Objectives:The phytochemical investigation of traditional herbal medicines holds significant promise for modern drug discovery,particularly in cancer therapy.This study aimed to evaluate the cytotoxicity,apoptosis ind...Objectives:The phytochemical investigation of traditional herbal medicines holds significant promise for modern drug discovery,particularly in cancer therapy.This study aimed to evaluate the cytotoxicity,apoptosis induction,and immune-modulatory activities of extracts from three herbal medicines with historical use in traditional medicine—Acanthopanax sessiliflorus,Phragmites communis,and Pinus densiflora,as well as their combined extract(GMAS 01/COM),on human lung cancer cells(A549)and normal cell lines,including murine macrophages(RAW 264.7)and human keratinocytes(HaCaT).Methods:Plant extracts were prepared using aqueous extraction,sonication,and rotary evaporation.The total phenolic and flavonoid contents were quantified using the Folin-Ciocalteu and AlCl3 colorimetric methods,respectively.Antioxidant potential was evaluated via 2,2-Diphenyl-1-picrylhydrazyl(DPPH)scavenging and reducing power assays.Cytotoxicity was assessed using an MTT assay,while reactive oxygen species(ROS)generation was quantified using a 2′,7′-Dichlorodihydrofluorescein diacetate(DCFH-DA)assay.Anticancer properties,including colony formation inhibition and migration suppression,were examined using colony formation and wound healing assays.The expression of apoptotic and inflammatory mediators was analysed through qPCR.Results:GMAS 01 selectively induced apoptosis in A549 cells without cytotoxic effects on RAW264.7 and HaCaT cells.Mechanistically,it elevated intracellular ROS and activated the intrinsic mitochondrial apoptotic pathway,evidenced by p53 upregulation,increased Bax,and decreased Bcl-2 expression.GMAS 01 also significantly inhibited colony formation and migration in A549 cells.In RAW264.7 cells,it reduced nitric oxide(NO)production and downregulated iNOS,COX-2,IL-6,and IL-8,indicating strong immunomodulatory activity.Conclusion:GMAS 01 exhibits potent antioxidant,anti-inflammatory,and anticancer effects,likely mediated through ROS-induced mitochondrial apoptosis.However,mechanistic interpretations are limited by the absence of protein-level validation and pathway inhibition studies.Upcoming studies should aim to verify the underlying mechanisms and evaluate their potential for real-world application.展开更多
BACKGROUND Type 2 diabetes mellitus(T2DM)is increasing rapidly in Pakistan,especially among socioeconomically disadvantaged populations.While clinical care remains central,social determinants such as poverty,gender no...BACKGROUND Type 2 diabetes mellitus(T2DM)is increasing rapidly in Pakistan,especially among socioeconomically disadvantaged populations.While clinical care remains central,social determinants such as poverty,gender norms,and mistrust in healthcare critically shape disease outcomes.AIM To synthesize qualitative evidence on how these factors influence the experience and management of T2DM in Pakistan.METHODS Following PRISMA guidelines,a systematic review of qualitative studies published between 2000 and 2025 was conducted on February 25,2025 using PubMed,CINAHL,MEDLINE Plus,and PakMediNet.Eleven studies exploring socioeconomic influences on T2DM care and self-management in Pakistan were included.Thematic synthesis was used to identify key patterns.Quality was appraised using the Joanna Briggs Institute Checklist for Qualitative Research.RESULTS Three major themes were identified:(1)Economic insecurity.High cost of treatment,poor rural infrastructure,and food insecurity hinder access and adherence;(2)Sociocultural and gender norms.Restricted mobility of females,family control over health decisions,and fatalistic beliefs delay care;and(3)Knowledge gaps and mistrust.A lack of culturally appropriate education,reliance on traditional remedies,and distrust in public health systems reduce compliance.These intersecting barriers collectively impede effective diabetes management.CONCLUSION T2DM in Pakistan is driven by entrenched social and economic barriers.Addressing it requires culturally sensitive,equity-oriented strategies that go beyond biomedical models.Policy reforms should focus on affordability,rural outreach,and inclusive health education.Future research should engage marginalized voices through participatory methods.展开更多
Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when deal...Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.展开更多
The present study is focused on evaluation of deep marine pelagic sediments of Late Cretaceous Kawagarh Formation of Kala-Chitta Range in the context of microfacies analysis,paleoenvironmental interpretation,planktoni...The present study is focused on evaluation of deep marine pelagic sediments of Late Cretaceous Kawagarh Formation of Kala-Chitta Range in the context of microfacies analysis,paleoenvironmental interpretation,planktonic foraminiferal biostratigraphy,sequence stratigraphy and diversification of species.A total of thirty three rock samples were collected from the measured section.Three microfacies are interpreted,namely planktonic foraminifera wackestone,planktonic foraminifera mudstone and sandy mudstone indicating low energy depositional environment i.e.,outer ramp.The biostratigraphic studies show plentiful planktonic foraminifera species of Globotruncana,Heterohelix and Globotruncanita.However,no association of benthic or siliceous organisms was observed.On the basis of available species assemblage,a single local planktonic foraminifera biozone i.e.,Globotruncana-Heterohelix-Globotruncanita Assemblage Biozone is established.The biozone information is combined with published literature and Lower Santonian to Middle Maastrichtian age has been assigned to the Kawagarh Formation.The trend of species occurrences evinces that species number decreases over time with pulsated rise in the Kawagarh Formation.The Kawagarh Formation carbonates show an overall Transgressive Systems Tract(TST).The Kawagarh Formation of Pakistan evinces analogous characteristics to that of the Late Cretaceous Gurpi Formation of Iran based on the geologic age,outcrop lithology,microfacies,planktonic foraminiferal assemblages,depositional setting and sequence stratigraphy.展开更多
We investigate the light deflection in the weak field approximation from the accelerating charged AdS black hole.For this purpose,we apply the Gauss–Bonnet theorem to calculate the light deflection in the weak field ...We investigate the light deflection in the weak field approximation from the accelerating charged AdS black hole.For this purpose,we apply the Gauss–Bonnet theorem to calculate the light deflection in the weak field area and use the Gibbons–Werner approach to analyze the optical geometry of the accelerating charged AdS black hole in the non-magnetic plasma absence/presence of a non-magnetic medium.We also represent the graphical behavior of the light deflection angle w.r.t.the impact parameter.We also compute the light deflection angle using Keeton and Petters approximations under the impact of accelerating charged AdS black hole geometry.Furthermore,by using the ray-tracing approach,we determine the shadow in the nonmagnetic plasma presence and also demonstrate that graphical shadow has an impact on the gauge potential,non-magnetic plasma frequencies and charge.展开更多
Electronic nose(eNose) is a modern bioelectronic sensor for monitoring biological processes that convert CO_(2) into valueadded products, such as products formed during photosynthesis and microbial fermentation. eNose...Electronic nose(eNose) is a modern bioelectronic sensor for monitoring biological processes that convert CO_(2) into valueadded products, such as products formed during photosynthesis and microbial fermentation. eNose technology uses an array of sensors to detect and quantify gases, including CO_(2), in the air. This study briefly introduces the concept of eNose technology and potential applications thereof in monitoring CO_(2) conversion processes. It also provides background information on biological CO_(2) conversion processes. Furthermore, the working principles of eNose technology vis-à-vis gas detection are discussed along with its advantages and limitations versus traditional monitoring methods. This study also provides case studies that have used this technology for monitoring biological CO_(2) conversion processes. eNose-predicted measurements were observed to be completely aligned with biological parameters for R~2 values of 0.864, 0.808, 0.802, and 0.948. We test eNose technology in a variety of biological settings, such as algae farms or bioreactors, to determine its effectiveness in monitoring CO_(2) conversion processes. We also explore the potential benefits of employing this technology vis-à-vis monitoring biological CO_(2) conversion processes, such as increased reaction efficiency and reduced costs versus traditional monitoring methods. Moreover, future directions and challenges of using this technology in CO_(2) capture and conversion have been discussed. Overall, we believe this study would contribute to developing new and innovative methods for monitoring biological CO_(2) conversion processes and mitigating climate change.展开更多
Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated.It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction.Early det...Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated.It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction.Early detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this condition.However,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per patient.To overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for training.Developing this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in Tehran.To address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority class.Additionally,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training phase.The effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance metrics.Overall,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.展开更多
Polychloroprene (PC) based contact adhesives are widely used in various applications;however, there is a possibility to improve the properties of PC adhesive. Modifications of polymers can enhance the properties of th...Polychloroprene (PC) based contact adhesives are widely used in various applications;however, there is a possibility to improve the properties of PC adhesive. Modifications of polymers can enhance the properties of the material, e.g. increase in thermal stability, compatibility, rigidity, physical response, flexibility and improve the polymer process ability. In the current study, improved formulation of solvent-based adhesive was developed, and the properties were further enhanced by the addition of nano-reinforcement of multiwall carbon nanotubes (MWCNTs). The addition of nano-reinforcement was optimized to obtain improvement in the bond strength and also to enhance its resistance at a high temperature (~100<span style="white-space:nowrap;">°</span>C). This paper discusses the uniform dispersion of MWCNTs during the synthesis of polychloroprene solvent-based adhesive, thereby improving its structural properties. Incorporation of MWCNTs-solvent-based adhesives resulted in a 20% - 35% improvement in 180<span style="white-space:nowrap;">°</span> peel strength determined on flexible substrates such as canvas, leather. The reinforced based adhesive also exhibited improved thermal stability and weather resistance compared with unreinforced adhesive. The MWCNTs- solvent-based contact adhesives is a potential candidate in an industrially relevant branch of adhesives commonly used in structural applications, e.g., footwear, plastic, leather, automobile, construction industries, etc.展开更多
The dependence of the magnetic properties on the particle size of recycled HDDR Nd-Fe-B powders was investigated,with the aim to assess the reprocessing potential of the end-of-life scrap magnets via spark plasma sint...The dependence of the magnetic properties on the particle size of recycled HDDR Nd-Fe-B powders was investigated,with the aim to assess the reprocessing potential of the end-of-life scrap magnets via spark plasma sintering(SPS).The as received recycled HDDR powder has coercivity(Hci)=830 kA/m and particles in the range from 30 to 700 μm(average 220 μm).After burr milling,the average particle size is reduced to 120 μm and subsequently the Hci of fine(milled) powder was 595 kA/m.Spark plasma sintering was exploited to consolidate the nanograined HDDR powders and limit the abnormal grain coarsening.The optimal SPS-ing of coarse HDDR powder at 750℃for 1 min produces fully dense magnets with Hci=950±100 kA/m which further increases to 1200 kA/m via thermal treatment at 750℃for 15 min.The burr milled fine HDDR powder under similar SPS conditions and after thermal treatment results in Hci=940 kA/m.The fine powder is further sieved down from 630 to less than 50 μm mesh size,to evaluate the possible reduction in Hci in relation to the particle size.The gain in oxygen content doubles for <50 μm sized particles as compared with coarser fractions(>200 μm).The XRD analysis for fractionated powder indicates an increase in Nd2O3 phase peaks in the finer(<100 μm)fractions.Similarly,the Hci reduces from 820 kA/m in the coarse particles(>200 μm) to 460 kA/m in the fine sized particles(<100μm).SPS was done on each HDDR powder fraction under the optimal conditions to measure the variation in Hci and density.The Hci of SPS-ed coarse fraction(>200 μm) is higher than 930 kA/m and it falls abruptly to just 70 kA/m for the fine sized particles(<100 μm).The thermal treatment further improves the Hci to>1000 kA/m only up to 100 μm sized fractions with>90% sintered density.The full densification(>99%) is observed only in the coarse fractions.The loss of coercivity and lack of sinterability in the fine sized particles(<100 μm) are attributed to a very high oxygen content.This implies that during recycling,if good magnetic properties are to be maintained or even increase the HDDR powder particles can be sized down only up to≥100 μm.展开更多
This paper presents a compact Multiple Input Multiple Output(MIMO)antenna with WLAN band notch for Ultra-Wideband(UWB)applications.The antenna is designed on 0.8mmthick low-cost FR-4 substrate having a compact size of...This paper presents a compact Multiple Input Multiple Output(MIMO)antenna with WLAN band notch for Ultra-Wideband(UWB)applications.The antenna is designed on 0.8mmthick low-cost FR-4 substrate having a compact size of 22mm×30 mm.The proposed antenna comprises of two monopole patches on the top layer of substrate while having a shared ground on its bottom layer.The mutual coupling between adjacent patches has been reduced by using a novel stub with shared ground structure.The stub consists of complementary rectangular slots that disturb the surface current direction and thus result in reducing mutual coupling between two ports.A slot is etched in the radiating patch for WLAN band notch.The slot is used to suppress frequencies ranging from 5.1 to 5.9 GHz.The results show that the proposed antenna has a very good impedance bandwidth of|S11|<−10 dB within the frequency band from 3.1–14 GHz.A low mutual coupling of less than−23 dB is achieved within the entire UWB band.Furthermore,the antenna has a peak gain of 5.8 dB,low ECC<0.002 and high Diversity Gain(DG>9.98).展开更多
Text Summarization is an essential area in text mining,which has procedures for text extraction.In natural language processing,text summarization maps the documents to a representative set of descriptive words.Therefo...Text Summarization is an essential area in text mining,which has procedures for text extraction.In natural language processing,text summarization maps the documents to a representative set of descriptive words.Therefore,the objective of text extraction is to attain reduced expressive contents from the text documents.Text summarization has two main areas such as abstractive,and extractive summarization.Extractive text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding principles.All such text extractions have limitations in providing the basic theme of the underlying documents.In this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word embedding.The study compared these forms of the text summarizations with the actual text,by calculating cosine similarities.Furthermore,TF-IDF based PageRank keywords are extracted from the other two extractive summarizations.An intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is performed.This technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each document.This form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized text.It also solves the issue of deciding the number of representative keywords for a specific text document.To evaluate the technique,the study used a sample of more than eighteen hundred text documents.The abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text summarization.The proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.展开更多
This review efficiently covers the research progress in the area of polymer bio composites in perspective of the modern-day renewable materials.In the last decade,attraction towards the bio-composite based systems has...This review efficiently covers the research progress in the area of polymer bio composites in perspective of the modern-day renewable materials.In the last decade,attraction towards the bio-composite based systems has been the topic of interest due to their potential as a substitute of conventional materials produced in important manufacturing industries.Recently,preparation of biocompatible and biodegradable polymer composites is an important achievement as an alternative of petrochemical based renewable products.Successful production of eco-friendly bio-composite materials have been achieved with natural fibers viz jute,bamboo,hair,flex,wool,silk and many others instead of synthesized fibers like carbon,glass dispersed in synthesized resins viz poly vinyl alcohol,epoxy and etc.Biomaterials based on natural fibers dispersed in natural matrix like natural rubber or polyester have also been obtained with endless applications for the mankind.The utilization of such materials for the good well of mankind is attributed to their ease of disposal and being renewable.The last but not the least,the extraordinary mechanical properties of bio-composites make them superior to many other conventional materials.This review paper addresses the recent trends,mechanical and chemical properties,synthesis,and application of bio-composites in the recent years.展开更多
Agriculture is undoubtedly a leading field for livelihoods in China.As the population increases,it is necessary to increase agricultural productivity.By capturing the support and the increment in production on farms,t...Agriculture is undoubtedly a leading field for livelihoods in China.As the population increases,it is necessary to increase agricultural productivity.By capturing the support and the increment in production on farms,the need for freshwater used for irrigation increases too.Presently,agriculture accounts for 80% of overall water uptake in China.Unexpected overflow of water carelessly leads to waste of water.Therefore we created a programmed plant irrigation system with Arduino that mechanically supplies water to the plants and keeps it updated by transferring the message to user.Plant irrigation system employs the soil moisture sensor which controls a degree of moisture in the soil.If the humidity degree is lower,Arduino activates a pump of water to supply water to the system.The pump of water stops by design when the organism detects sufficient moisture in the ground.Each time the system is switched off or on,an electronic messaging is conveyed to the end-user through the IoT unit,informing the position of the soil moisture and the pump of water.A spray motor and the pump of water are grounded on the crane concept.Widely,this system is applicable for in small fields,gardens farms,etc.This design is entirely programmed and needed no human involvement.Furthermore,transmission of the sensor readings send through a Thing speak frequency to produce graphic elements for better inquiry.This study gathers the ideas of IoT(Internet of Things)with some engineering tools like machinery,artificial intelligence and use of sensors in an efficient way to respond current needs and extraction of resources by availing scientific methods and procedures that work on inputs.Moreover,this study further defines the engineering works that have been part of this field,but it requires more efficiency and reduction of energy as well as costs by adding more contribution of IoT in the field of agriculture engineering.展开更多
The Late Permian succession of the Upper Indus Basin in northeastern Pakistan is represented by the carbonatedominated Zaluch Group, which consists of the Amb, Wargal and Chhidru formations, which accumulated on the s...The Late Permian succession of the Upper Indus Basin in northeastern Pakistan is represented by the carbonatedominated Zaluch Group, which consists of the Amb, Wargal and Chhidru formations, which accumulated on the southwestern shelf of the Paleo-Tethys Ocean, north of the hydrocarbon-producing Permian strata of the Arabian Peninsula. The reservoir properties of the mixed clastic-carbonate Chhidru Formation(CFm) are evaluated based on petrography, using scanning electron microscopy(SEM), energy dispersive x-ray spectroscopy(EDX) and x-ray diffraction(XRD) techniques. The diagenetic features are recognized, ranging from marine(isopachous fibrous calcite, micrite), through meteoric(blocky calcite-I, neomorphism and dissolution) to burial(poikilotopic cement, blocky calcite-II-III, fractures, fracture-filling, and stylolites). Major porosity types include fracture and moldic, while inter-and intra-particle porosities also exist. Observed visual porosity ranges from 1.5%–7.14% with an average of 5.15%. The sandstone facies(CMF-4) has the highest average porosity of 10.7%, whereas the siliciclastic grainstone microfacies(CMF-3) shows an average porosity of 5.3%. The siliciclastic mudstone microfacies(CMF-1) and siliciclastic wacke-packestone microfacies(CMF-2) show the lowest porosities of 4.8% and 5.0%, respectively. Diagenetic processes like cementation, neomorphism, stylolitization and compaction have reduced the primary porosities;however, processes of dissolution and fracturing have produced secondary porosity. On average, the CFm in the Nammal Gorge, Salt Range shows promise and at Gula Khel Gorge, Trans-Indus, the lowest porosity.展开更多
Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and e...Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss.State-of-the-art technologies have the potential for long-term benefits in post-surgery living.In this work,an Internet of Things(IoT)framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight.The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients.It also attempts to automate the data analysis and represent the facts about a patient.The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system.The proposed IoT framework also benefits from machine learning based activity classification systems,with relatively high accuracy,which allow the communicated data to be translated into meaningful information.展开更多
In the current era of the internet,people use online media for conversation,discussion,chatting,and other similar purposes.Analysis of such material where more than one person is involved has a spate challenge as comp...In the current era of the internet,people use online media for conversation,discussion,chatting,and other similar purposes.Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks.There are several approaches to identify users’emotions fromthe conversational text for the English language,however regional or low resource languages have been neglected.The Urdu language is one of them and despite being used by millions of users across the globe,with the best of our knowledge there exists no work on dialogue analysis in the Urdu language.Therefore,in this paper,we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’emotions from the text.To accomplish this task,we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis.After that,we have preprocessed the data and selected dialogues with common emotions.Once the dataset is prepared,we have used different deep learning and machine learning techniques for the classification of emotion.We have tuned the algorithms according to the Urdu language datasets.The experimental evaluation has shown encouraging results with 67%accuracy for the Urdu dialogue datasets,more than 10,000 dialogues are classified into five emotions i.e.,joy,fear,anger,sadness,and neutral.We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.展开更多
Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diab...Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diabetes,hypertension,and some types of cancer.Therefore,it is vital to avoid obesity and or reverse its occurrence.Incorporating healthy food habits and an active lifestyle can help to prevent obesity.In this regard,artificial intelligence(AI)can play an important role in estimating health conditions and detecting obesity and its types.This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset.This dataset is in the form of electronic health records(EHR)containing data on several aspects of daily living,such as dietary habits,physical conditions,and lifestyle variables for various participants with different health conditions(underweight,normal,overweight,and obesity type I,II and III),expressed in terms of a variety of features or parameters,such as physical condition,food intake,lifestyle and mode of transportation.Three classifiers,i.e.,eXtreme gradient boosting classifier(XGB),support vector machine(SVM),and artificial neural network(ANN),are implemented to detect the status of several conditions,including obesity types.The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods,achieving overall performance rates of 98.5%and 99.6%in the scenarios explored.展开更多
Sentiment Analysis deals with consumer reviews available on blogs,discussion forums,E-commerce websites,andApp Store.These online reviews about products are also becoming essential for consumers and companies as well....Sentiment Analysis deals with consumer reviews available on blogs,discussion forums,E-commerce websites,andApp Store.These online reviews about products are also becoming essential for consumers and companies as well.Consumers rely on these reviews to make their decisions about products and companies are also very interested in these reviews to judge their products and services.These reviews are also a very precious source of information for requirement engineers.But companies and consumers are not very satisfied with the overall sentiment;they like fine-grained knowledge about consumer reviews.Owing to this,many researchers have developed approaches for aspect-based sentiment analysis.Most existing approaches concentrate on explicit aspects to analyze the sentiment,and only a few studies rely on capturing implicit aspects.This paper proposes a Keywords-Based Aspect Extraction method,which captures both explicit and implicit aspects.It also captures opinion words and classifies the sentiment about each aspect.We applied semantic similarity-basedWordNet and SentiWordNet lexicon to improve aspect extraction.We used different collections of customer reviews for experiment purposes,consisting of eight datasets over seven domains.We compared our approach with other state-of-the-art approaches,including Rule Selection using Greedy Algorithm(RSG),Conditional Random Fields(CRF),Rule-based Extraction(RubE),and Double Propagation(DP).Our results have shown better performance than all of these approaches.展开更多
Digitalization has changed the way of information processing, and newtechniques of legal data processing are evolving. Text mining helps to analyze andsearch different court cases available in the form of digital text...Digitalization has changed the way of information processing, and newtechniques of legal data processing are evolving. Text mining helps to analyze andsearch different court cases available in the form of digital text documents toextract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reducedtime. The rapid development of judicial ontologies seems to deliver interestingproblem solving to legal knowledge formalization. Mining context informationthrough ontologies from corpora is a challenging and interesting field. Thisresearch paper presents a three tier contextual text mining framework throughontologies for judicial corpora. This framework comprises on the judicial corpus,text mining processing resources and ontologies for mining contextual text fromcorpora to make text and data mining more reliable and fast. A top-down ontologyconstruction approach has been adopted in this paper. The judicial corpus hasbeen selected with a sufficient dataset to process and evaluate the results.The experimental results and evaluations show significant improvements incomparison with the available techniques.展开更多
文摘Objectives:The phytochemical investigation of traditional herbal medicines holds significant promise for modern drug discovery,particularly in cancer therapy.This study aimed to evaluate the cytotoxicity,apoptosis induction,and immune-modulatory activities of extracts from three herbal medicines with historical use in traditional medicine—Acanthopanax sessiliflorus,Phragmites communis,and Pinus densiflora,as well as their combined extract(GMAS 01/COM),on human lung cancer cells(A549)and normal cell lines,including murine macrophages(RAW 264.7)and human keratinocytes(HaCaT).Methods:Plant extracts were prepared using aqueous extraction,sonication,and rotary evaporation.The total phenolic and flavonoid contents were quantified using the Folin-Ciocalteu and AlCl3 colorimetric methods,respectively.Antioxidant potential was evaluated via 2,2-Diphenyl-1-picrylhydrazyl(DPPH)scavenging and reducing power assays.Cytotoxicity was assessed using an MTT assay,while reactive oxygen species(ROS)generation was quantified using a 2′,7′-Dichlorodihydrofluorescein diacetate(DCFH-DA)assay.Anticancer properties,including colony formation inhibition and migration suppression,were examined using colony formation and wound healing assays.The expression of apoptotic and inflammatory mediators was analysed through qPCR.Results:GMAS 01 selectively induced apoptosis in A549 cells without cytotoxic effects on RAW264.7 and HaCaT cells.Mechanistically,it elevated intracellular ROS and activated the intrinsic mitochondrial apoptotic pathway,evidenced by p53 upregulation,increased Bax,and decreased Bcl-2 expression.GMAS 01 also significantly inhibited colony formation and migration in A549 cells.In RAW264.7 cells,it reduced nitric oxide(NO)production and downregulated iNOS,COX-2,IL-6,and IL-8,indicating strong immunomodulatory activity.Conclusion:GMAS 01 exhibits potent antioxidant,anti-inflammatory,and anticancer effects,likely mediated through ROS-induced mitochondrial apoptosis.However,mechanistic interpretations are limited by the absence of protein-level validation and pathway inhibition studies.Upcoming studies should aim to verify the underlying mechanisms and evaluate their potential for real-world application.
文摘BACKGROUND Type 2 diabetes mellitus(T2DM)is increasing rapidly in Pakistan,especially among socioeconomically disadvantaged populations.While clinical care remains central,social determinants such as poverty,gender norms,and mistrust in healthcare critically shape disease outcomes.AIM To synthesize qualitative evidence on how these factors influence the experience and management of T2DM in Pakistan.METHODS Following PRISMA guidelines,a systematic review of qualitative studies published between 2000 and 2025 was conducted on February 25,2025 using PubMed,CINAHL,MEDLINE Plus,and PakMediNet.Eleven studies exploring socioeconomic influences on T2DM care and self-management in Pakistan were included.Thematic synthesis was used to identify key patterns.Quality was appraised using the Joanna Briggs Institute Checklist for Qualitative Research.RESULTS Three major themes were identified:(1)Economic insecurity.High cost of treatment,poor rural infrastructure,and food insecurity hinder access and adherence;(2)Sociocultural and gender norms.Restricted mobility of females,family control over health decisions,and fatalistic beliefs delay care;and(3)Knowledge gaps and mistrust.A lack of culturally appropriate education,reliance on traditional remedies,and distrust in public health systems reduce compliance.These intersecting barriers collectively impede effective diabetes management.CONCLUSION T2DM in Pakistan is driven by entrenched social and economic barriers.Addressing it requires culturally sensitive,equity-oriented strategies that go beyond biomedical models.Policy reforms should focus on affordability,rural outreach,and inclusive health education.Future research should engage marginalized voices through participatory methods.
文摘Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.
文摘The present study is focused on evaluation of deep marine pelagic sediments of Late Cretaceous Kawagarh Formation of Kala-Chitta Range in the context of microfacies analysis,paleoenvironmental interpretation,planktonic foraminiferal biostratigraphy,sequence stratigraphy and diversification of species.A total of thirty three rock samples were collected from the measured section.Three microfacies are interpreted,namely planktonic foraminifera wackestone,planktonic foraminifera mudstone and sandy mudstone indicating low energy depositional environment i.e.,outer ramp.The biostratigraphic studies show plentiful planktonic foraminifera species of Globotruncana,Heterohelix and Globotruncanita.However,no association of benthic or siliceous organisms was observed.On the basis of available species assemblage,a single local planktonic foraminifera biozone i.e.,Globotruncana-Heterohelix-Globotruncanita Assemblage Biozone is established.The biozone information is combined with published literature and Lower Santonian to Middle Maastrichtian age has been assigned to the Kawagarh Formation.The trend of species occurrences evinces that species number decreases over time with pulsated rise in the Kawagarh Formation.The Kawagarh Formation carbonates show an overall Transgressive Systems Tract(TST).The Kawagarh Formation of Pakistan evinces analogous characteristics to that of the Late Cretaceous Gurpi Formation of Iran based on the geologic age,outcrop lithology,microfacies,planktonic foraminiferal assemblages,depositional setting and sequence stratigraphy.
基金funded by the National Natural Science Foundation of China 11975145。
文摘We investigate the light deflection in the weak field approximation from the accelerating charged AdS black hole.For this purpose,we apply the Gauss–Bonnet theorem to calculate the light deflection in the weak field area and use the Gibbons–Werner approach to analyze the optical geometry of the accelerating charged AdS black hole in the non-magnetic plasma absence/presence of a non-magnetic medium.We also represent the graphical behavior of the light deflection angle w.r.t.the impact parameter.We also compute the light deflection angle using Keeton and Petters approximations under the impact of accelerating charged AdS black hole geometry.Furthermore,by using the ray-tracing approach,we determine the shadow in the nonmagnetic plasma presence and also demonstrate that graphical shadow has an impact on the gauge potential,non-magnetic plasma frequencies and charge.
基金supported by the National Key Technologies R & D Program of China during the 14th Five-Year Plan period (No. 2021YFD1700904)Henan Provincial Important Project (No. 221100320200)+1 种基金State Key Laboratory of Wheat and Maize Crap Science (No. SKL2023ZZ09)the Henan Center for Outstanding Overseas Scientists (No. GZS2021007)。
文摘Electronic nose(eNose) is a modern bioelectronic sensor for monitoring biological processes that convert CO_(2) into valueadded products, such as products formed during photosynthesis and microbial fermentation. eNose technology uses an array of sensors to detect and quantify gases, including CO_(2), in the air. This study briefly introduces the concept of eNose technology and potential applications thereof in monitoring CO_(2) conversion processes. It also provides background information on biological CO_(2) conversion processes. Furthermore, the working principles of eNose technology vis-à-vis gas detection are discussed along with its advantages and limitations versus traditional monitoring methods. This study also provides case studies that have used this technology for monitoring biological CO_(2) conversion processes. eNose-predicted measurements were observed to be completely aligned with biological parameters for R~2 values of 0.864, 0.808, 0.802, and 0.948. We test eNose technology in a variety of biological settings, such as algae farms or bioreactors, to determine its effectiveness in monitoring CO_(2) conversion processes. We also explore the potential benefits of employing this technology vis-à-vis monitoring biological CO_(2) conversion processes, such as increased reaction efficiency and reduced costs versus traditional monitoring methods. Moreover, future directions and challenges of using this technology in CO_(2) capture and conversion have been discussed. Overall, we believe this study would contribute to developing new and innovative methods for monitoring biological CO_(2) conversion processes and mitigating climate change.
文摘Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly treated.It is triggered by viral infections and presents symptoms such as chest pain and heart dysfunction.Early detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this condition.However,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per patient.To overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for training.Developing this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in Tehran.To address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority class.Additionally,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training phase.The effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance metrics.Overall,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.
文摘Polychloroprene (PC) based contact adhesives are widely used in various applications;however, there is a possibility to improve the properties of PC adhesive. Modifications of polymers can enhance the properties of the material, e.g. increase in thermal stability, compatibility, rigidity, physical response, flexibility and improve the polymer process ability. In the current study, improved formulation of solvent-based adhesive was developed, and the properties were further enhanced by the addition of nano-reinforcement of multiwall carbon nanotubes (MWCNTs). The addition of nano-reinforcement was optimized to obtain improvement in the bond strength and also to enhance its resistance at a high temperature (~100<span style="white-space:nowrap;">°</span>C). This paper discusses the uniform dispersion of MWCNTs during the synthesis of polychloroprene solvent-based adhesive, thereby improving its structural properties. Incorporation of MWCNTs-solvent-based adhesives resulted in a 20% - 35% improvement in 180<span style="white-space:nowrap;">°</span> peel strength determined on flexible substrates such as canvas, leather. The reinforced based adhesive also exhibited improved thermal stability and weather resistance compared with unreinforced adhesive. The MWCNTs- solvent-based contact adhesives is a potential candidate in an industrially relevant branch of adhesives commonly used in structural applications, e.g., footwear, plastic, leather, automobile, construction industries, etc.
基金Project supported by European Community’s Horizon 2020Program [H2020/2014-2019] under grant Agreement No.674973(MSCA-ETN DEMETER)
文摘The dependence of the magnetic properties on the particle size of recycled HDDR Nd-Fe-B powders was investigated,with the aim to assess the reprocessing potential of the end-of-life scrap magnets via spark plasma sintering(SPS).The as received recycled HDDR powder has coercivity(Hci)=830 kA/m and particles in the range from 30 to 700 μm(average 220 μm).After burr milling,the average particle size is reduced to 120 μm and subsequently the Hci of fine(milled) powder was 595 kA/m.Spark plasma sintering was exploited to consolidate the nanograined HDDR powders and limit the abnormal grain coarsening.The optimal SPS-ing of coarse HDDR powder at 750℃for 1 min produces fully dense magnets with Hci=950±100 kA/m which further increases to 1200 kA/m via thermal treatment at 750℃for 15 min.The burr milled fine HDDR powder under similar SPS conditions and after thermal treatment results in Hci=940 kA/m.The fine powder is further sieved down from 630 to less than 50 μm mesh size,to evaluate the possible reduction in Hci in relation to the particle size.The gain in oxygen content doubles for <50 μm sized particles as compared with coarser fractions(>200 μm).The XRD analysis for fractionated powder indicates an increase in Nd2O3 phase peaks in the finer(<100 μm)fractions.Similarly,the Hci reduces from 820 kA/m in the coarse particles(>200 μm) to 460 kA/m in the fine sized particles(<100μm).SPS was done on each HDDR powder fraction under the optimal conditions to measure the variation in Hci and density.The Hci of SPS-ed coarse fraction(>200 μm) is higher than 930 kA/m and it falls abruptly to just 70 kA/m for the fine sized particles(<100 μm).The thermal treatment further improves the Hci to>1000 kA/m only up to 100 μm sized fractions with>90% sintered density.The full densification(>99%) is observed only in the coarse fractions.The loss of coercivity and lack of sinterability in the fine sized particles(<100 μm) are attributed to a very high oxygen content.This implies that during recycling,if good magnetic properties are to be maintained or even increase the HDDR powder particles can be sized down only up to≥100 μm.
基金The authors would like to acknowledge the support from Taif University Researchers Supporting Project Number (TURSP-2020/264),Taif University,。
文摘This paper presents a compact Multiple Input Multiple Output(MIMO)antenna with WLAN band notch for Ultra-Wideband(UWB)applications.The antenna is designed on 0.8mmthick low-cost FR-4 substrate having a compact size of 22mm×30 mm.The proposed antenna comprises of two monopole patches on the top layer of substrate while having a shared ground on its bottom layer.The mutual coupling between adjacent patches has been reduced by using a novel stub with shared ground structure.The stub consists of complementary rectangular slots that disturb the surface current direction and thus result in reducing mutual coupling between two ports.A slot is etched in the radiating patch for WLAN band notch.The slot is used to suppress frequencies ranging from 5.1 to 5.9 GHz.The results show that the proposed antenna has a very good impedance bandwidth of|S11|<−10 dB within the frequency band from 3.1–14 GHz.A low mutual coupling of less than−23 dB is achieved within the entire UWB band.Furthermore,the antenna has a peak gain of 5.8 dB,low ECC<0.002 and high Diversity Gain(DG>9.98).
文摘Text Summarization is an essential area in text mining,which has procedures for text extraction.In natural language processing,text summarization maps the documents to a representative set of descriptive words.Therefore,the objective of text extraction is to attain reduced expressive contents from the text documents.Text summarization has two main areas such as abstractive,and extractive summarization.Extractive text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding principles.All such text extractions have limitations in providing the basic theme of the underlying documents.In this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word embedding.The study compared these forms of the text summarizations with the actual text,by calculating cosine similarities.Furthermore,TF-IDF based PageRank keywords are extracted from the other two extractive summarizations.An intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is performed.This technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each document.This form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized text.It also solves the issue of deciding the number of representative keywords for a specific text document.To evaluate the technique,the study used a sample of more than eighteen hundred text documents.The abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text summarization.The proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.
文摘This review efficiently covers the research progress in the area of polymer bio composites in perspective of the modern-day renewable materials.In the last decade,attraction towards the bio-composite based systems has been the topic of interest due to their potential as a substitute of conventional materials produced in important manufacturing industries.Recently,preparation of biocompatible and biodegradable polymer composites is an important achievement as an alternative of petrochemical based renewable products.Successful production of eco-friendly bio-composite materials have been achieved with natural fibers viz jute,bamboo,hair,flex,wool,silk and many others instead of synthesized fibers like carbon,glass dispersed in synthesized resins viz poly vinyl alcohol,epoxy and etc.Biomaterials based on natural fibers dispersed in natural matrix like natural rubber or polyester have also been obtained with endless applications for the mankind.The utilization of such materials for the good well of mankind is attributed to their ease of disposal and being renewable.The last but not the least,the extraordinary mechanical properties of bio-composites make them superior to many other conventional materials.This review paper addresses the recent trends,mechanical and chemical properties,synthesis,and application of bio-composites in the recent years.
基金sponsored by the synergistic innovation center of Jiangsu modern agriculture equipment and technology(No.4091600014).
文摘Agriculture is undoubtedly a leading field for livelihoods in China.As the population increases,it is necessary to increase agricultural productivity.By capturing the support and the increment in production on farms,the need for freshwater used for irrigation increases too.Presently,agriculture accounts for 80% of overall water uptake in China.Unexpected overflow of water carelessly leads to waste of water.Therefore we created a programmed plant irrigation system with Arduino that mechanically supplies water to the plants and keeps it updated by transferring the message to user.Plant irrigation system employs the soil moisture sensor which controls a degree of moisture in the soil.If the humidity degree is lower,Arduino activates a pump of water to supply water to the system.The pump of water stops by design when the organism detects sufficient moisture in the ground.Each time the system is switched off or on,an electronic messaging is conveyed to the end-user through the IoT unit,informing the position of the soil moisture and the pump of water.A spray motor and the pump of water are grounded on the crane concept.Widely,this system is applicable for in small fields,gardens farms,etc.This design is entirely programmed and needed no human involvement.Furthermore,transmission of the sensor readings send through a Thing speak frequency to produce graphic elements for better inquiry.This study gathers the ideas of IoT(Internet of Things)with some engineering tools like machinery,artificial intelligence and use of sensors in an efficient way to respond current needs and extraction of resources by availing scientific methods and procedures that work on inputs.Moreover,this study further defines the engineering works that have been part of this field,but it requires more efficiency and reduction of energy as well as costs by adding more contribution of IoT in the field of agriculture engineering.
文摘The Late Permian succession of the Upper Indus Basin in northeastern Pakistan is represented by the carbonatedominated Zaluch Group, which consists of the Amb, Wargal and Chhidru formations, which accumulated on the southwestern shelf of the Paleo-Tethys Ocean, north of the hydrocarbon-producing Permian strata of the Arabian Peninsula. The reservoir properties of the mixed clastic-carbonate Chhidru Formation(CFm) are evaluated based on petrography, using scanning electron microscopy(SEM), energy dispersive x-ray spectroscopy(EDX) and x-ray diffraction(XRD) techniques. The diagenetic features are recognized, ranging from marine(isopachous fibrous calcite, micrite), through meteoric(blocky calcite-I, neomorphism and dissolution) to burial(poikilotopic cement, blocky calcite-II-III, fractures, fracture-filling, and stylolites). Major porosity types include fracture and moldic, while inter-and intra-particle porosities also exist. Observed visual porosity ranges from 1.5%–7.14% with an average of 5.15%. The sandstone facies(CMF-4) has the highest average porosity of 10.7%, whereas the siliciclastic grainstone microfacies(CMF-3) shows an average porosity of 5.3%. The siliciclastic mudstone microfacies(CMF-1) and siliciclastic wacke-packestone microfacies(CMF-2) show the lowest porosities of 4.8% and 5.0%, respectively. Diagenetic processes like cementation, neomorphism, stylolitization and compaction have reduced the primary porosities;however, processes of dissolution and fracturing have produced secondary porosity. On average, the CFm in the Nammal Gorge, Salt Range shows promise and at Gula Khel Gorge, Trans-Indus, the lowest porosity.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia,for this research through a grant(NU/IFC/ENT/01/020)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia。
文摘Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss.State-of-the-art technologies have the potential for long-term benefits in post-surgery living.In this work,an Internet of Things(IoT)framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight.The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients.It also attempts to automate the data analysis and represent the facts about a patient.The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system.The proposed IoT framework also benefits from machine learning based activity classification systems,with relatively high accuracy,which allow the communicated data to be translated into meaningful information.
文摘In the current era of the internet,people use online media for conversation,discussion,chatting,and other similar purposes.Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks.There are several approaches to identify users’emotions fromthe conversational text for the English language,however regional or low resource languages have been neglected.The Urdu language is one of them and despite being used by millions of users across the globe,with the best of our knowledge there exists no work on dialogue analysis in the Urdu language.Therefore,in this paper,we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’emotions from the text.To accomplish this task,we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis.After that,we have preprocessed the data and selected dialogues with common emotions.Once the dataset is prepared,we have used different deep learning and machine learning techniques for the classification of emotion.We have tuned the algorithms according to the Urdu language datasets.The experimental evaluation has shown encouraging results with 67%accuracy for the Urdu dialogue datasets,more than 10,000 dialogues are classified into five emotions i.e.,joy,fear,anger,sadness,and neutral.We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia,for this research through a grant(NU/IFC/ENT/01/020)under the Institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diabetes,hypertension,and some types of cancer.Therefore,it is vital to avoid obesity and or reverse its occurrence.Incorporating healthy food habits and an active lifestyle can help to prevent obesity.In this regard,artificial intelligence(AI)can play an important role in estimating health conditions and detecting obesity and its types.This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset.This dataset is in the form of electronic health records(EHR)containing data on several aspects of daily living,such as dietary habits,physical conditions,and lifestyle variables for various participants with different health conditions(underweight,normal,overweight,and obesity type I,II and III),expressed in terms of a variety of features or parameters,such as physical condition,food intake,lifestyle and mode of transportation.Three classifiers,i.e.,eXtreme gradient boosting classifier(XGB),support vector machine(SVM),and artificial neural network(ANN),are implemented to detect the status of several conditions,including obesity types.The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods,achieving overall performance rates of 98.5%and 99.6%in the scenarios explored.
文摘Sentiment Analysis deals with consumer reviews available on blogs,discussion forums,E-commerce websites,andApp Store.These online reviews about products are also becoming essential for consumers and companies as well.Consumers rely on these reviews to make their decisions about products and companies are also very interested in these reviews to judge their products and services.These reviews are also a very precious source of information for requirement engineers.But companies and consumers are not very satisfied with the overall sentiment;they like fine-grained knowledge about consumer reviews.Owing to this,many researchers have developed approaches for aspect-based sentiment analysis.Most existing approaches concentrate on explicit aspects to analyze the sentiment,and only a few studies rely on capturing implicit aspects.This paper proposes a Keywords-Based Aspect Extraction method,which captures both explicit and implicit aspects.It also captures opinion words and classifies the sentiment about each aspect.We applied semantic similarity-basedWordNet and SentiWordNet lexicon to improve aspect extraction.We used different collections of customer reviews for experiment purposes,consisting of eight datasets over seven domains.We compared our approach with other state-of-the-art approaches,including Rule Selection using Greedy Algorithm(RSG),Conditional Random Fields(CRF),Rule-based Extraction(RubE),and Double Propagation(DP).Our results have shown better performance than all of these approaches.
文摘Digitalization has changed the way of information processing, and newtechniques of legal data processing are evolving. Text mining helps to analyze andsearch different court cases available in the form of digital text documents toextract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reducedtime. The rapid development of judicial ontologies seems to deliver interestingproblem solving to legal knowledge formalization. Mining context informationthrough ontologies from corpora is a challenging and interesting field. Thisresearch paper presents a three tier contextual text mining framework throughontologies for judicial corpora. This framework comprises on the judicial corpus,text mining processing resources and ontologies for mining contextual text fromcorpora to make text and data mining more reliable and fast. A top-down ontologyconstruction approach has been adopted in this paper. The judicial corpus hasbeen selected with a sufficient dataset to process and evaluate the results.The experimental results and evaluations show significant improvements incomparison with the available techniques.
