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Reliable Task Offloading for 6G-Based IoT Applications

computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

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Intelligent Automation & Soft Computing 2023年第7期37卷 73-90页

作者： Asma Qaiser Saman Hina Abdul Karim Kazi Saad Ahmed Raheela Asif Department of Computer Science and Information Technology NED University of Engineering and TechnologyKarachi75270Pakistan Department of Computer Science Iqra UniversityKarachi76400Pakistan Department of Software Engineering NED University of Engineering and TechnologyKarachi75270Pakistan

In the past few years,social media and online news platforms have played an essential role in distributing news content *** of the authenticity of news has become a major *** the COVID-19 outbreak,misinformation and fake news were major sources of confusion and insecurity among the general *** the first quarter of the year 2020,around 800 people died due to fake news relevant to *** major goal of this research was to discover the best learning model for achieving high accuracy and performance.A novel case study of the Fake News Classification using ELECTRA model,which achieved 85.11%accuracy score,is thus reported in this *** addition to that,a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been *** the COVAX-Reality dataset,the performance of FNEC is compared to several traditional learning models i.e.,Support Vector Machine(SVM),Naive Bayes(NB),Passive Aggressive Classifier(PAC),Long Short-Term Memory(LSTM),Bi-directional LSTM(Bi-LSTM)and Bi-directional Encoder Representations from Transformers(BERT).For the evaluation of FNEC,standard metrics(Precision,Recall,Accuracy,and F1-Score)were utilized.

关键词： Deep learning fake news detection machine learning transformer model classification

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Dysphonic Voice Pattern Based Parkinson Disease Detection Using Machine Learning Models 2

Dysphonic Voice Pattern Based Parkinson Disease Detection Us...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rhythm Khandelwal, Rajat Badlani, Prerna Saxena, Bhawna Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798350371055

Parkinson's Disease stands as a widespread cerebral degenerative disorder impacting millions of people across the world. Timely and precise diagnosis of PD is of paramount importance, enabling early interventions and enhancing overall patient care. The presented work focuses on non-invasive detection of PD through voice analysis, targeting early intervention. Dysphonic voice patterns, indicative of PD progression, are analyzed using advanced signal processing and machine learning. Methodology involves collecting voice recordings from PD-affected individuals and healthy controls, with rigorous analysis of acoustic features. This approach minimizes patient discomfort and reduces healthcare costs, potentially revolutionizing PD care and enhancing patient quality of life. This study explored the efficacy of 4 machine learning models - Decision Trees, k-Nearest Neighbors, Support Vector Machine, and Naive Bayes- in Parkinson's Disease (PD) detection. k-Nearest Neighbors classifier demonstrates much better performance compared to the other three, achieving an accuracy of 96.61%. © 2024 IEEE.

关键词： Dysphonic Voice Pattern KNN Machine Learning Parkinson's Disease

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Gene Variant Analysis for the Detection of Hemophilia: A Literature Review

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Recent Patents on engineering 2024年第9期18卷 48-62页

作者： Sumathi, N. Anitha Kumari, K. Department of Computer Science and Engineering Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Information Technology PSG College of Technology Tamil Nadu Coimbatore641004 India

Background: Proteins act as clotting factors to stop bleeding at the lesion site. This implies that people with hemophilia tend to bleed longer after an injury and are more prone to internal bleeding. Depending on the type of hemophilia, individuals with hemophilia will have lesser amounts of factor VIII or factor IX than people without it. Objective: By analyzing the gene variant of hemophilia affected patient we can predict the severity of disease at earlier stage which helps to avoid further complications. Methods: Predicting hemophilia can be achieved through potential technologies like machine learning. Using these technologies, one can detect and predict the severity of hemophilia, such as mild, moderate, or severe. Results: By comparing the methods used in protein structure analysis, the advantages and limitations of methods used in protein structure analysis are discussed. Conclusion: The best practices in predicting hemophilia are highlighted in this patent study and particularly aim at the basic understanding of applying the potential technologies in the prediction of hemophilia and its severity. This study represents recent research on hemophilia and the use of different machine learning techniques (MLT) in this area. © 2024 Bentham science Publishers.

关键词： Proteins

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Artificial intelligence-inspired comprehensive framework for desertification prediction

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International Journal of Cognitive Computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Soft transmission of 3D video for low power and low complexity scenario

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Digital Communications and Networks 2023年第3期9卷 769-778页

作者： Ao Li Taihai Yang Wenxin Wu Lei Luo School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology East China Normal UniversityShanghai200062China School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China

Thanks to the rapid development of naked-eye 3D and wireless communication technology,3D video related applications on mobile devices have attracted a lot of ***,the time-varying characteristics of the wireless channel is very challenging for conventional source-channel coding based transmission ***,the high complexity of source-channel coding based transmission scheme is undesired for low power mobile *** advanced transmission scheme named Softcast was proposed to achieve efficient transmission performance for 2D image/***,it cannot be directly applied to wireless 3D video transmission with high *** paper proposes a more efficient soft transmission scheme for 3D video with a graceful quality adaptation within a wide range of channel Signal-to-Noise Ratio(SNR).The proposed method first extends the linear transform to 4 dimensions with additional view dimension to eliminate the view redundancy,and then metadata optimization and chunk interleaving are designed to further improve the transmission ***,a synthesis distortion based chunk discard strategy is developed to improve the overall 3D video quality under the condition of limited *** experimental results demonstrate that the proposed method significantly improves the 3D video transmission performance over the wireless channel for low power and low complexity scenarios.

关键词： Softcast Low power 3D video Wireless

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Towards Robust Fake News Detection: An Emperical Investigation of Preprocessing and Modeling Techniques

Towards Robust Fake News Detection: An Emperical Investigati...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Porya, Anirban Singh, Varun Sinha, Bam Bahadur Indian Institute Of Information Technology Computer Science & Engineering Jharkhand Ranchi India National Institute Of Technology Sikkim Computer Science & Engineering Ravangla India

ISBN: (纸本)9798350306446

The challenge of identifying fake news through text classification has been met with extensive experimentation to find the best mix of preprocessing techniques and algorithms. Despite efforts, an effective fake news detection model is still out of reach, hindered by model implementation issues and the lack of clean, unbiased data. This situation highlights the need for further research in this area. We investigated many models and approaches for preparing data, including both deep learning algorithms like CNN and LSTM networks and more conventional machine learning algorithms like Random Forest and Gradient Boost. This exploration revealed specific challenges, notably the tendency of automated learning to focus narrowly on individual news items. This paper presents a thorough application of various documented preprocessing techniques and model architectures, aiming to lay a foundation for future research, particularly for newcomers. The performance of these models was tested on two widely used datasets, making a significant contribution to the field of fake news detection. The insights from this study are expected to guide future research and aid in developing more effective detection mechanisms. © 2024 IEEE.

关键词： Classification (of information)

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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

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computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

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A Locality-Sensitive-Hashing-Based Collaborative Recommendation Method for Responsible AI-Driven Recommender Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 393-404页

作者： Lin, Wenmin Zhou, Xinyi Sun, Lu Qi, Lianyong Tsai, Sang-Bing Yang, Yihong Liu, Hanwen Kou, Huaizhen Kong, Lingzhen Hangzhou Normal University Alibaba Business School Hangzhou311121 China College of Computer Science and Technology Qingdao266580 China International Engineering and Technology Institute Hong Kong999077 Hong Kong China University of Geosciences School of Information Engineering Beijing100083 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

As one of the most representative recommendation solutions, traditional collaborative filtering (CF) models typically have limitations in dealing with large-scale, sparse data to capture complex relationships between users and items. The rise of artificial intelligence (AI) provides powerful tools such as deep neural networks to overcome the data sparsity issue with typical CF models. Existing works on AI-driven recommender systems are focusing on improving recommendation accuracy by extracting rich user/item features from multisource data with deep learning tools to build more accurate user preference model. How to ensure the responsibility of AI-driven recommender systems is still a big challenge. On one hand, AI-driven recommender systems need to access raw data such as user profiles to train recommendation model, which face the risk of leaking user privacy information. On the other hand, the ever-increasing volume of user/item interaction records raises the efficiency challenge to provide recommendation results in a real-time manner. In view of those observations, we propose a collaborative recommendation method by adopting the locality-sensitive-hashing (LSH) technique (i.e., DisRecLSH), which aims to achieve the goal of privacy protection and calculation efficiency for AI-driven recommender systems. More specifically, we split DisRecLSH into three phases: 1) offline feature extraction: deep neural networks are applied to extract user/item features from multisource user/item data on each local dataset;2) offline user index building: LSH technique is adopted to map user features to hash codes to quickly recall correlated similar users for a given target user;and 3) online top-N items calculation: with similar users selected by phase 2, top-N items are calculated based on the ranking of predicted user-item rating score. Finally, extensive experiments are conducted on three public datasets to evaluate the efficiency of our proposal. © 2020 IEEE.

关键词： Recommender systems

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Ensemble Prototype Networks for Unsupervised Cross-modal Hashing with Cross-Task Consistency

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IEEE Transactions on Multimedia 2025年 27卷 3476-3488页

作者： Liu, Xiaoqing Zeng, Huanqiang Shi, Yifan Zhu, Jianqing Yang, Kaixiang Yu, Zhiwen Huaqiao University School of Information Science and Engineering Xiamen361021 China Huaqiao University School of Engineering Quanzhou362021 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

In the swiftly advancing realm of information retrieval, unsupervised cross-modal hashing has emerged as a focal point of research, taking advantage of the inherent advantages of the multifaceted and dynamism inherent in multimedia data. Existing unsupervised cross-modal hashing methods rely mainly on initial pre-trained correlations among cross-modal features, and the inaccurate neighborhood correlations impacts the presentation of common semantics throughout the optimization. To address the aforementioned issues, we propose Ensemble Prototype Networks (EPNet), which delineates class attributes of cross-modal instances through an ensemble clustering methodology. EPNet seeks to extract correlation information between instances by leveraging local correlation aggregation and ensemble clustering from multiple perspectives, aiming to reduce initialization effects and enhance cross-modal representations. Specifically, the local correlation aggregation is first proposed within a batch of semantic affinity relationships to generate a precise and compact hash code among cross-modal instances. Secondly, the ensemble prototype module is employed to discern the class attributes of deep features, thereby aiding the model in extracting more universally applicable feature representations. Thirdly, an early attempt to constrict the representational congruity of local semantic affinity relationships and deep feature ensemble prototype correlations using cross-task consistency loss aims to enhance the representation of cross-modal common semantic features. Finally, EPNet outperforms several state-of-the-art cross-modal retrieval methods on three real-world image-text datasets in extensive experiments. © 2025 IEEE.

关键词： Semantics

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