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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Rajasekar, P. Bhosale, Rupali Shekar Indhumathi, C. Sandeep, K.V. Prasanthi Kumari, N. Rajendiran, M. School of Computing Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Kattankulathur603203 India Bharati Vidyapeeth Institute of Technology Department of Electronics And Telecommunication Engineering Maharashtra Navi Mumbai400614 India Rmk College of Engineering And Technology Department of Computer Science And Business Systems Tamil Nadu Puduvoyal601206 India Dayananda Sagar College of Engineering Department of Electronics & Telecommunication Engineering Karnataka Bengaluru560111 India School of Computer Sciences University of Petroleum And Energy Studies Uttarakhand 248007 Dehradun India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

New IoT devices generate massive real-time data. Fast, real-time stream processing is needed for IoT data insight extraction. There are pros and downsides to real-time stream processing for the Internet of Things (IoT). This article analyzes present methods and technologies. The inquiry begins with large-scale, real-time IoT data analysis. Real-time analytics and bulk processing limits are important for IoT decision-making. We then investigate IoT stream processing frameworks, methodologies, and systems' complexity. We explore Internet of Things technology selection and tradeoffs between scalability, accuracy, latency, and real-time processing system scalability. It covers real-time stream processing system design and deployment and risk mitigation. Case studies and use cases highlight real-time IoT stream processing. Healthcare, smart city, and industrial IoT operational efficiency and proactive decision-making improve with real-time data. Finally, the report suggests the Internet of Things real-time stream processing research. New IoT data analytics protocols, methods, and technologies are highlighted. We teach real-time stream processing to IoT academics, practitioners, and decision-makers. They could then develop more efficient and scalable IoT data stream optimization solutions. © 2024 IEEE.

关键词： Edge computing

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Interpretable Machine Learning Model for Breast Cancer Prediction Using LIME and SHAP 9

Interpretable Machine Learning Model for Breast Cancer Predi...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Maheswari, B Uma Aaditi, A. Avvaru, Ananya Tandon, Aryan De Prado, R. Pérez Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India University of Jaén Dept. Telecommunication Engineering Jaén23700 Spain

ISBN: (纸本)9798350394474

Breast cancer, which occurs in both men and women, causes approximately 10 lakh deaths globally and has no specific risk factors. The time frame of the treatment is a long-drawn process based on the person, the type of cancer, and its level of spread. It is imperative to detect this cancer early on in order to prevent mortality. Given the prediction's significance, an accurate breast cancer prediction model must be developed. This study explores the Breast Cancer Prediction dataset, applies SMOTE (Synthetic Minority Over-sampling Technique) to balance the dataset, and proposes an effective Machine Learning (ML) model fused with Explainable AI to provide health professionals with explanations. ML algorithms are analyzed before and after applying Principal Component Analysis (PCA), and visualization is performed using t-SNE. ML algorithms, Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Random Forest (RF), Stochastic gradient Descent, XGBoost, Gradient Boosting, Decision Tree (DT), and Naïve Bayes are trained on the Breast Cancer dataset. It is seen that the RF model outperforms other models considered with 95.9% accuracy. To understand the weightage of features considered by the best breast cancer prediction models and to provide trust to the doctors, the Explainable AI (XAI) packages LIME (Local Interpretable Model-agnostic Explanations), and SHAP (SHapley Additive exPlanations) are used. The effective ML model with XAI techniques empowers clinicians with actionable insights for more informed breast cancer diagnosis and treatment decision-making. © 2024 IEEE.

关键词： Principal component analysis

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Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

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computer Systems science & engineering 2023年第8期46卷 1845-1861页

作者： K.Kavin Kumar P.M.Dinesh P.Rayavel L.Vijayaraja R.Dhanasekar Rupa Kesavan Kannadasan Raju Arfat Ahmad Khan Chitapong Wechtaisong Mohd Anul Haq Zamil S.Alzamil Ahmed Alhussen Department of Electronics and Communication Engineering Kongu Engineering College638060Tamil NaduIndia Department of Electronics and Communication Engineering Sona College of Technology636005Tamil NaduIndia Department of Computer Science and Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhonKaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be *** India,around 15 million cases are diagnosed *** mitigate the seriousness of the tumor it is essential to diagnose at the ***,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor ***,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and ***,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit *** computer models adapted to diagnose the tumor,but the accuracy of the model needs to be *** all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception *** augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and *** accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th *** the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

关键词： AlexNet brain tumor data augmentation inception V3 ResNet 50

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A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

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computers, Materials & Continua 2024年第4期79卷 1631-1657页

作者： Maria Gul Imran Ali Khan Gohar Zaman Atta Rahman Jamaluddin Mir Sardar Asad Ali Biabani May IssaAldossary Mustafa Youldash Ashraf Saadeldeen Maqsood Mahmud Asiya Abdus Salam Dania Alkhulaifi Abdullah AlTurkey31Department of Computer Science COMSATS University IslamabadAbbottabad CampusAbbottabad22010Pakistan Department of Computer Science Abbottabad University of Science and TechnologyAbbottabad22010Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Science and Technology Unit Umm Al-Qura UniversityMakkah21955Saudi Arabia Deanship of Scientific Research Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyOmdurman Islamic UniversityOmdurman14415Sudan Business Analytic Program Department of Management and MarketingCollege of Business AdministrationUniversity of BahrainSakhir32038Bahrain

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route *** proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

关键词： Safe crowd evacuation public safety emergency transition probability cooperation

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Fog-Assisted Smart Healthcare Prediction System for Diabetics Patients 1

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Emergent Converging Technologies and Biomedical Systems Select Proceedings of the 2nd International Conference, ETBS 2022

作者： Sekhar Tripathy, Subhranshu Bhusan Panda, Shashi Pati, Abhilash Rath, Mamata Tripathy, Niva Sahu, Premananda Odisha Bhubaneswar India Department of Electronics and Telecommunication Engineering Raajdhani Engineering College Odisha Bhubaneswar India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan University Odisha Bhubaneswar India Department of Computer Science and Engineering DRIEMS Autonomous College Odisha Cuttack India Department of Computer Science and Engineering SRMIST Delhi-NCR Campus UP Ghaziabad India

ISBN: (数字)9789819922710

ISBN: (纸本)9789819922703

Accurately, forecasting human diseases continues to be a challenging issue in the search for better and more crucial studies. Diabetic multifunctional sickness is a potentially dangerous condition that afflicts people all around the entire globe. Among the important parts of the human body it affects are the heart, neuropathy, retinopathy, and nephropathy. A healthcare monitoring recommendation system accurately detects and also suggests diabetes complications using adaptive machine learning methods and data fusion techniques on medical management datasets. Recently, various machine learning approaches and algorithms for predicting diabetic illness have been created. Even so, a lot of systems are unable to handle the massive volume of multi-feature raw data on the diabetes mellitus condition. An intelligent diabetic health management strategy is created using deep machine learning and data fusion technologies. By combining the data, we can reduce the unnecessary burden on organization computing power and increase the effectiveness of the suggested method, allowing us to predict and advise this life-threatening condition more accurately. Finally, the adaptive prediction system is assessed, and a machine learning model for diabetes diagnosis that is effective and up-to-date is trained. The suggested approach attained the maximum level of accuracy against standard advanced classification algorithms. The strategy we provide is therefore more effective in predicting and advising multimodal diabetic illness. Our proposed system’s improved disease diagnosis performance warrants its inclusion in automated diabetes patient diagnostic and recommendation systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Modified Artificial Hummingbird Algorithm with Deep Learning for Pest Detection through Leveraging Pattern Recognition-based Fusion Approach 7

Modified Artificial Hummingbird Algorithm with Deep Learning...

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

作者： Rubadevi, B. Suresh, Helina Rajini Abdullah, Ibrahim Choudhary, Prashant Kumar Choudhary, Minu Mathurkar, Piyush Arunai Engineering College Department of Computer Science and Engineering Tamilnadu Tiruvannamalai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Al-Turath University Computer Science Department Baghdad Iraq Lovely Professional University Department of Electronics and Communication Engineering Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Vishwakarma Institute of Information Technology Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350372816

The limitations of manual inspection and simple algorithms highlight the need for advanced insect detection systems in agriculture. The study presents the Modified Artificial Hummingbird Algorithm with Deep Learning (MAHADL), a significant breakthrough in pest detection. MAHADL combines the speed and efficacy of the Artificial Hummingbird Algorithm (AHA) with deep learning models such as Recurrent and Convolutional Neural Networks. Combining pattern recognition with the fusion approach improves pest detection accuracy while reducing false positives and negatives. Accurate pest identification is essential for effective agricultural management, which MAHADL provides through data collection, preprocessing, and algorithmic integration. MAHADL outperforms existing systems in terms of accuracy (0.94), recall (0.95), precision (0.92), computational economy (12 hours of training time, 0.5 seconds/image inference time), and decrease in false positives and negatives. The innovative technique provides enhanced agricultural output and sustainability, representing a significant leap in intelligent pest management. © 2024 IEEE.

关键词： Convolutional neural networks

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Investigating Object Sorting Problem for Smart Garbage Application 5

Investigating Object Sorting Problem for Smart Garbage Appli...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Ma, Wing Shiu Yuen, Shing Hang Keung, Chi Nam Qureshi, Mehak Fatima Qureshi, Umair Mujtaba Umair, Zuneera The Hong Kong Polytechnic University School of Professional Education and Executive Development Hong Kong Mehran University of Engineering and Technology Department of Telecommunication Engineering Sindh Pakistan Chinese University of Hong Kong Kowloon Department of Computer Science & Engineering Hong Kong The Hong Kong Baptist University Department of Computer Science Hong Kong

ISBN: (纸本)9798350394764

In recent years, the rapid growth in industrialization is observed to promote the economic growth of nations. This indeed has raised the financial standards and ways of living from sustainability to a well-off living standards, but has counter effects on environmental conditions such as increasing waste generation, pollution etc, which has become a serious concern and can be alarming situation in nearby future. Thus, this research aims to create a sustainable solution to reduce the recycling material using the advance AI deep learning technology. In this research, we have designed and developed a smart recycling garbage bin that can effectively sort different types of recycling waste i.e. glass, paper, metal and plastic. To develop the smart garbage bin, the pre-trained Tensorflow model is utilized, after evaluating its performance and comparative analyses with YOLO model on the customize dataset. The final results shows that the model classified all the garbage images from the garbage used to test the smart garbage bin and achieved the high overall accuracy of 91%. © 2024 IEEE.

关键词： Recycling

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Factors Affecting the Willingness to Share Data in Mobile Health (mHealth) Apps in Indonesia 25

Factors Affecting the Willingness to Share Data in Mobile He...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Lestari, Dian Rahayu, Anita Adi, Puput Dani Prasetyo Puspitasari, Chasandra Persada, Satria Fadil Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Medicine Surabaya60132 Indonesia Research Center for Telecommunication Bandung Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta Indonesia

ISBN: (纸本)9798350378573

This study investigates the factors influencing user willingness to share data in mobile health (m-health) applications in Indonesia, integrating the Technology Acceptance Model, Theory of Planned Behavior, and Privacy Calculus Theory through Structural Equation Modeling (SEM). Data were collected via an online questionnaire from 103 Indonesian active users of m-health platforms, aged 15-55, focusing on applications that provide online consultations and health-related information. The SEM analysis, validated by indices including the Goodness of Fit Index and Root Mean Square Error of Approximation, reveals a strong model fit. The findings highlight that perceived benefits, such as improved health outcomes and personalized advice, are the primary drivers for users' willingness to share data, significantly outweighing concerns over privacy risks. Perceived usefulness and trust in the application also significantly affect data-sharing behavior, while ease of use and social influences have a moderate to minor influence, respectively. These insights are critical for application developers and policymakers in enhancing app utility and user engagement while addressing privacy concerns. The study's limitations include its demographic scope and reliance on self-administered questionnaires, pointing to directions for future research to broaden demographic representation and deepen qualitative insights. © 2024 IEEE.

关键词： data sharing mHealth privacy concerns SEM analysis structural equation modeling

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Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm

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IEEE Journal of Biomedical and Health Informatics 2024年第3期28卷 1173-1184页

作者： Ahmad, Nehal Lai, Kuan-Ting Tanveer, M. Indian Institute of Technology Indore Simrol Department of Mathematics Indore453552 India National Taipei University of Technology Department of International Program of Electrical Engineering and Computer Science Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the 'STrutred Analysis of the REtina (STARE) Database' accessed on Oct. 2020 (https://***/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm. © 2013 IEEE.

关键词： Blood vessels

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Drone-Based Predictive Structure Analysis: A Review

Drone-Based Predictive Structure Analysis: A Review

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Patil, Trushant Nandan, Durgesh Sharma, Kanhaiya Parashar, Deepak Department of Robotics and Automation Pune India Department of Electronics and Telecommunication Engg. Pune India Department of Computer Science & Engineering Pune India Faculty of Engineering Pune India

ISBN: (纸本)9798350329186

Drone-based predictive structure analysis is a rapidly growing field that has the potential to revolutionize infrastructure and building inspection. In this review of the research, we give a general overview of how drones could increase the effectiveness and precision of structural analysis, particularly for keeping an eye on difficult-to-reach locations. The usage of drones for 3D mapping, inspection, and monitoring of diverse constructions, such as concrete buildings, bridges, tunnels, and dams. The investigations show that drone-based inspection can produce 3D models and high-resolution photos, allowing for remote monitoring and the early identification of possible problems. The findings show that drones may greatly boost infrastructure management's safety and effectiveness, making them an important tool for predictive structural research. © 2023 IEEE.

关键词： Drones

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