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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： A. Mahendran Sridhar Babu Nalla Lalit Mohan Trivedi S Mohankumar D.R Prince Williams Manne Renuka Department of Computer Science Sona College of Arts and Science Salem India Department of Computer Science and Engineering SR University Hyderabad India Department of Ash Mathematics Moradabad Institute of Technology Moradabad India Department of Computer Science and Engineering Erode Sengunthar Engineering College Perundurai India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Science- Sohar Sohar Sultanate of Oman Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Medical treatment costs are rising for many reasons. These include an increasing world population, an aging population, greater healthcare needs, more diseases, and inflation. With AI's emergence in navigation, voice recognition, image lesion segmentation, ride-sharing, and smartphone personal assistants, the healthcare industry has quickly adopted it. Initial preprocessing includes standardization, missing values, and discarding anomalies. Next, the model is trained using the obtained features. Spearman correlation, a rank-based nonparametric statistic, can quantify monotonic correlation strength and direction. Even with missing values, the Graph Convolutional Network (GCN) performs well in model training. The suggested model outperformed state-of-the-art alternatives with 93.48% accuracy. It outperformed TransLSTM and GraphSAGE on complex datasets. This research stresses the system's ability to improve healthcare forecast accuracy through preprocessing, feature extraction, and GCN model training. Results show that AI can improve healthcare analytics despite lacking data.

关键词： Training Support vector machines Correlation Accuracy Graph convolutional networks Medical services Feature extraction Integrated circuit modeling Artificial intelligence Stress

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LIFEGPT: TOPOLOGY-AGNOSTIC GENERATIVE PRETRAINED TRANSFORMER MODEL FOR CELLULAR AUTOMATA

arXiv

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arXiv 2024年

作者： Berkovich, Jaime A. Buehler, Markus J. Department of Materials Science and Engineering Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Department of Civil and Environmental Engineering Department of Mechanical Engineering Center for Computational Science and Engineering Schwarzmann College of Computing Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States

Conway’s Game of Life (Life), a well known algorithm within the broader class of cellular automata (CA), exhibits complex emergent dynamics, with extreme sensitivity to initial conditions. Modeling and predicting such intricate behavior without explicit knowledge of the system’s underlying topology presents a significant challenge, motivating the development of algorithms that can generalize across various grid configurations and boundary conditions. We develop a decoder-only generative pretrained transformer (GPT) model to solve this problem, showing that our model can simulate Life on a toroidal grid with no prior knowledge on the size of the grid, or its periodic boundary conditions (LifeGPT). LifeGPT is topology-agnostic with respect to its training data and our results show that a GPT model is capable of capturing the deterministic rules of a Turing-complete system with near-perfect accuracy, given sufficiently diverse training data. We also introduce the idea of an ‘autoregressive autoregressor’ to recursively implement Life using LifeGPT. Our results pave the path towards true universal computation within a large language model framework, synthesizing of mathematical analysis with natural language processing, and probing AI systems for situational awareness about the evolution of such algorithms without ever having to compute them. Similar GPTs could potentially solve inverse problems in multicellular self-assembly by extracting CA-compatible rulesets from real-world biological systems to create new predictive models, which would have significant consequences for the fields of bioinspired materials, tissue engineering, and architected materials design. © 2024, CC BY-NC-ND.

关键词： Inverse problems

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Arabic Fake News Detection Using Machine Learning Approach

Arabic Fake News Detection Using Machine Learning Approach

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2024 International Symposium on Networks, Computers and Communications, ISNCC 2024

作者： Duridi, Tasneem Eleyan, Derar Eleyan, Amna Bejaoui, Tarek Palestine Technical University Department of Computer Science Tulkarm Palestine Nablus University for Technical and Vocational Education Faculty of Telecomm. and Information Technology Nablus Palestine Palestine Technical University - Kadoorie Department of Computer Science Tulkarm Palestine Manchester Metropolitan University Department of Computing and Mathematics ManchesterM1 5GD United Kingdom University of Carthage Computer Engineering Department Tunisia

ISBN: (纸本)9798350364910

In the contemporary digital realm, the widespread dissemination of false Arabic news is a significant social concern laden with various risks. Recognizing the seriousness of this issue, our research utilizes cutting-edge technologies, specifically Machine Learning, to distinguish between authentic Arabic news and deceptive counterparts. The consequences of propagating misinformation go beyond compromising social cohesion;they extend to the erosion of digital information's credibility, fostering an atmosphere of mistrust and deception that undermines the very foundations of societal bonds. Through the application of contemporary technologies, this study aims to identify and underscore Arabic news that embodies such risks. The intricate characteristics of Arabic language morphology, marked by words carrying multiple meanings based on inflectional forms and the prevalence of numerous diacritical marks, intensify the challenges of text classification. Despite contending with these linguistic intricacies, modern natural language processing approaches offer practical solutions. Notably, our methodology relies on the preprocessing of two available datasets for training and testing, a crucial step for seamlessly integrating a range of Machine Learning techniques, including Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and Multinomial Naive Bayes (NB). Importantly, the Logistic Regression technique emerged as the most effective, achieving an accuracy of 95.92% in the SANAD dataset for discerning nuanced Arabic news © 2024 IEEE.

关键词： Adversarial machine learning

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A Study on Word Embeddings in Local LLM-based Chatbot Applications

A Study on Word Embeddings in Local LLM-based Chatbot Applic...

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International Conference on Innovation and Intelligence for Informatics, computing, and Technologies (3ICT)

作者： Hariprasath S Arjun Paramarthalingam Shanmugam Sundaramurthy Stefano Cirillo Department of Computer Science and Engineering University College of Engineering Kakuppam Villupuram India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu India Department of Computer Science University of Salerno Salerno Italy

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

Large Language Models (LLMs) have revolutionized natural language processing, but their deployment often requires substantial computational resources and constant network con-nectivity. There is a growing need for efficient, locally-runnable LLMs with improved word embeddings for various applications, including chatbots. This study focuses on developing a Large Language Model (LLM) capable of running locally without network connections to generate solutions for given queries using improved word embeddings. In this work, two pre-trained models are used and further trained separately to enhance embedding results. The system incorporates additional features such as document-based chat functionality. Our approach develops a dynamic and adaptive NLP system that can assimilate new data from various sources when connected to a network, ensuring up-to-date word embeddings that capture the latest semantic nuances and language trends. The study compares FastText, GloVe, and Word2Vec models, finding that fastText outperforms the others in text generation within the specified time frame. While fastText may require more time to generate larger content, its efficacy in producing better results makes it the preferred choice for this application.

关键词： Adaptation models Technological innovation Adaptive systems Large language models Semantics Chatbots Market research Informatics

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Extended Kalman Filter Algorithm to Detect False Data Injection in Wireless Sensor Network

Extended Kalman Filter Algorithm to Detect False Data Inject...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： R Babuji J. Jeya Ganesan D. Lekha M. Karthikeyan D. M. Kalai Selvi Department of Electronics and Communication Engineering RMK Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science And Technology Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.D. Engineering College Chennai Tamil Nadu India

Wireless Sensor Networks (WSN) contains spatially distributed sensor nodes that collaborate with each other. However, the WSN is susceptible since the wireless medium is unpredictable. Several conventional approaches are used to distinguish the abnormal in the WSN, and all have difficulties. An Extended Kalman Filter Algorithm (EKFA) to detect false inject data in WSN is proposed in this paper. It addresses two issues such as abnormality recognition and obstacle detection. It also distinguishes false insert data. Particularly, it observes neighbors' behaviors and then applying EKFA to forecast their future states. The node behavior is observed by incorporating abnormal node examination and system examination elements. The Path Optimization Method (POM) is a shortest route that is used to discover the obstacle in the WSN. The EKFA mechanism minimizes the forwarder count and delay by applying POM. Simulation results demonstrate that the EKFA approach enhances abnormal node detection and minimizes the false negative ratio.

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BeliN: A Novel Corpus for Bengali Religious News Headline Generation using Contextual Feature Fusion

arXiv

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arXiv 2025年

作者： Osama, Md Dey, Ashim Ahmed, Kawsar Kabir, Muhammad Ashad Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Raozan4349 Bangladesh Saidpur Nilphamari5310 Bangladesh School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW2795 Australia

Automatic text summarization, particularly headline generation, remains a critical yet underexplored area for Bengali religious news. Existing approaches to headline generation typically rely solely on the article content, overlooking crucial contextual features such as sentiment, category and aspect. This limitation significantly hinders their effectiveness and overall performance. This study addresses this limitation by introducing a novel corpus, BeliN (Bengali Religious News) - comprising religious news articles from prominent Bangladeshi online newspapers, and MultiGen - a contextual multi-input feature fusion headline generation approach. Leveraging transformer-based pre-trained language models such as BanglaT5, mBART, mT5, and mT0, MultiGen integrates additional contextual features-including category, aspect, and sentiment-with the news content. This fusion enables the model to capture critical contextual information often overlooked by traditional methods. Experimental results demonstrate the superiority of MultiGen over the baseline approach that uses only news content, achieving a BLEU score of 18.61 and ROUGE-L score of 24.19, compared to baseline approach scores of 16.08 and 23.08, respectively. These findings underscore the importance of incorporating contextual features in headline generation for low-resource languages. By bridging linguistic and cultural gaps, this research advances natural language processing for Bengali and other underrepresented languages. To promote reproducibility and further exploration, the dataset and implementation code are publicly accessible at https://***/akabircs/BeliN. © 2025, CC BY-NC-ND.

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Spike learning based Privacy Preservation of Internet of Medical Things in Metaverse

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-8页

作者： Khowaja, Sunder Ali Dahri, Kamran Jarwar, Muhammad Aslam Lee, Ik Hyun Faculty of Engineering and Technology University of Sindh Pakistan Department of Computing Sheffield Hallam University Sheffield U.K Department of Mechatronics Engineering Tech University of Korea Siheung-Si Republic of Korea

With the rising trend of digital technologies, such as augmented and virtual reality, Metaverse has gained a notable popularity. The applications that will eventually benefit from Metaverse is the telemedicine and e-health fields. However, the data and techniques used for realizing the medical side of Metaverse is vulnerable to data and class leakage attacks. Most of the existing studies focus on either of the problems through encryption techniques or addition of noise. In addition, the use of encryption techniques affects the overall performance of the medical services, which hinders its realization. In this regard, we propose Generative adversarial networks and spike learning based convolutional neural network (GASCNN) for medical images that is resilient to both the data and class leakage attacks. We first propose the GANs for generating synthetic medical images from residual networks feature maps. We then perform a transformation paradigm to convert ResNet to spike neural networks (SNN) and use spike learning technique to encrypt model weights by representing the spatial domain data into temporal axis, thus making it difficult to be reconstructed. We conduct extensive experiments on publicly available MRI dataset and show that the proposed work is resilient to various data and class leakage attacks in comparison to existing state-of-the-art works (1.75x increase in FID score) with the exception of slightly decreased performance (less than 3%) from its ResNet counterpart. while achieving 52x energy efficiency gain with respect to standard ResNet architecture. IEEE

关键词： Cryptography

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Interior Angle Computation for detecting Adversary Sensor in Wireless Sensor Networks

Interior Angle Computation for detecting Adversary Sensor in...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), International Conference

作者： R. Saranya K. Anitha A. Ayub Khan M. Sivakumar R. Arthi Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science Engineering (Cyber Security) Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

After being distributed over a wireless sensor network (WSN), the sensor nodes in the network normally remain in the same place permanently. An attacker who compromises a subset of the nodes is not essential to stand by the same restriction. This article introduces Interior Angle Computation for detecting adversary sensor (IACD) in WSN. The main awareness of this scheme for applying the angle-based method is to decide the adversary node. In this case, the sender wishes to transmit the information to the receiver, and the sender selects the neighbor node that will serve as the next hop in the network. The subsequent hop chooses, from among the neighbors of the sender node, the sensor node that is geographically closest to the recipient. The sender selected the neighboring node, and the sensor node that was physically closest to it created the triangle. In this triangle, the three inner angles add up to a total of 180 degrees, and in order to achieve a value of 180 degrees for the triangle, each of these three nodes must be a genuine sensor node. If it is not, then the node is an opponent. In addition, this approach applies a binary tree algorithm to improve the routing efficiency. It improves routing efficiency. The simulation results explain that the IACD has a better packet rate and increases the remaining energy in the network. In addition, it increases the adversary detection ratio in the WSN.

关键词： Wireless sensor networks Simulation Receivers Binary trees Routing Security

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Hybrid Music Recommendation System Utilizing Item Popularity and Content-Based Filtering Innovations in music

Hybrid Music Recommendation System Utilizing Item Popularity...

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Aditya Singh Utsav Porwal P. Selvaraj Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350333855

recommendation systems are increasing, making it viable to customize the listening trends and fine-tune the selections per the user’s preferences. Over the years, every user’s personalized music playlist can be primarily customized based totally on the statement of that user’s behavior. In this work, we advocate a Hybrid Music advice machine using object recognition and content analysis primarily based on innovative filtering. We illustrated its implementation for virtual audio, a product class with swiftly increasing trends. The proposed machine routinely indicates customized playlists of MP3 songs for every user. The machine works in real-time and is scalable to the significant statistics generally encountered in personalization applications. We applied this personalization system on the "Million-Song Dataset" and examined its overall performance with several users. Moreover, our proposed recommendation system performs well in recommending the music based on the item’s popularity and user-centric content filtering.

关键词： Technological innovation Filtering Market research Real-time systems Behavioral sciences Object recognition Artificial intelligence

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A Bio-Inspired Metaheuristics Algorithm for Reduction of Coverage Holes in Optimizing the Performance of Next Generation Wireless Network (NGWN) 2nd

A Bio-Inspired Metaheuristics Algorithm for Reduction of Cov...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ajibade, Samuel-Soma M. Abdul Majeed, Anwar P. P. Oyebode, Oluwadare Joshua Jasser, Muhammed Basheer Ajayi, Babatunde Adedotun Alebiosu, David Olayemi Luo, Yang Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia Civil and Environmental Engineering Afe Babalola University Ekiti Ado Ekiti Nigeria Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

While Next Generation Wireless Networks are widely used, controlling them can be challenging regardless of whether a network is fully connected, there may be coverage gaps. In this study, we provide a technique for placing nodes in a wireless network while making sure that there is a minimum coverage hole in the network coverage area. We use the firefly algorithm to close the coverage gap in order to increase connectivity. The Firefly algorithm provides a novel solution for overcoming the drawbacks of traditional overburden hole detection techniques, such as downtime, low accuracy, and high operating costs, by installing sensor nodes in the appropriate locations and scheduling those network devices. The algorithm looks for the best locations where the NGWN sensor may be placed inside the designated zone in order to reduce the coverage hole area. The algorithm will treat randomly generated solutions as fireflies, and it will determine how bright they are based on how well they perform in terms of the goal function. Simulation results show that the proposed method decreases the covering hole area by 96%. The provided approach was suitable for NGWN since it reduced computation time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Operating costs

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