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22nd International Symposium on Communications and Information Technologies, ISCIT 2023

作者： Patil, Shantakumar Lutimath, Nagaraj M. Jogish, D. Premjyoti Patil, Bhargav S. Sai Vidya Institute of Technology Computer Science and Engineering Bengaluru India Dayananda Sagar Academy of Technology and Management Computer Science and Engineering Bengaluru India Bengaluru India B.M.S. College of Engineering Computer Science and Engineering Karnataka Bengaluru560019 India

ISBN: (纸本)9781665457316

The domain of medical diagnosis has attracted many researchers. Several cases of human early mortality have been predicted by investigating the diseases. Different reasons are the cause of several diseases and one such cause is heart disease. Set of disorders in the heart lead to heart disease. It consists of blood vessel issues such as irregular heart beat problems, weak heart muscle problems, congenital heart difficulties, cardio vascular defects and coronary artery disorder. Many researchers have summarized idiosyncratic procedures to preserve human life and help health care experts to recognize, prevent and manage heart disorder. Machine Learning is utilized for decision making in many domains. Naive Bayes technique is one approach which uses conditional probability. In this paper we have taken UCI machine learning data repository including of patients affected from heart disease is analyzed using Complement Naive Bayes probability methodology along with correlated features of the heart disease data set. The prediction is done using this concept as hybrid Naive Bayes probability technique. The advantage of Complement Naive Bayes probability prediction is that, it can be used for imbalanced data set. Accuracy of classification of patients suffering from heart disease is predicted. Implementation is done in Python environment. © 2023 IEEE.

关键词： Python

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A Compact Home-Based Training System for Preventing Frailty Using a Mapping Model and Cross-Dataset Transfer Learning

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 1-8页

作者： Liu, Lizheng Hu, Hsuan Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Chen, Chun-Chuan Fudan University Department of Academy of Engineering and Technology Shanghai200437 China National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan National Central University Department of Biomedical Sciences and Engineering Taoyuan320 Taiwan

Frailty is becoming a more serious issue as the population ages. Numerous studies have shown that exercise can effectively slow the development of frailty. Compared with vigorous exercise, Baduanjin (BDJ), a kind of traditional Chinese Qigong with eight simple movements, is more suitable for frailty patients. BDJ has been used to train frailty patients by physical therapists. To provide an enhanced training method, we designed a lightweight family-based frailty training system via a virtual BDJ coach. To achieve a compact system, we use a webcam as the main device. The system also supports the Kinect framework. We use pose estimation and motion recognition methods to analyze the user's movements. In addition, a novel transfer learning method is proposed. We designed a mapping model called "Skeleton Mapnet" to convert skeletal data from different frameworks. This method enables datasets from different frameworks to share classification models. It can also mix skeletal data from different frameworks to solve the lack of webcam datasets. Such a design allows the system to be easily ported into other platforms. In addition, the system is also suitable for the use of the artificial intelligence of things. Our design ensures that frailty patients can easily learn and operate the system. © 2024 The Authors.

关键词： Transfer learning

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Personalized Educational Video Evaluation Combining Student's Cognitive and Teaching Style

Personalized Educational Video Evaluation Combining Student'...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Weng, Jinta Dong, Haoyu Deng, Yifan Hu, Yue Wu, Hao Huang, Heyan School of Cyber Security University of Chinese Academy of Sciences China School of Computer Science and Cyber Engineering Guangzhou University China School of Computer Science and Technology Southeast Academy of Information Technology Beijing Institute of Technology China

ISBN: (纸本)9798350337020

AI-powered technologies, like ChatGPT and learning analytic technologies, have encouraged the sharing of online teaching resources and the transformation of teaching methods and learning pathways. However, the mixed resources and the result-oriented video evaluation repeatedly let students fall into an information trap and only appeal to students' attention to unsuitable resources. Inspired by human-computer interaction, a novel online video assessment LPSA(Linguistic- Presentative-scientific-Artistic) is proposed, integrated by cognitive style and teaching style, to realize more precise learning detection and teaching quality assessment. The LPSA evaluation consists of a four-level classification and eight secondary indexes quantified by machine learning algorithms. By automatically searching for an appropriate threshold of all secondary indexes, a real-time video assessment system is developed to certify its technical feasibility and pedagogical availability. The results show that the proposed AI-assisted assessment could realize practical pedagogical recommendations and real-time supervision. © 2023 IEEE.

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Enhancing Stockpile Management Through Deep Learning with a Focus on Demand Forecasting and Inventory Optimization

Enhancing Stockpile Management Through Deep Learning with a ...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Sheela, S. Latha, A.P. Jyothi, S. Vidyarani, H.J. Komala, M. Sharmila, N. Mallikarjunaswamy, S. Pavithra, G.S. Global Academy of Technology Department of Computer Science and Engineering Bengaluru India Dayananda Sagar College of Engineering Department of Information Science and Engineering Bengaluru India Dr. Ambedkar Institute of Technology Department of Information Science and Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Rns Insitute of Technology Dept of Computer Science and Alml Bengaluru India

ISBN: (纸本)9798350388602

Stockpile management is essential for industries to efficiently meet customer demands while minimizing costs. Traditional methods like the Economic Order Quantity (EOQ) model and ARIMA often struggle with adaptability and accuracy, leading to inefficiencies such as overstocking or stockouts. This study proposes using Long Short-Term Memory (LSTM) networks to improve demand forecasting in stockpile management. LSTMs are designed to handle sequential data, capturing complex patterns and long-term dependencies that conventional methods miss. The LSTM-based approach achieved up to a 0.30% improvement in forecasting accuracy, resulting in more precise inventory control and better alignment with actual demand. The proposed system, termed Deep Inventory Management (DIM), incorporates LSTM models that adapt to changes in demand by considering factors such as seasonality and trends. This adaptive approach allows for proactive inventory predictions, enhancing decision-making in procurement and stock levels. Key abbreviations include LSTM (Long Short-Term Memory), EOQ (Economic Order Quantity), and DIM (Deep Inventory Management). By aligning inventory more closely with demand, this deep learning approach enhances operational efficiency, reduces costs, and improves customer satisfaction, making it a valuable advancement over traditional inventory management methods. © 2024 IEEE.

关键词： Semantic Segmentation

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A Traffic-Aware Task Mapping Problem in Fog-Cloud of Things

A Traffic-Aware Task Mapping Problem in Fog-Cloud of Things

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Srichandan, Suresh Kumar Jena, Sudarson Majhi, Santosh Kumar Mishra, Kaushik Veer Suredra Sai University of Technology Department of Computer Science and Engineering Odisha Sambalpur Burla India Sambalpur University Institute of Information Technology Sambalpur Univesity Department of Computer Science and Engineering Odisha India Guru Ghasidash Vishwavidyalaya Department of Computer Science and Information Technology C. G. Birlaspur India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350379525

The Internet of Things (loT) is rapidly advancing, but its limited resources are often supplemented by integrating fog computing to address these constraints. Despite this, fog devices encounter challenges such as heterogeneity, widespread distribution, dynamic nature, and resource limitations. To tackle these issues, a robust task scheduling strategy is crucial for efficiently utilizing fog resources and enhancing Quality of Service (QoS). Moreover, tasks generated are dynamic in nature with disparate requirements such as deadline, cost, priority, etc. Fog resources could be utilized effectively with proper mapping of tasks based on ingress traffic on each node to meet the requirements. This research formulates the task mapping problem (TMP) with the goal of minimizing cost while improving QoS by reducing response times and deadline violations for loT tasks. It uses SVM (Support Vector Machine) to predict fog node traffic and categorizes nodes into peak-traffic and low-traffic groups. The Quantum-inspired PSO-based TM Algorithm (QPSO) has been implemented to map the tasks on these nodes. Simulation results reveal that the proposed method offers significant improvements, achieving up to 19.66% better response times, 30% cost reduction, and a 26% deadline violation rate i.e., an increase in meeting task deadlines compared to other approaches. © 2024 IEEE.

关键词： Cost reduction

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Online Signature Recognition using Transformer Networks: An Overview 6

Online Signature Recognition using Transformer Networks: An ...

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6th IEEE International Conference on Advances in science and technology, ICAST 2023

作者： Bharadi, Vinayak Hamdule, Zohaib Kambli, Sujan Salvi, Raj Chavan, Raj Nimbalkar, Vrishali Finolex Academy of Management and Technology Department of Information Technology Ratnagiri India Finolex Academy of Management and Technology Dept. of Computer Science and Engineering Ratnagiri India

ISBN: (纸本)9798350359817

Online signature verification remains a pivotal component in digital security, ensuring the authenticity of digital transactions and documents. This paper offers a comprehensive overview of prevailing online signature verification techniques, with a particular emphasis on methods employing Deep Learning. RNNs, with their inherent capability to process sequential data, have been instrumental in improving the accuracy and efficiency of signature verification systems. However, as the digital forgery landscape becomes increasingly sophisticated, there is a pressing need to explore advanced neural architectures. In this context, the paper introduces a transformer-based approach for signature verification. Unlike traditional models, transformers, with their self-attention mechanisms, have the potential to capture intricate signature patterns and nuances, offering a promising avenue for future research and application. © 2023 IEEE.

关键词： Recurrent neural networks

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DESI: Diversification of E-Commerce Recommendations Using Semantic Intelligence 1

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6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023

作者： Deepak, Gerard Anavkar, Harshada Vinay Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Mechanical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

Due to the dynamism in the Web 3.0 there is need for semantically driven framework for ecommerce based recommendations. This paper proposes the DESI framework, which is a query-driven, semantically oriented, Web 3.0 conforming ecommerce recommendation framework. The pre-process query was enriched using the Latent Semantic Indexing. Ontologies are generated from the ecommerce product dataset. The classification of the metadata takes place using the LSTM classifier. The relevance computation is staged and is achieved using Lin similarity, Adaptive Pointwise Mutual Information measure. The semantics oriented reasoning on the basis of semantic similarity measures yield the matching products and Frog Leap algorithm. The normalized point wise mutual information measure is used to compute the intermediate results and probability algorithm ensures optimality computation from the initial feasible solution set. An overall precision accuracy measure of so and so on with the lowest value of False Discovery Rate has been achieved with a proposed framework. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantics

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Synchronization and implementation of real-time traffic signal optimization regulator

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Multimedia Tools and Applications 2025年第12期84卷 10725-10745页

作者： Varshney, Abhilasha Dakshayini, M. Gururaj, H.L. Hu, Yu-Chen Department of Information Science and Engineering B.M.S. College of Engineering Karnataka Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung407224 Taiwan

In most cosmopolitan cities like Bengaluru, the major problems are the woes of congestion, such as increased vehicular density, travel time, stress, and vehicular carbon emissions are the major problems. These issues have led to quantifiable and non-quantifiable losses, such as high vehicle operation and maintenance costs, reduced human productivity and mobility, increased health hazards, etc. Sustainable transportation coupled with Technological interventions could alleviate these issues by efficiently managing the traffic flow. The major aims of this work are to reduce the overall commute time, traffic congestion, and air pollution, making the commutation of public stress-free and happy. So, we propose a comprehensive Real-time Traffic Signal Optimizer and Regulator (RTSOR) that identifies the right High-Density Corridors (HDC) and achieves these aims by successfully attaining the coordination among the Adaptive Traffic Signal Control System (ATSCS) fixed on the successive junctions of HDC considered in this work as a pilot initiative by upgrading the existing traffic signal in Bengaluru City to the latest ATSCS technology. Implementing this proposed RTSOR has effectively lessened the waiting time in traffic, travel time, and fuel consumption and improved air quality by reducing pollution and lowering stress levels, leading to improved quality of health and life of the public. In Bengaluru city, the current innovation would be implemented for the first time, and no prior model exists that could help optimize the travel time. The method that the study envisaged through RTSOR focuses on collecting real-time data on an HDC to elucidate our claim of originality in reducing the delay time on roads by signal synchronization. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Travel time

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AI Enhanced Phishing Detection System 3

AI Enhanced Phishing Detection System

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Chinnasamy, P. Krishnamoorthy, P. Alankruthi, K. Mohanraj, T. Kumar, B. Santhosh Chandran, Likha Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Thiruthangal Nadar College Pg Department of Computer Science Chennai51 India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Ibrahimpatnam501506 India Mlr Institute of Technology Department of Science and Humanities Hyderabad India

ISBN: (纸本)9798350361186

It attempts to create a sophisticated system that uses artificial intelligence (AI) methods to prevent and detect phishing assaults in advance. Cybersecurity is seriously threatened by phishing attempts, which take advantage of human weakness to trick people into disclosing private information. In order to precisely identify and categorize any phishing attempts, the suggested method makes use of machine learning algorithms and natural language processing (NLP) to examine a variety of characteristics of emails, websites, and digital *** project uses AI-driven methods to improve cybersecurity defenses already in place by offering a more reliable and effective way to detect and stop phishing assaults, protecting people and businesses from the negative effects of cyberattacks. © 2024 IEEE.

关键词： Denial-of-service attack

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A Hybrid Deep Learning Method for Forecasting Reservoir Water Level from Sentinel-2 Satellite Images

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computers, Materials & Continua 2025年第6期83卷 4915-4937页

作者： Hoang Thi Minh Chau Tran Thi Ngan Nguyen Long Giang Tran Manh Tuan Tran Kim Chau Graduate University of Science and Technology Vietnam Academy of Science and TechnologyHanoi100000Vietnam Faculty of Information Technology University of Economics Technology for IndustriesHanoi100000Vietnam Artificial Intelligence Research Center VNU Information Technology InstituteVietnam National UniversityHanoi100000Vietnam International School Vietnam National UniversityHanoi100000Vietnam Institute of Information Technology Vietnam Academy of Science and TechnologyHanoi100000Vietnam Faculty of Computer Science and Engineering Thuyloi UniversityHanoi100000Vietnam Faculty of Water Resources Engineering Thuyloi UniversityHanoi100000Vietnam

Global climate change,along with the rapid increase of the population,has put significant pressure on water security.A water reservoir is an effective solution for adjusting and ensuring water *** particular,the reservoir water level is an essential physical indicator for the *** the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management *** recent years,deep learning models have been widely applied to solve forecasting *** this study,we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9,ConvLSTM,and linear interpolation to predict reservoir water *** utilizes data from Sentinel-2 satellite images,generated from visible spectrum bands(Red-Blue-Green)to reconstruct true-color reservoir *** is used as the optimization algorithm with the loss function being MSE(Mean Squared Error)to evaluate the model’s error during *** implemented and validated the proposed model using Sentinel-2 satellite imagery for the An Khe reservoir in *** assess its performance,we also conducted comparative experiments with other related models,including SegNet_ConvLSTM and UNet_ConvLSTM,on the same *** model performances were validated using k-fold cross-validation and ANOVA *** experimental results demonstrate that the YOLOv9_ConvLSTM model outperforms the compared *** has been seen that the proposed approach serves as a valuable tool for reservoir water level forecasting using satellite imagery that contributes to effective water resource management.

关键词： YOLOv9 ConvLSTM reservoir water level forecasting satellite images

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