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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Periya Nayaki, A. Vidyabharathi, A.K. Krishnaveni, S. Thanabal, M.S. Department of Information Technology Velammal College of Engineering and Technology Madurai India Computer Science and Engineering PSNA College of Engineering and Technology Madurai India

ISBN: (纸本)9783031699818

Alzheimer’s disease typically leads in the death of neurons and the connections between them in memory-related parts of the brain, such as the entorhinal cortex and hippocampus. Following that, it affects the areas of the cerebral cortex responsible for language, cognition, and social interaction. Several other regions of the brain get harmed at some time. Patients with Alzheimer’s disease gradually lose their capacity to function normally and go about their daily lives. Currently, age is regarded as one of the most important risk factors;the majority of patients with dementia are 65 or older. Symptoms of the condition include increased disorientation, memory loss, including the inability to recall recent events or personal history, and learning difficulties. As of yet, there is no treatment for Alzheimer’s. A vast quantity of data will be acquired from the previously affected person, and the user input data will be compared to past data using machine learning techniques to determine whether they are still impacted. Finding the best categorization approach is difficult because it necessitates adapting to regional datasets. The methods LightGBM, AdaBoost, Random Forest Classifier, Support-Vector-Machine, Decision-Tree, and XGBoost provided accuracy rates of 78%, 86%, 81%, 73%, and 84%, respectively. In this work, the Voting Classifier algorithm, which may be more accurate than other methods, will be employed. © IFIP International Federation for Information Processing 2024.

关键词： Decision trees

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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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Fake News Detection on Social Media Using Ensemble Methods

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computers, Materials & Continua 2024年第12期81卷 4525-4549页

作者： Muhammad Ali Ilyas Abdul Rehman Assad Abbas Dongsun Kim Muhammad Tahir Naseem Nasro Min Allah Department of Computer Science COMSATS UniversityIslamabad45550Pakistan School of Computer Science and Engineering Kyungpook National UniversityDaegu41566Republic of Korea Department of Computer Science and Engineering Korea UniversitySeoul02841Republic of Korea Department of Electronic Engineering Yeungnam UniversityGyeongsan-si38541Republic of Korea Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam34223Saudi Arabia

In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of distinguishing between truthful and deceptive *** news,a prevalent issue,particularly on social media,complicates the assessment of news *** pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources,creating confusion and polarizing *** the volume of information grows,individuals increasingly struggle to discern credible content from false narratives,leading to widespread misinformation and potentially harmful *** numerous methodologies proposed for fake news detection,including knowledge-based,language-based,and machine-learning approaches,their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or *** study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news *** employ multiple feature extraction methods,including Count Vectorizer,Bag of Words,Global Vectors for Word Representation(GloVe),Word to Vector(Word2Vec),and Term Frequency-Inverse Document Frequency(TF-IDF),alongside feature selection techniques such as Information Gain,Chi-Square,Principal Component Analysis(PCA),and Document *** comprehensive approach enhances the model’s ability to identify and analyze relevant features,leading to more accurate and effective fake news *** findings highlight the importance of a multi-faceted approach,offering a significant improvement in model accuracy and ***,the study emphasizes the adaptability of the proposed ensemble model across diverse datasets,reinforcing its potential for broader application in real-world *** introduce a pioneering ensemble

关键词： Fake news detection Machine Learning(ML) Deep Learning(DL) Chi-Square ensembling

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Deep Learning Based Tuberculosis Classification from Chest X-ray Images 15

Deep Learning Based Tuberculosis Classification from Chest X...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kavitha, S. Mahalakshmi, A. Prakash, P. Thambi Raj, V. Computer Science and Engineering Kongu Engineering College Erode India Computer Science and Engineering Sri Eshwar college of Engineering Coimbatore India Medical Electronics Velalar College of Engineering and Technology Erode India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370249

Tuberculosis (TB) is a serious health issue that kills a lot of people these days. TB is completely curable if it is discovered early enough. One way of diagnosing tuberculosis (TB) early on is to undergo a chest X-ray (CXR), which can show if a person has active TB. Chest x-ray images (CXR) can be analyzed to diagnose the deadly TB disease more accurately owing to deep learning (DL), a significant area of artificial intelligence. To automate tuberculosis (TB) identification, this study used Convolutional Neural Networks (CNNs), a form of deep learning models designed specifically for image processing. This architecture needs a lot of datasets to learn features, which makes the learning process take longer. Transfer Learning (TL), which uses a large number of pre-trained models to perform disease classification, is suggested as a solution to this issue. In this work, four different CNN architecturesEfficientNetB0, AlexNet, GoogleNet, and Xception-are trained and evaluated. The results demonstrated that GoogleNet could diagnose tuberculosis with an astounding 97% accuracy, even after extended training periods. By comparison, AlexNet trained faster due to its simpler architecture, but having a little lower accuracy of 96%. © 2024 IEEE.

关键词： Deep learning Accuracy Tuberculosis Computational modeling Transfer learning Predictive models Feature extraction Data models X-ray imaging Medical diagnostic imaging

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Analyzing Body Language and Facial Expressions Using Machine Learning Techniques

Analyzing Body Language and Facial Expressions Using Machine...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Patel, Harvendra Kumar Rai, Vijendra Singh, Hare Ram Kumar, Rakesh Iimt College of Engineering Greator Department of Computer Science and Engineering Noida India Iec College of Engineering and Technology Greator Department of Computer Science and Engineering Noida India Gniot Noida India

ISBN: (纸本)9798331508685

Body Language decoding is an important aspect of understanding human emotions, intentions, and personality by interpreting non-verbal cu es. It helps to get insights into human behavior, mindset, and psychology of the person. AI Body Talk is a software system that is capable of detecting and interpreting the body language of a person in real-time inference. It can recognize the sign language along with face, hand, and pose estimation with immediate feedback. It empowers the minority group of society who use sign language to communicate in society equipped with the Mediapipe to train custom models with its pre-built solutions to detect face landmarks, hand-tracking, and pose estimation. The results exemplify the system's potential for various applications like monitoring audience distraction, inspecting interviewee personality, understanding cultural sensitivity, diagnosing patients' health, and more. © 2025 IEEE.

关键词： Face recognition

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Validating ML algorithms for efficient Breast Cancer Prediction using K-fold 7

Validating ML algorithms for efficient Breast Cancer Predict...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Refonaa, J. Shabu, S. L. Jany Karthik, Janapati Vardhan, A. Harsha Suresh, L. Padma Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

Breast cancer has always been a disease that affects a lot of people. There is vast range of factors that may lead to breast cancer and this makes it more difficult to detect at an early stage. The tissues called benign or malignant present in the breast profile are so identical that it makes detecting cancer very complex for the doctors. Performing various tests like uniformity of cell shape and thickness of tissues may result in no valuable insights. This gives rise to machine learning and artificial intelligence that can be used in the prognosis of breast cancer. Here we attempt at correlating diverse machine learning algorithms to effectively predict breast cancer over a dataset. The five diverse machine learning algorithms employed are Naïve Bayes, Decision Tree, Neural Networks (Multilayer Perceptron), SVM and K-nearest Neighbour. An effective comparative study has been done to filter out the best Algorithm based on their Performance. © 2024 IEEE.

关键词： Lung cancer

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A Comparative Study on Deep Learning based Segmentation Techniques for Tamil Inscriptions 8

A Comparative Study on Deep Learning based Segmentation Tech...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Poornimathi, K. Muralibhaskaran, V. Priya, L. Rajalakshmi Engineering College Department of Computer Science and Business Systems India Rajalakshmi Engineering College Department of Computer Science and Engineering India Rajalakshmi Engineering College Department of Information Technology India

ISBN: (纸本)9798350376425

The digitization and preservation of Tamil inscriptions are crucial for safeguarding the rich cultural heritage they represent. This study presents an in-depth evaluation of deep learning-based segmentation methods specifically designed for the complex and ancient Tamil script. Evaluate several cutting-edge deep learning models, including Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, and attention-based mechanisms, using a dataset of Tamil inscriptions. The evaluation criteria encompass segmentation accuracy, computational efficiency, and the ability to handle the noise and degradation common in historical artifacts. Our findings indicate that traditional methods often falter when faced with the complexities of Tamil script, whereas advanced deep learning techniques markedly enhance segmentation quality. This improvement facilitates more precise transcription and analysis. The comparison emphasizes the benefits and challenges of each method, delivering important insights into their effectiveness for cultural heritage preservation and paving the way for future research in the digitization of ancient scripts. © 2024 IEEE.

关键词： Convolutional neural networks

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Zero Trust Networks: Evolution and Application from Concept to Practice

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computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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DeBERTa-GRU: Sentiment Analysis for Large Language Model

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computers, Materials & Continua 2024年第6期79卷 4219-4236页

作者： Adel Assiri Abdu Gumaei Faisal Mehmood Touqeer Abbas Sami Ullah Department of Informatics for Business College of BusinessKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Department of Computer Science and Technology Beijing University of Chemical TechnologyBeijing100029China Department of Computer Science Government College University FaisalabadFaisalabadPunjab38000Pakistan

Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

关键词： DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model

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Internet of Things Applicable Authentication and Authorization Based on a Two-Layer Blockchain Approach

Internet of Things Applicable Authentication and Authoriza...

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International Conference on Power engineering and Intelligent Systems, PEIS 2024

作者： Kothari, Rakshit Jain, Kalpana Choudhary, Naveen Department of Computer Science and Engineering College of Technology and Engineering Maharana Pratap University of Agriculture and Technology Udaipur India

ISBN: (纸本)9789819767090

The Internet of Things (IoT) connects billions of devices and generates massive amounts of data. However, IoT systems face major security challenges, especially regarding authentication and authorization. Centralized approaches have single points of failure and limited scalability. Blockchain technology offers a decentralized solution, but public blockchains like Bitcoin and Ethereum have limitations for IoT devices due to computational overhead and latency. This paper proposes IoT-optimized two-layer blockchain architecture for authentication and authorization. The first layer is a consortium blockchain optimized for IoT devices. The second layer is a public blockchain for decentralized identity and access control. Cross-chain protocols connect the two layers securely. Linear secret sharing schemes (LSSS) implement fine-grained, attribute-based access control policies in smart contracts. Experiments on Ethereum and Hyperledger Fabric validate the approach’s efficiency and security compared to centralized methods. The two-layer model significantly reduces overhead for IoT devices while retaining decentralization benefits. The architecture provides resilient, scalable access control suitable for distributed IoT networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ethereum

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