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A Voice Assistive Mobile Application Tool to Detect Cardiovascular Disease Using Machine Learning Approach

Biomedical Materials and Devices 2024年第2期2卷 1246-1257页

作者： Uddin, Khandaker Mohammad Mohi Dey, Samrat Kumar Babu, Hafiz Md. Hasan Department of Computer Science and Engineering Southeast University School of Science and Technology Bangladesh Open University Department of Computer Science and Engineering University of Dhaka

Many people all around the world suffer from heart disease, which is regarded as a severe illness. In healthcare, especially cardiology, it is crucial to accurately and quickly diagnose cardiac problems. In this research, we proposed an accurate and efficient mobile application-based system for diagnosing cardiac disease based on machine learning approaches. The developed mobile application is voice assistive, which makes the proposed application more user-friendly. Numerous machine learning methods have been examined in this study to predict and diagnose cardiovascular disease (CVD). A detailed comparative study was also drawn using eighteen (18) classification algorithms such as Support Vector Machines, Logistic Regression, Linear SVC, K-Nearest Neighbors, Naive Bayes, Stochastic Gradient Descent, Gradient Boosting, Ridge, Bagging, Random Forest, Decision Tree, XGB, LGBM, Extra Trees Perceptron, and Voting Classifier (hard or soft voting). Sixty-eight thousand nine hundred seventy-five (68,975) samples from various patients were used to test the performance of each approach. According to the study, Random Forest and Decision Trees have the best accuracy levels at 99.9%. However, the Support Vector Machine classifier had the lowest performance, barely achieving 60% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2024.

关键词： Cardiovascular heart disease Detection Healthcare informatics Machine learning Mobile application

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Detection and Categorisation of Brain Tumours using Hybrid AI Deep Learning Methods 6

Detection and Categorisation of Brain Tumours using Hybrid A...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Appavu, Narenthirakumar Department of Computer Science and Engineering Saveeths School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798331522667

It is crucial to accurately identify the different kinds of brain tumours in order to diagnose them quickly and maybe save lives. Magnetic resonance imaging (MRI) produces non-invasive, high contrast, greyscale images of the brain and is mostly used to diagnose cancer. Deep learning has completely changed how medical imaging is interpreted, leading to major breakthroughs since the introduction of convolutional neural networks (CNN) in diagnostic systems. We use a fine-tuning technique based on transfer learning (TL) in conjunction with ResNets to categorize brain malignancies into three groups: pituitary, glioma, and meningioma. The freely available CE-MRI (Figshare) dataset is used to refine CNN's five pre-trained models from the ResNets family, which includes ResNet18 through ResNet50. In our method, we improve the pre-trained ResNet model in two steps. To initialize the model, weights taken from the ImageNet dataset are employed. Additional layers, such as top layers and a fully connected layer (FC), are then added to facilitate tumour classification. To evaluate our improved ResNets' resilience to other pre-trained models, we run a number of experiments. We also examine how the accuracy of the model's tests is affected by data augmentation. We use Grad-CAM visualization to analyze the attention maps produced by the optimum model, which effectively displays the positions of tumours in brain pictures, in order to comprehend the model's decision-making process. Our results demonstrate considerable speed advantages when using ResNet34 as the underlying framework. In particular, the F1-score, recall, accuracy, and precision for the entire test were determined to be 97.61%, 97.45%, 98.74%, and 97.48%, respectively. © 2025 IEEE.

关键词： Convolutional neural networks

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Real-Time Violence Recognition in CCTV Video Surveillance Enhanced by AI Deep Technique 6

Real-Time Violence Recognition in CCTV Video Surveillance En...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Appavu, Narenthirakumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798331522667

Despite its many uses, deep learning (DL) is a key tool in the machine learning (ML) community’s toolbox. Numerous techniques have been developed to identify violence and criminal activity on video since many crimes take place in public areas that are not adequately monitored. The significance of automatic violence detection in surveillance video studies has grown. They do, however, have several drawbacks and frequently rely on particular circumstances. This article presents a method for detecting aggression in surveillance films that is based on convolutional neural networks (CNNs). Accuracy is increased by applying both deep learning and machine learning methods as advised. Performance assessments showed how effectively the suggested approach identifies violent content in video. Test results show that the future solution works better than current approaches for classifying violent then criminal material in films. The models that were previously trained InceptionV3, ResNetV2, Inception_ResNetV2, Violence_Net, and the suggested vioNet remained trained on four datasets. The following were the classification outcomes of the endorsement data for each model: VioNet model 99.72%, InceptionResNetV2 90%, ViolenceNet pseudo-97, and InceptionV3 92%. Due to its easier integration of feature maps, the Dense_Net prototypical was selected for our application arrangement instead of the more intricate Inception or InceptionResNet models. Compared to earlier iterations, its sturdy construction provides greater accuracy and performance with fewer filters and settings. © 2025 IEEE.

关键词： Convolutional neural networks

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OSSIoT: An ontology-based Operational Security model for Social Internet of Things using Machine Learning Techniques

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第10期51卷 1440-1453页

作者： Kumar, K.S. Santhosh Hanumanthappa, J. Prakash, S.P. Shiva Krinkin, Kirill Department of Studies in Computer Science University of Mysore Karnataka Mysuru India Department of Information Science and Engineering JSS Science and Technology University Karnataka Mysuru India School of Computer Science and Engineering Constructor University Bremen Germany

The Social Internet of Things (SIoT) is an innovative fusion of IoT and smart devices that enable them to establish dynamic relationships. Securing sensitive data in a smart environment requires a model to determine the relationships between devices through object profiling and ontological models. To address this need, we have proposed an ontology-based operational security model for the SIoT. In our approach, the interpolation method is used to establish relationships, while fiduciary relationships are employed to detect threats. Furthermore, the encryption of heterogeneous device data, coupled with the implementation of an operation-based intrusion detection system, proves highly effective in identifying potential *** relationships are identified as intruders and validated using machine learning techniques. Encrypting heterogeneous device data, along with an operation-based intrusion detection system, efficiently identifies threats in the ever-evolving dynamic nature of the SIoT environment. the encryption of heterogeneous device data, coupled with an operation-based intrusion detection system, effectively identifies threats. Invalid relationships are promptly identified as attackers and machine learning techniques are used to validate relationships encryption and machine learning stand as indispensable tools in the endeavor to secure sensitive information within the realm of the SIoT. The suggested model outperformed the results of the current model, as evidenced by its average accuracy of 85.67%, precision of 90.37%, recall of 92.06%, and F1-score of 91.03% when compared to the existing model. © (2024), (International Association of Engineers). All rights reserved.

关键词： Network intrusion

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Synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations 3rd

Synergizing Collaborative and Content-Based Filtering for En...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Walia, Madhav Raj, Shivanshu Aishwary, M. Lakshmi, T. Jaya Department of Computer Science and Engineering School of Engineering and Applied Sciences SRM University Andhra Pradesh Guntur India

ISBN: (纸本)9789819743582

This study combined content-based and collaborative filtering algorithms to create an all-inclusive movie recommendation system. In order to find trends and provide suggestions based on the tastes of comparable users, collaborative filtering analyzes user–item interaction data. Movie qualities are evaluated using content-based filtering in order to suggest related products. Preprocessing techniques like data cleansing, filtering, and text processing are used in the implementation to extract pertinent information and textual elements. While the textual characteristics are converted into numerical representations using methods like literal_eval and CountVectorizer, the metadata contains information on genres, release dates, cast, crew, and keywords. Using the vectorized characteristics of two videos, the cosine similarity between them is computed. Techniques for collaborative and content-based filtering are used to deliver accurate and customized. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling Intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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Forecasting electricity demand in Türkiye: A comprehensive review of methods, determinants, and policy implications

Journal of Energy Systems

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Journal of Energy Systems 2025年第1期9卷 132-158页

作者： Elbaş, Hakan Bilgin, Turgay Tugay Bursa Technical University Graduate School Department of Intelligent Systems Engineering Bursa Turkey Bursa Technical University Faculty of Engineering and Natural Sciences Department of Computer Engineering Bursa Turkey

This review examines the methods, determinants, and forecasting horizons used in electricity demand forecasting in Türkiye. The study investigates how Türkiye's electricity demand is influenced by economic, climatic, socio-demographic, and technological factors, and explores the evolving landscape of forecasting techniques, from traditional statistical models to advanced machine learning and hybrid approaches. The research addresses three key questions: The significant determinants of electricity demand in Türkiye, the most effective forecasting methods, and the application of these insights to improve energy planning and policy development. Through a systematic analysis of peer-reviewed literature, official reports, and case studies, the study reveals the complex interplay of factors affecting electricity demand and the increasing sophistication of forecasting methodologies. Economic growth, industrial production, climate change, urbanization, and technological advancements emerge as primary drivers of demand, while artificial neural networks and hybrid models demonstrate superior forecasting capabilities. The study highlights the importance of integrated modeling approaches, sector-specific strategies, and the incorporation of climate projections in long-term planning. It also emphasizes the need for aligning energy policies with broader economic and environmental objectives. This review provides valuable insights for researchers, policymakers, and industry stakeholders, offering a comprehensive framework for understanding and improving electricity demand forecasting. © 2025 Erol Kurt. All rights reserved.

关键词： Demand response

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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PS-CoT-Adapter: adapting plan-and-solve chain-of-thought for scienceQA

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science China(Information sciences) 2025年第1期68卷 392-393页

作者： Qun LI Haixin SUN Fu XIAO Yiming WANG Xinping GAO Bir BHANU School of Computer Science Nanjing University of Posts and Telecommunications Purple Mountain Laboratories Department of Electrical and Computer Engineering University of California at Riverside

Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1-3]has investigated chain-of-thought (CoT) reasoning in complex multimodal scenarios,such as science question answering (scienceQA) tasks [4],by fine-tuning multimodal models through human-annotated CoT ***,collected CoT rationales often miss the necessary rea-soning steps and specific expertise.

关键词： chain adapting plan-and-solve ps-cot-adapter scienceqa thought

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