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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Neema, N. Prathibha, R.J. Sonali, N.M. Jss Science and Technology University Department of Information Science and Engineering Mysuru India Jss Science and Technology University Department of Computer Science and Engineering Mysuru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Strokes are the second leading cause of mortality and also long-term disability. A blockage in the blood flow to the brain or a broken supply line to the brain causes the sudden death of cerebrum cells;this is known as brain-stroke. There are three primary types of brain stroke viz., I) Ischemic stroke, ii) Hemorrhagic stroke and iii) Transient ischemic stroke. This paper proposes the analysis and classification of occurrence of brain stroke types using machine learning approaches. In this paper, dataset is taken from a medical institute containing 13485 records. In the pre-processing stage, duplicate records and inconsistent data are eliminated and missing data are imputed in the dataset. Later records in the dataset are classified into three types of strokes using machine learning methods. The Random Forest classifier gave the best accuracy of 95.46% with the dataset split into 80:20 ratios for training and testing data respectively. This study focuses on developing a system that helps to take precautionary measures by the patient at an earlier stage for a healthy life. © 2022 IEEE.

关键词： Classification (of information)

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Anomaly-Based Intrusion Detection system in WSN using DNN Algorithm 1

Anomaly-Based Intrusion Detection System in WSN using DNN Al...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Fuhaidi, Belal Farae, Zainab Al-Sorori, Wedad Maqtary, Naseebah Al-Ashmoery, Yahya Al-Fuhaidy, Farouk Al-Taweel, Sadik Faculty of Computing and IT University of Science and Technology Sana’a Yemen Department of IT Al-Razi University Sana’a Yemen Dept. Mathematics & Computer Faculty of Science Sana’a University Sana’a Yemen Department of Electrical Engineering Faculty of Engineering Ibb University Ibb Yemen

ISBN: (纸本)9798331533557

Intrusion detection systems (IDSs) are a necessary principle in WSN security, which can successfully prevent various hackers' and intruders' attempts to hack the network. In this research, we address the problem of achieving high accuracy in detecting intrusions in WSNs due to specific characteristics of WSN data, including the appropriate dataset, the drawbacks of feature selection, and choosing the proper algorithms for the classification process. In this paper, we proposed the anomaly-based IDS model using the DNN algorithm and mutual information (MI) technology to select features. The proposed model has been implemented using the Python language used in the Anaconda platform and relying on the standard NSL-KDD dataset. The experimental results showed the capability of the proposed model to achieve high-performance accuracy in intrusion detection using the DNN algorithm compared to the state-of-the-art. The proposed model outperforms other previous relevant works by 3.65% enhancement in terms of accuracy. © 2024 IEEE.

关键词： Network intrusion

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Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

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computer systems science & Engineering 2023年第2期44卷 961-977页

作者： E.Dhiravidachelvi M.Suresh Kumar L.D.Vijay Anand D.Pritima Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Electronics and Communication Engineering Mohamed Sathak A.J.College of EngineeringChennai603103India Department of Information Technology Sri Sairam Engineering CollegeChennai602109India Department of Robotics Engineering Karunya Institute of Technology and SciencesCoimbatore641114India Department of Mechatronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artiﬁcial Intelligence(AI)*** techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and *** wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor ***,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,***-developed Artiﬁcial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR *** this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare *** proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare *** addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the ***-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of ***,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN ***,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different *** experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.

关键词： Artiﬁcial intelligence human activity recognition deep learning deep belief network hyperparameter tuning healthcare

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IoT based Women Safety Bag

IoT based Women Safety Bag

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Kumar, M. Ashok Pradeepa, P. Muthu, A. Essaki Krishnan, R. Santhana Saravanan, K. Narayanan, K. Lakshmi SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India PSNA College of Engineering and Technology Department of Information Technology Dindigul India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India

ISBN: (纸本)9798350397208

Women's safety has become one of the chief concerns in India, since quite a large number of violence takes place against women's like sexual assault, abuse and murder. Women's are worshiped as God in this country yet her safety is at stake. Even when Women's started to equally flourish in every sectors starting from a Taxi Driver to a CEO of the top MNC, there are major safety issues which still prevent them to excel in their sector. Women's of all ages are affected by some sort of problems against their rights. It tends to increase as we let things grow without noticing them. The reports from National Crime Records Bureau (NCRB) which was released in 2021 which clearly states that more than 4, 00,000 cases were registered under crime against women. Hence, there should be a system which helps them to seek help when they are feeling unsecured. Therefore we propose a system which will assist the women to seek help when they are in danger. This system facilitates them to contact their relative when they experience unsecured situations. © 2023 IEEE.

关键词： Crime

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Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

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computer systems science & Engineering 2023年第9期46卷 3749-3765页

作者： Iyad Katib Mahmoud Ragab Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The concept of smart healthcare has seen a gradual increase with the expansion of information *** healthcare will use a new generation of information technologies,like artificial intelligence,the Internet of Things(IoT),cloud computing,and big data,to transformthe conventional medical system in an all-around way,making healthcare highly effective,more personalized,and more *** work designs a new Heap Based Optimization with Deep Quantum Neural Network(HBO-DQNN)model for decision-making in smart healthcare *** presented HBO-DQNN modelmajorly focuses on identifying and classifying healthcare *** the presented HBO-DQNN model,three stages of operations were *** normalization is applied to pre-process the input data at the initial ***,the HBO algorithm is used in the second stage to choose an optimal set of features from the healthcare *** last,the DQNN model is exploited for healthcare data classification.A series of experiments were carried out to portray the promising classifier results of the HBO-DQNN *** extensive comparative study reported the improvements of the HBO-DQNN method over other existing models with maximum accuracy of 97.05%and 95.72%under the colon cancer and lymphoma dataset.

关键词： Heap-based optimization smart healthcare decision making intelligent models artificial intelligence

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Comparison of anomaly detection algorithms for near-fall detection with video surveillance 10

Comparison of anomaly detection algorithms for near-fall det...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Tran, Khac Chinh Gassi, Meryem Nehme, Perla Rousseau, Jacqueline Nguyen, Tan Khoi Meunier, Jean Department of Computer Science School of Rehabilitation University of Montreal Montréal Canada Information Technology Faculty Danang University of Science and Technology The University of Danang Danang Viet Nam

ISBN: (纸本)9781665485579

Falls are a major problem for seniors living in the community. To prevent their negative consequences, we propose a video surveillance system to detect early signs of mobility decline based on the frequency of near-fall occurrences (e.g., stumble, slip, misstep, loss of balance) for more efficient intervention before a fall occurs. Velocity features were extracted from a simplified skeleton fitted to the body based on the keypoint RCNN (Region-based Convolutional Neural Network) algorithm. Then, three anomaly detection algorithms (One-Class SVM, Gaussian Mixture Model (GMM) and Isolation Forest (IF)) were tested to identify abnormal events in a dataset of 55 videos (> 16000 frames) of simulated normal and abnormal activities in a realistic apartment-laboratory. Our experimental results showed that all three algorithms could successfully distinguish anomalies (near-falls) with an EER (Equal Error Rate) of 10% (AUC = 87%) for the One-Class SVM, EER = 20% (AUC = 85%) for the GMM, and EER = 10% (AUC = 92%) for the Isolation Forest. This study demonstrates the potential of a smart video surveillance system to identify near-falls associated with mobility decline for the benefit of older adults living alone at home. © 2022 IEEE.

关键词： Fall detection

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Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

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Automated Scoring Model for Semi-Open-Ended Question in Scientific Explanation

Automated Scoring Model for Semi-Open-Ended Question in Scie...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Teevit Lertchaturaporn Pokpong Songmuang Department of Computer Science Faculty of Science and Technology Thammasat University Thailand

ISBN: (数字)9798350370096

ISBN: (纸本)9798350370102

Scientific explanation is a crucial skill for analyzing data and drawing reasonable conclusions, especially in the context of semi-open-ended and open-ended questions. However, evaluating such questions requires significant effort from teachers, leading to a labor-intensive grading process influenced by class size and teacher expertise, potentially compromising the question's reliability. Consequently, students may lack valuable feedback, hindering the effective development of their skills. This paper presents our research focus on designing a system to automatically score students' responses. We leverage natural language processing techniques and machine learning algorithms to construct an automatic assessment model for scientific explanations in semi-open-ended questions. The responses were obtained from eighth-grade students in the fundamental science subject of the Thai curriculum, totaling 100 responses. Initially, Thai natural language processing is employed to preprocess the responses. Subsequently, N-Gram and word embedding techniques are utilized to represent the responses in a trainable format. Finally, the performance of various machine learning models trained with different word representation techniques is reported using F1-score. The experimental results demonstrate that a common method of word representation, combined with a simple machine learning model, can achieve high performance.

关键词： Training Vocabulary Technological innovation Machine learning algorithms Semantics Machine learning Writing

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Soft Set Based Clustering and Its Comparison on Categorical Data 9

Soft Set Based Clustering and Its Comparison on Categorical ...

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9th IEEE information technology International Seminar, ITIS 2023

作者： Riyadi Yanto, Iwan Tri Waishiang, Cheah Hidayat, Rahmat Wahyudi, Rofiul Suprihatin Apriani, Ani Universitas Ahmad Dahlan Information System Department Yogyakarta Indonesia Universiti Malaysia Sarawak Faculty of Computer Science & IT Samarahan Sarawak Malaysia Politeknik Negeri Padang Information Technology Department Padang Indonesia Universitas Ahmad Dahlan Islamic Banking Department Yogyakarta Indonesia Institute Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9798350306835

Categorical data clustering is problematic since it is difficult or complex to determine how comparable the data is. Several methods, most recently centroid-based strategies, have been developed to reduce the complexity of the similarity of categorical data. These methods nevertheless result in lengthy processing durations. Another method, soft set-based clustering (SSC), based on the probability function of multivariate multinomial distributions, is suggested in this article. Soft sets are used to represent the data, and each soft set has a probability for each object. The joint cluster distribution function determines the probability for each object after the multivariate multinomial distribution function. The connected cluster would receive the highest likelihood. Benchmark data sets from UCI machine learning are used to compare the performance of the approach to the baseline techniques. The outcomes demonstrate that the suggested strategy performed better in purity, rank index, and calculation time. © 2023 IEEE.

关键词： Distribution functions

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Mobility-Aware Power Control and User Scheduling for Downlink V2I Networks

Mobility-Aware Power Control and User Scheduling for Downlin...

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IEEE Conference on Vehicular technology (VTC)

作者： Yujie Wang Momiao Zhou Yanshi Sun Kan Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Faculty of Computer Science and Engineering Xi’an University of Technology Xi’an China

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Vehicle-to-infrastructure (V2I) network is a new paradigm of wireless system with special topology where roadside units (RSUs) are linearly deployed along the roadside and vehicles linearly move on the road. For such system, some classical problems would have new formulations and solutions. We in this paper investigate the joint power control and user scheduling problem for a multi-cell downlink V2I network, the objective of which is to maximize the sum-rate of the network under the signal-to-interference-plus-noise ratio (SINR) constraint of each V2I link. Considering the high mobility of vehicles, the objective function is set as the mean of the sum-rate over a sequence of time slots. For ease of handling, we first decouple the problem into multiple separate subproblems based on the linear distribution of RSUs. Then we employ the quadratic transform technique for fractional programming (FP) to transform the mixed integer nonlinear programming (MINLP) subproblems into convex problems, and obtain the solutions with Branch and Bound method. Finally the validity of our proposed algorithm is verified by numerical simulations.

关键词： Wireless communication Vehicular and wireless technologies Vehicle-to-infrastructure Power control Transforms Interference Programming Downlink Optimization Signal to noise ratio

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