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Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

Computers, Materials & Continua 2024年第2期78卷 2047-2066页

作者： muhammad Rizwan muhammad Faheem Mushtaq Maryam Rafiq Arif Mehmood Isabel de la Torre Diez Monica Gracia Villar Helena Garay Imran Ashraf Department of Artificial Intelligence The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Information Technology Khwaja Fareed University of Engineering and ITRYKhan64200Pakistan Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Signal Theory and Communications and Telematic Engineering Unviersity of ValladolidPaseo de Belén 15Valladolid47011Spain Faculty of Social Science and Humanities Universidad Europea del Atlántico Isabel Torres 21Santander39011Spain Department of Project Management Universidad Internacional IberoamericanaCampeche24560México Fundación Universitaria Internacional de Colombia Bogotá111611Colombia Universidad Internacional Iberoamericana AreciboPuerto Rico00613USA Universidade Internacional do Cuanza Cuito46703Angola Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541South Korea

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media *** study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the ***,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic *** baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the *** FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to ***,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual *** proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText *** proposed model better captures the contextual features of the relatively small sample class and aids in the detection of

关键词： Depression classification deep learning FastText machine learning

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Impact of numerous larval diets on the biology of pink bollworm Pectinophora gossypiella(Saunders)(Lepidoptera:Gelechiidae)under laboratory conditions

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Journal of Cotton Research 2024年第1期7卷 69-78页

作者： AKHTAR Shamim ARIF muhammad Jalal GOGI muhammad Dildar HAQ Imran-ul Department of Entomology University of Agriculture FaisalabadPunjabPakistan Ayub Agricultural Research Institute(Entomological Research Institute) FaisalabadPunjabPakistan Department of Plant Pathology University of AgricultureFaisalabadPunjabPakistan

Background Pink bollworm,Pectinophora gossypiella(Saunders)(Lepidoptera:Gelechiidae)has become a poten-tial pest of cotton by causing substantial yield losses around the world including *** in view the facts like limited research investigations,unavailability,and high cost of artificial diet’s constituents and their premixes,the present research investigations on the dietary aspect of *** were *** larvae of *** were reared on different diets that were prepared using indigenous *** standard/laboratory diet com-prised of wheat germ meal 34.5 g,casein 30.0 g,agar–agar 20.0 g,sucrose 10.0 g,brewer’s yeast 5.0 g,α-cellulose 1.0 g,potassium-sorbate1.5 g,niplagin 0.5 g,decavitamin 0.01 g,choline-chloride 0.06 g,maize-oil 3.30 g,honey 2.0 g,and water 730.0 *** to cotton bolls and wheat germ meal were okra seed sprouts,okra fruit,cottonseed meal,and okra seed meals,which were included in the study to introduce an efficient and economic mass-rearing *** The larval development completed in 19.68d±0.05 d with a weight of 20.18mg±0.20 mg at the fourth instar fed on the cottonseed meal-based diet instead of wheat germ meal based *** the same diet,84.00%±4.00%,17.24 mg±0.03 mg,and 7.76d±0.06 d were recorded as pupae formation,pupal weight,and pupal duration,*** emergence,76.00%±1.00%was recorded from pupae collected from larvae raised on cottonseed meal-based *** male and female moths lived for 40.25d±0.10 d,and 44.34d±0.11 d,*** deposited 21.28±0.04 eggs per day with the viability of 65.78%±0.14%.The larval mortal-ity at the fourth instar was 37.20%±1.36%and malformed pupation of 12.00%±1.41%was *** of wheat germ meal with that of local meals(cottonseed and okra seed)in the standard laboratory diet has saved 463.80 to 467.10 PKR with 1.62 to 1.63 cost economic returns,*** This research is of novel nature as it provides a concis

关键词： Artificial diets Cottonseed meal Laboratory rearing Pectinophora gossypiella Wheat germ meal

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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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Computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： muhammad Ahmad Nawaz Ul Ghani Kun She muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

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Vision based intelligent traffic light management system using Faster R‐CNN

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 932-947页

作者： Syed Konain Abbas muhammad Usman Ghani Khan Jia Zhu Raheem Sarwar Naif R.Aljohani Ibrahim A.Hameed muhammad Umair Hassan Department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of ICT and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

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Genetics of biochemical attributes regulating morpho-physiology of upland cotton under high temperature conditions

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Journal of Cotton Research 2024年第1期7卷 29-44页

作者： MAJEED Sajid CHAUDHARY muhammad Tanees MUBARIK muhammad Salman RANA Iqrar Ahmad SHABAN muhammad TAN Daniel KY JIA Yinhua DU Xiongming HINZE Lori AZHAR muhammad Tehseen Federal Seed Certification and Registration Department Ministry of National Food Security and ResearchIslamabad 44090Pakistan Horticultural Research Institute National Agricultural Research CentreIslamabad 44000Pakistan Department of Biotechnology University of NarowalNarowal 51600Pakistan Centre of Agricultural Biochemistry and Biotechnology University of AgricultureFaisalabad 38400Pakistan Department of Plant Breeding and Genetics University of Agriculture FaisalabadSub-Campus BurewalaVehariPakistan Faculty of Science School of Life and Environ-mental SciencesPlant Breeding InstituteSydney Institute of AgricultureThe University of SydneySydneyNSW 2006Australia State Key Laboratory of Cotton Biology Institute of Cotton Research of Chinese Academy of Agri-cultural ScienceAnyang 455000China Agricultural Research Service USDASouthern Plains Agricultural Research CentreCollege StationTX 77845USA Department of Plant Breeding and Genetics University of AgricultureFaisal-abad 38400Pakistan School of Agriculture Sciences Zhengzhou UniversityZhengzhou 450000China.

Background Cotton is a strategically important fibre crop for global textile *** profoundly impacts several countries’industrial and agricultural *** cotton production is continuously threatened by the unpre-dictable changes in climate,specifically high *** heat-tolerant,high-yielding cotton cultivars with wide adaptability to be grown in the regions with rising temperatures is one of the primary objectives of modern cotton breeding ***,the main objective of the current study is to figure out the effective breed-ing approach to imparting heat tolerance as well as the judicious utilization of commercially significant and stress-tolerant attributes in cotton ***,the two most notable heat-susceptible(FH-115 and NIAB Kiran)and tolerant(IUB-13 and GH-Mubarak)cotton cultivars were spotted to develop filial and backcross populations to accom-plish the preceding study *** heat tolerant cultivars were screened on the basis of various morphological(seed cotton yield per plant,ginning turnout percentage),physiological(pollen viability,cell membrane thermostabil-ity)and biochemical(peroxidase activity,proline content,hydrogen peroxide content)*** The results clearly exhibited that heat stress consequently had a detrimental impact on every studied plant trait,as revealed by the ability of crossing and their backcross populations to tolerate high ***,when considering overall yield,biochemical,and physiological traits,the IUB-13×FH-115 cross went over particularly well at both normal and high temperature ***,overall seed cotton yield per plant exhibited a posi-tive correlation with both pollen viability and antioxidant levels(POD activity and proline content).Conclusions Selection from segregation population and criteria involving pollen viability and antioxidant levels concluded to be an effective strategy for the screening of heat-tolerant cotton g

关键词： Accessions Biochemical Breeding Cotton Generation mean analysis Heat stress

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Influence of Various Earth-Retaining Walls on the Dynamic Response Comparison Based on 3D Modeling

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2835-2863页

作者： muhammad Akbar Huali Pan Jiangcheng Huang Bilal Ahmed Guoqiang Ou Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengdu610000China Institute of International Rivers and Eco-Security Yunnan UniversityKunming650500China Department of Structural Engineering Faculty of Civil EngineeringDoctoral SchoolAkademicka 2Silesian University of TechnologyGliwice44-100Poland

The present work aims to assess earthquake-induced earth-retaining(ER)wall *** study is on the dynamics analysis of various earth-retaining wall designs in hollow precast concrete panels,reinforcement concrete facing panels,and gravity-type earth-retaining *** finite element(FE)simulations utilized a 3D plane strain condition to model full-scale ER walls and numerous nonlinear dynamics *** seismic performance of differentmodels,which includes reinforcement concrete panels and gravity-type and hollowprecast concrete ER walls,was simulated and examined using the FE *** also displays comparative studies such as stress distribution,deflection of the wall,acceleration across the wall height,lateral wall displacement,lateral wall pressure,and backfill plastic *** components of the created ER walls were found throughout this research *** is a granular reinforcement backfill,while the other is a wall-facing panel and base *** dynamic response effects of varied earth-retaining walls have also been *** was discovered that the facing panel of the model significantly impacts the earthquake-induced displacement of ER *** proposed analytical model’s validity has been evaluated and compared with the reinforcement concrete facing panels,gravity-type ER wall,scientifically available data,and American Association of State Highway and Transportation Officials(AASHTO)guidelines results based on FE *** results of the observations indicate that the hollow prefabricated concrete ER wall is the most feasible option due to its lower displacement and high-stress distribution compared to the two *** methodology and results of this study establish standards for future analogous investigations and professionals,particularly in light of the increasing computational capabilities of desktop computers.

关键词： Seismic analysis finite element modeling earth-retaining ER walls dynamic response structural resilience

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Methacrylated gelatin and platelet-rich plasma based hydrogels promote regeneration of critical-sized bone defects

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Regenerative Biomaterials 2024年第4期3卷 66-81页

作者： Shichao Lian Zhiyu Mu Zhengchao Yuan muhammad Shafiq Xiumei Mo Weidong Mu Department of Traumatic Orthopaedics Shandong Provincial HospitalShandong UniversityJinanShandong 250012China Zoucheng People’s Hospital ZouchengShandong 273500China Department of Medical Physics and Biomedical Engineering University of LondonLondon WC1E 6BTUK State Key Laboratory for Modification of Chemical Fibers and Polymer Materials Shanghai Engineering Research Center of Nano-Biomaterials and Regenerative MedicineCollege of Biological Science and Medical EngineeringDonghua UniversityShanghai 201620PR China Innovation Center of NanoMedicine(iCONM) Kawasaki Institute of Industrial PromotionKawasaki-KuKawasaki 210-0821Japan

Physiological repair of large-sized bone defects requires instructive scaffolds with appropriate mechanical properties,biocompatibility,biodegradability,vasculogenic ability and *** objective of this study was to fabricate in situ injectable hydrogels using platelet-rich plasma(PRP)-loaded gelatin methacrylate(GM)and employ them for the regeneration of large-sized bone *** performed various biological assays as well as assessed the mechanical properties of GM@PRP hydrogels alongside evaluating the release kinetics of growth factors(GFs)from *** GM@PRP hydrogels manifested sufficient mechanical properties to support the filling of the tissue *** biofunction assay,the GM@PRP hydrogels significantly improved cell migration and ***,transcriptome RNA sequencing of human umbilical vein endothelial cells and bone marrow-derived stem cells were performed to delineate vascularization and biomineralization abilities of GM@PRP *** GM@PRP hydrogels were subcutaneously implanted in rats for up to 4 weeks for preliminary biocompatibility followed by their transplantation into a tibial defect model for up to 8 weeks in *** defects treated with GM@PRP hydrogels manifested significant bone regeneration as well as angiogenesis,biomineralization,and collagen *** on the biocompatibility and biological function of GM@PRP hydrogels,a new strategy is provided for the regenerative repair of large-size bone defects.

关键词： large-sized bone defect repair platelet-rich plasma hydrogel tissue scaffold gelatin methacrylate

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A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2131-2164页

作者： Madiha Hameed Aneela Zameer muhammad Asif Zahoor Raja Department of Computer Sciences Pakistan Institute of Engineering and Applied SciencesNilore50700Islamabad Future Technology Research Center National Yunlin University of Science and TechnologyDouliou64002TaiwanChina Directorate of IT Services University of GujratGujrat50700Pakistan

The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer *** datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis *** challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human *** cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left *** paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their *** notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion *** review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation *** explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset *** leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model ***,ViTs alleviate augmentation issues by effectively leveraging original *** a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and *** study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological ***,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic

关键词： Medical image skin cancer classification skin cancer segmentation international skin imaging collaboration convolutional neural network deep learning

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On some new travelling wave structures to the(3+1)-dimensional Boiti-Leon-Manna-Pempinelli model

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Journal of Ocean Engineering and Science 2024年第2期9卷 99-111页

作者： Kalim U.Tariq Ahmet Bekir muhammad Zubair Department of Mathematics Mirpur University of Science and TechnologyMirpur 10250 AJKPakistan Neighbourhood of Akcaglan Imarli StreetNumber:28/4Eskisehir26030 Turkey

In this article,the(1/G')-expansion method,the Bernoulli sub-ordinary differential equation method and the modified Kudryashov method are implemented to construct a variety of novel analytical solutions to the(3+1)-dimensional Boiti-Leon-Manna-Pempinelli model representing the wave propagation through incompressible *** linearization of the wave structure in shallow water necessitates more critical wave capacity conditions than it does in deep water,and the strong nonlinear properties are *** novel travelling wave solutions have been observed including solitons,kink,periodic and rational solutions with the aid of the latest computing tools such as Mathematica or *** physical and analytical properties of several families of closed-form solutions or exact solutions and rational form function solutions to the(3+1)-dimensional Boiti-Leon-Manna-Pempinelli model problem are examined using Mathematica.

关键词： The(3+1)-dimensional Boiti-Leon-Manna-Pempinelli model The(1/G')-expansion method The Bernoulli sub-ODE method The modified Kudryashov method

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A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

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Computers, Materials & Continua 2024年第5期79卷 3373-3395页

作者： Farhan Ullah Xuexia Zhang Mansoor Khan muhammad Abid Abdullah Mohamed School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China College of Intelligent Manufacturing and Control Engineering Qilu Institute of TechnologyJinan250200China College of Aerospace and Civil Engineering Harbin Engineering UniversityHarbin150001China Research Centre Future University in EgyptNew Cairo11835Egypt

Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance *** approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering *** approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-EraRetrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.

关键词： Ensemble learning machine learning real-time data analysis stakeholder analysis temporal convolutional network wind power forecasting

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