are predominant, with a 20% annual increase in phishing attempts. Employee susceptibility to these attacks is heightened by insufficient training, with entry-level employees showing the highest vulnerability rates. Further, only 35% of MFIs offer regular cybersecurity training, significantly impacting incident reduction. This paper recommends enhanced training frequency, robust internal controls, and a cybersecurity-aware culture to mitigate human-induced cyber risks in MFIs.
文摘Malaysia,as one of the highest producers of palm oil globally and one of the largest exporters,has a huge potential to use palmoil waste to generate electricity since an abundance of waste is produced during the palmoil extraction process.In this paper,we have first examined and compared the use of palmoil waste as biomass for electricity generation in different countries with reference to Malaysia.Some areas with default accessibility in rural areas,like those in Sabah and Sarawak,require a cheap and reliable source of electricity.Palm oil waste possesses the potential to be the source.Therefore,this research examines the cost-effective comparison between electricity generated frompalm oil waste and standalone diesel electric generation in Marudi,Sarawak,Malaysia.This research aims to investigate the potential electricity generation using palm oil waste and the feasibility of implementing the technology in rural areas.To implement and analyze the feasibility,a case study has been carried out in a rural area in Sarawak,Malaysia.The finding shows the electricity cost calculation of small towns like Long Lama,Long Miri,and Long Atip,with ten nearby schools,and suggests that using EFB from palm oil waste is cheaper and reduces greenhouse gas emissions.The study also points out the need to conduct further research on power systems,such as energy storage andmicrogrids,to better understand the future of power systems.By collecting data through questionnaires and surveys,an analysis has been carried out to determine the approximate cost and quantity of palm oil waste to generate cheaper renewable energy.We concluded that electricity generation from palm oil waste is cost-effective and beneficial.The infrastructure can be a microgrid connected to the main grid.
基金funding of the Deanship of Graduate Studies and Scientific Research,Jazan University,Saudi Arabia,through Project Number:ISP-2024.
文摘Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities.This study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing conditions.Designed to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real time.The training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent performance.The simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.
基金The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through the Large Group Project under grant number(RGP2/337/46)The research team thanks the Deanship of Graduate Studies and Scientific Research at Najran University for supporting the research project through the Nama’a program,with the project code NU/GP/SERC/13/352-4.
文摘Edge computing(EC)combined with the Internet of Things(IoT)provides a scalable and efficient solution for smart homes.Therapid proliferation of IoT devices poses real-time data processing and security challenges.EC has become a transformative paradigm for addressing these challenges,particularly in intrusion detection and anomaly mitigation.The widespread connectivity of IoT edge networks has exposed them to various security threats,necessitating robust strategies to detect malicious activities.This research presents a privacy-preserving federated anomaly detection framework combined with Bayesian game theory(BGT)and double deep Q-learning(DDQL).The proposed framework integrates BGT to model attacker and defender interactions for dynamic threat level adaptation and resource availability.It also models a strategic layout between attackers and defenders that takes into account uncertainty.DDQL is incorporated to optimize decision-making and aids in learning optimal defense policies at the edge,thereby ensuring policy and decision optimization.Federated learning(FL)enables decentralized and unshared anomaly detection for sensitive data between devices.Data collection has been performed from various sensors in a real-time EC-IoT network to identify irregularities that occurred due to different attacks.The results reveal that the proposed model achieves high detection accuracy of up to 98%while maintaining low resource consumption.This study demonstrates the synergy between game theory and FL to strengthen anomaly detection in EC-IoT networks.
基金This research has been supported by the National Science Foundation(under grant#1723596)the National Security Agency(under grant#H98230-17-1-0355).
文摘Pervasive IoT applications enable us to perceive,analyze,control,and optimize the traditional physical systems.Recently,security breaches in many IoT applications have indicated that IoT applications may put the physical systems at risk.Severe resource constraints and insufficient security design are two major causes of many security problems in IoT applications.As an extension of the cloud,the emerging edge computing with rich resources provides us a new venue to design and deploy novel security solutions for IoT applications.Although there are some research efforts in this area,edge-based security designs for IoT applications are still in its infancy.This paper aims to present a comprehensive survey of existing IoT security solutions at the edge layer as well as to inspire more edge-based IoT security designs.We first present an edge-centric IoT architecture.Then,we extensively review the edge-based IoT security research efforts in the context of security architecture designs,firewalls,intrusion detection systems,authentication and authorization protocols,and privacy-preserving mechanisms.Finally,we propose our insight into future research directions and open research issues.
基金supported by Hong Kong RGC under the GRF grant PolyU5106/10ENokia Research Lab (Beijing) under the grant H-ZG19+1 种基金supported by the National S&T Major Project of China under No.2009ZX03006-001Guangdong S&T Major Project under No.2009A080207002
文摘Mobile Cloud Computing （MCC） is emerging as one of the most important branches of cloud computing. In this paper, MCC is defined as cloud computing extended by mobility, and a new ad-hoc infrastructure based on mobile devices. It provides mobile users with data storage and processing services on a cloud computing platform. Because mobile cloud computing is still in its infancy we aim to clarify confusion that has arisen from different views. Existing works are reviewed, and an overview of recent advances in mobile cloud computing is provided. We investigate representative infrastructures of mobile cloud computing and analyze key components. Moreover, emerging MCC models and services are discussed, and challenging issues are identified that will need to be addressed in future work.
文摘Big Data applications are pervading more and more aspects of our life, encompassing commercial and scientific uses at increasing rates as we move towards exascale analytics. Examples of Big Data applications include storing and accessing user data in commercial clouds, mining of social data, and analysis of large-scale simulations and experiments such as the Large Hadron Collider. An increasing number of such data—intensive applications and services are relying on clouds in order to process and manage the enormous amounts of data required for continuous operation. It can be difficult to decide which of the many options for cloud processing is suitable for a given application;the aim of this paper is therefore to provide an interested user with an overview of the most important concepts of cloud computing as it relates to processing of Big Data.
文摘This paper looks at student's view of the usefulness of a problem solving and programming module in the first year of a 3-year undergraduate program.The School of Science and Technology,University of Northampton,UK has been investigating,over the last seven years the teaching of problem solving.Including looking at whether a more visual approach has any benefits(the visual programming includes both 2-d and graphical user interfaces).Whilst the authors have discussed the subject problem solving and programming in the past [1] this paper considers the students perspective from research collected/collated by a student researcher under a new initiative within the University.All students interviewed either had completed the module within the two years of the survey or were completing the problem-solving module in their first year.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic tools.In this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature extraction.The methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of features.Additionally,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)systems.To evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are utilized.In 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 vs.pneumonia classification and 97%in distinguishing normal cases.Overall,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.
文摘Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc Networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes altogether.Recognizing the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against BHA.An additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network behaviors.Rigorous testing of our solution has revealed its remarkable ability to enhance communication in VANETs.Specifically,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and security.These results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.
基金supported by Northern Border University,Arar,KSA,through the Project Number“NBU-FFR-2024-2248-02”.
文摘This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and Friedman.It articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility patterns.The study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network scenarios.The findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network requirements.This paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.
基金The financial support of the National Natural Science Foundation of China under grants 61901416 and 61571401(part of the Natural Science Foundation of Henan under grant 242300420269)the Young Elite Scientists Sponsorship Program of Henan under grant 2024HYTP026the Innovative Talent of Colleges and the University of Henan Province under grant 18HASTIT021。
文摘Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive tasks.One fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource utilization.Existing approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource availability.These approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider lock-in.Therefore,we present Edge Colla,which is based on the integration of edge resources running across multi-edge deployments.Edge Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize them.Extensive experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.
基金supported in part by the Chongqing Municipal Education Commission projects under Grant No.KJCX2020035,KJQN202200829Chongqing Science and Technology Commission projects under grant No.CSTB2022BSXM-JCX0117 and cstc2020jcyjmsxmX0339+1 种基金supported in part by National Natural Science Foundation of China under Grant No.(62171072,62172064,62003067,61901067)supported in part by Chongqing Technology and Business University projects under Grant no.(2156004,212017).
文摘Virtual Reality(VR)is a key industry for the development of the digital economy in the future.Mobile VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of VR.In this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive transmission.To support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint prediction.The tile-based content is proactively cached in the MEC nodes based on the popularity of the VR content.Second,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client buffer.Then,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are presented.Finally,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.
文摘At the panel session of the 3rd Global Forum on the Development of Computer Science,attendees had an opportunity to deliberate recent issues affecting computer science departments as a result of the recent growth in the field.6 heads of university computer science departments participated in the discussions,including the moderator,Professor Andrew Yao.The first issue was how universities are managing the growing number of applicants in addition to swelling class sizes.Several approaches were suggested,including increasing faculty hiring,implementing scalable teaching tools,and working closer with other departments through degree programs that integrate computer science with other fields.The second issue was about the position and role of computer science within broader science.Participants generally agreed that all fields are increasingly relying on computer science techniques,and that effectively disseminating these techniques to others is a key to unlocking broader scientific progress.
文摘This paper presents the Advanced Observer Model (AOM), a groundbreaking conceptual framework designed to clarify the complex and often enigmatic nature of quantum mechanics. The AOM serves as a metaphorical lens, bringing the elusive quantum realm into sharper focus by transforming its inherent uncertainty into a coherent, structured ‘Frame Stream’ that aids in the understanding of quantum phenomena. While the AOM offers conceptual simplicity and clarity, it recognizes the necessity of a rigorous theoretical foundation to address the fundamental uncertainties that lie at the core of quantum mechanics. This paper seeks to illuminate those theoretical ambiguities, bridging the gap between the abstract insights of the AOM and the intricate mathematical foundations of quantum theory. By integrating the conceptual clarity of the AOM with the theoretical intricacies of quantum mechanics, this work aspires to deepen our understanding of this fascinating and elusive field.
文摘This paper introduces the Advanced Observer Model (AOM), a novel framework that integrates classical mechanics, quantum mechanics, and relativity through the observer’s role in constructing reality. Central to the AOM is the Static Configuration/Dynamic Configuration (SC/DC) conjugate, which examines physical systems through the interaction between static spatial configurations and dynamic quantum states. The model introduces a Constant Frame Rate (CFR) to quantize time perception, providing a discrete model for time evolution in quantum systems. By modifying the Schrödinger equation with CFR, the AOM bridges quantum and classical physics, offering a unified interpretation where classical determinism and quantum uncertainty coexist. A key feature of the AOM is its energy scaling model, where energy grows exponentially with spatial dimensionality, following the relationshipE∝(π)n. This dimensional scaling connects the discrete time perception of the observer with both quantum and classical energy distributions, providing insights into the nature of higher-dimensional spaces. Additionally, the AOM posits that spacetime curvature arises from quantum interactions, shaped by the observer’s discrete time perception. The model emphasizes the observer’s consciousness as a co-creator of reality, offering new approaches to understanding the quantum-classical transition. While speculative, the AOM opens new avenues for addressing foundational questions in quantum mechanics, relativity, dimensionality, and the nature of reality.
文摘The research focuses on improving predictive accuracy in the financial sector through the exploration of machine learning algorithms for stock price prediction. The research follows an organized process combining Agile Scrum and the Obtain, Scrub, Explore, Model, and iNterpret (OSEMN) methodology. Six machine learning models, namely Linear Forecast, Naive Forecast, Simple Moving Average with weekly window (SMA 5), Simple Moving Average with monthly window (SMA 20), Autoregressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM), are compared and evaluated through Mean Absolute Error (MAE), with the LSTM model performing the best, showcasing its potential for practical financial applications. A Django web application “Predict It” is developed to implement the LSTM model. Ethical concerns related to predictive modeling in finance are addressed. Data quality, algorithm choice, feature engineering, and preprocessing techniques are emphasized for better model performance. The research acknowledges limitations and suggests future research directions, aiming to equip investors and financial professionals with reliable predictive models for dynamic markets.
文摘Purpose: This study examines the transformative impact of artificial intelligence (AI) in healthcare, focusing on its applications in medical diagnosis, drug discovery, surgery, and disease management while addressing ethical, technological, and social concerns. Method: A comprehensive literature review synthesizes research on AI applications, including AI-assisted diagnosis, drug discovery, robot-assisted surgery, stroke management, and artificial neurons. Findings: AI has enabled significant breakthroughs in healthcare, enhancing outcomes in diagnostics, personalized treatments, and surgical procedures. Despite its promise, challenges such as privacy, safety, and equitable access remain critical concerns. Research Limitations: The study relies on existing literature and lacks empirical validation of AI models, with its scope limited by the rapid evolution of AI technologies. Social Implications: The integration of AI raises concerns about privacy, patient rights, and equitable access, particularly in underserved regions, potentially exacerbating healthcare disparities. Practical Implications: The study urges healthcare practitioners to adopt AI tools for improved diagnostics and treatments while advocating for regulatory frameworks to ensure ethical and safe AI integration. Originality: This study offers a comprehensive review of AI’s transformative role in healthcare, emphasizing ethical considerations and providing actionable insights for researchers and practitioners.
文摘Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.
文摘Context and Objectives: Stomach cancer ranks fifth in incidence and fourth in mortality worldwide. In Senegal, there were 597 new cases in 2020, with a mortality rate of almost 70%. The aim of this study was to develop a machine-learning model for the prognosis of death from stomach cancer 5 years after treatment. Methods: Our study sample consisted of 262 patients treated for gastric cancer at Aristide le Dantec Hospital and followed postoperatively between 2007 and 2020. We developed a multilayer perceptron with optimal hyperparameters and compared its performance with standard classification algorithms. We also augmented our data with a set of synthetic data generators to evaluate the behaviour of the model when faced with a larger amount of data. Results: Our model obtained an accuracy of 97.5%, outperforming the SVM (93%), RF (93.8%) and KNN (92.7%) models. An improvement of 1.5% in accuracy was achieved with synthetic data. Our study showed that the most pejorative factors in the evolution of the cancer were the appearance of hepatic metastases or adenopathy, smoking, and the infiltrative and stenosing aspects of the tumour on endoscopy. Conclusion: Our model predicted the occurrence of death from gastric cancer with very high accuracy, outperforming standard classification algorithms. The increase in training data produced an improvement in accuracy. Our study will help doctors to personalize the management of gastric cancer patients.
