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Alzheimer’s Disease Classification via Pre-trained Convolutional Neural Network Variants Utilizing Multi-level Thresholding based on Kapur’s Entropy

Alzheimer’s Disease Classification via Pre-trained Convolut...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： Nivedhitha Mahendran P M Durai Raj Vincent Department of Networking and Communications School of Computing Technologies SRM Institute of Science and Technology Chennai Kattankulathur India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Neurological diseases are the disorders of brain and the nerves that run throughout the human body. There is a range of symptoms that arise as a result of chemical, structural, or electrical modifications in the regions of brain. One such neurological disease is the Alzheimer’s Disease (AD), is considered as a slow and progressive disease that majorly affects the cognitive abilities of the individual. It is most common forms of dementia without any proper cure. However, early diagnosis and treatment might slow down the symptoms and improve the lifestyle of the individual. The most common techniques to identify the neurological disorders are imaging of the brain regions, namely, CT, MRI, PET, etc. The learning approaches, such as Deep Learning and Machine Learning are identified to be effective in analyzing these images to provide better accuracies in detecting the AD. In this study, four versions of Convolutional Neural Networks (CNN) are used to classify AD and Control cases. Further, the Kapur’s thresholding technique is also implemented to segment the images for improving the classification accuracy of AD. From the results shown in the study, the inference shows that the VGG16 of CNN provides considerable improvement than the other deep learning models before and after the implementation of thresholding.

关键词： Deep learning Neurological diseases Image segmentation Accuracy Thresholding (Imaging) Magnetic resonance imaging Computed tomography Convolutional neural networks Alzheimer's disease Information technology

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Securing Chemical Processing Plants using Isolation Forest Algorithm and IoT Sensors for Leakage Prevention 7

Securing Chemical Processing Plants using Isolation Forest A...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Thanigaivelu, P.S. Ranganathan, Chitra Sabapathy Priya, S. Asha, R.M. Murugan, S. Ganeshbabu, T.R. Srm Institute of Science And Technology Department of Computing Technologies Tamil Nadu Chennai India Mphasis Corporation ChandlerAZ United States Deemed To Be University Department of Electrical And Electronics Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Civil Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India Department of Electronics And Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350359299

Chemical processing plants are essential to many different sectors of the economy, but they also pose risks to people and the environment in the event of accidents like chemical spills. Using the Isolation Forest algorithm in conjunction with IoT sensors for leakage prevention, this study investigates a new way to improve chemical processing facility security. It uses the Isolation Forest method, which is well-known for its anomaly detection capabilities, to spot suspicious patterns that might indicate leaks in real-time. This technology, when combined with an Internet of Things (IoT) sensor network strategically installed across the facility, offers a thorough monitoring solution that can identify and prevent leaks before they become serious. It highlights the flexibility of the Isolation Forest algorithm by discussing its application to chemical processing facilities. The IoT sensors may be integrated, highlighting how user can improve the security architecture as a whole via data collecting, communication, and coordination. Quick detection and prevention of chemical leaks is validated by results from simulation studies or real-world deployments of the proposed system. To discuss potential challenges, such the scalability of the system and the dependability of the sensors, and offers methods to make sure the security architecture is strong. Measures to evaluate the system's efficiency, include reaction time, isolation score, and detection accuracy. The potential issues to be addressed are system's flexibility and the sensors' accuracy to provide solutions to enhance the proposed security architecture. © 2024 IEEE.

关键词： Anomaly detection

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Steel Surface Defect Detection Based on Improved MASK RCNN 8

Steel Surface Defect Detection Based on Improved MASK RCNN

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhang, Chenghong Yu, Bo Wang, Wei Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665450515

The defect detection of steel is an important process to ensure the quality of steel. The traditional detection methods have low efficiency and poor accuracy. With the development of deep learning and computer vision technologies, this paper proposes an improved Mask RCNN model for steel defect detection. The feature extraction network of Mask RCNN is replaced by a more robust EfficientNet, the improved BiFPN structure is combined with EfficientNet to extract features of different scales, and a CBAM module is added to the mask branch to improve the quality of mask prediction. Experiments on the Severstal steel surface defect dataset show that the improved method not only significantly improves the accuracy of the model, but also greatly reduces the model parameters. © 2022 IEEE.

关键词： Surface defects

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An Environment Control Management System for Container-Type Energy Storage System 6

An Environment Control Management System for Container-Type ...

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Yang, Chu-Sing Tsai, Han-Sheng Lee, Shih-Hsiung National Cheng Kung University Miin Wu School of Computing Tainan Taiwan National Cheng Kung University Department of Electrical Engineering Tainan Taiwan National Kaohsiung University of Science and Technology Department of Intelligent Commerce Kaohsiung Taiwan

ISBN: (纸本)9798350323535

The most widely used energy storage system in current industrial applications and commercialization is Battery Energy Storage System (BESS). Due to its fast response capability, BESS has been accepted as an energy storage system worldwide. However, there are still high risks associated with large-scale BESS installations. System malfunctions can lead to battery damage, and inadequate environmental control of BESS operation can result in incidents such as battery leakage, flooding, or prolonged exposure to high temperatures. To prevent disasters and proactively prepare for them, we proposed the planning and design of an Environmental Control System (ECS) for BESS. The ECS adopted sensors to monitor the BESS site and the batteries, preparing for potential disasters. It continuously collected environmental monitoring data and was integrated with a Battery Management and Monitoring System (MBMS) to protect the internal batteries and prevent the spread of disasters. The experiment was conducted in an actual BESS installation, and Modbus communication protocol was used for the communication of sensors and the management system. The experimental results demonstrated that this planned ECS maintained the temperature within the working range for the batteries inside the container, and the relevant components designed for the system effectively interacted with the MBMS to protect the energy storage system. © 2023 IEEE.

关键词： Battery storage

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GUEST EDITORIAL

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China Communications 2023年第2期 3-6页

作者： Yuan Wu Xu Chen Feng Lyu Xianfu Chen Xumin Huang Jie Gao Yueyue Dai Tony Q.S.Quek Yan Zhang the State Key Laboratory of Internet of Things for Smart City University of Macau the Department of Computer and Information Science University of Macau Sun Yat-sen University Sun Yat-sen University Institute of Advanced Networking and Computing Systems Sun Yat-sen University National and Local Joint Engineering Laboratory the School of Computer Science and Engineering Central South University the VTT Technical Research Centre of Finland the School of Automation Guangdong University of Technology the School of Information Technology Carleton University Huazhong University of Science and Technology Singapore University of Technology and Design(SUTD) the IEEE Communications Society Technical Committee on Green Communications and Computing(TCGCC)

With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and l...

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Enhanced Human Segmentation from 2D LiDAR Data Using DBSCAN Clustering

Enhanced Human Segmentation from 2D LiDAR Data Using DBSCAN ...

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Electrical and Information technology (IEIT), International Conference on

作者： Muhammad Ainul Fikri Moch. Iskandar Riansyah Farah Zakiyah Rahmanti Department of Information Technology School of Computing Telkom University Surabaya Indonesia Departement of Elecetrical Enginering School of Electrical Engineering Telkom University & Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Information Technology School of Computing Telkom University & Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331516888

ISBN: (纸本)9798331516895

This study presents a comprehensive approach to segmenting human objects in point cloud data generated by 2D LiDAR sensors within indoor environments, where challenges such as shape variability, noise, and the presence of non-human objects complicate the process. The proposed method leverages the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, which is well-suited for clustering data points based on density without requiring a predefined number of clusters. The experimental results identified three distinct clusters: Cluster 0 exhibited a regular spread of data points, Cluster 1 featured concentrated mean_x and mean_y values, and Cluster 2 displayed a higher mean_y with a larger standard deviation, reflecting a more dispersed cluster. The DBSCAN algorithm demonstrated its effectiveness with a silhouette score of 0.700 and a Davies-Bouldin index of 0.439, outperforming traditional methods like k-means in terms of clustering accuracy. Human objects were accurately identified, with objects 1 and 3 correctly clustered, while object 2, although a round chair, was grouped with human objects due to feature similarity. These findings underscore the method's potential for improving indoor environment monitoring and autonomous navigation systems, where precise segmentation of human objects is crucial

关键词： Laser radar Accuracy Shape Heuristic algorithms Noise Dynamics Clustering algorithms Indoor environment Object recognition Standards

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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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Computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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Enhanced performance optimization of Network Coding for Enhancing Security and Performance in Wireless Networks

Enhanced performance optimization of Network Coding for Enha...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Sangeetha, R. Logeshwaran, J. Vats, Satvik Vishwakarma, Pramod Srivastava, Santosh Kumar Department of CSE Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of ECE Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Computer Science & Engineering Graphic Era Hill University Dehradun India Chandigarh University Gharuan Punjab Mohali India CSE School of Computing Science & Engineering and Technology Galgotias University Greater Noida India

ISBN: (纸本)9798350305708

Network coding is a promising method for boosting wireless networks' performance and security. It is an effective coding method that lowers bandwidth and latency, boosts resilience, and uses less energy. The improvement of network coding for better performance and security in wireless networks is the main topic of this article. To increase the volume of data that may be delivered across wireless networks, a novel network coding approach is developed. With the suggested enhanced network coding, the network speed, latency, and security are all increased. Additionally, simulations are run to confirm the outcomes of the suggested strategy. The findings show that the suggested method considerably improves the upper and lower bounds for the network coding problem, leading to improved wireless network security and performance. These results suggest that enhanced network coding can be a promising approach for secure and high-performance networks. © 2023 IEEE.

关键词： bandwidth high-performance lower latency coding problem robustness

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A Multi-Resolution Deep Learning Approach for IoT-Integrated Biomedical Signal Processing using CNN-LSTM

A Multi-Resolution Deep Learning Approach for IoT-Integrated...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： K. Priyadharshini Ajisha Mathias R. Santhana Krishnan N Kanthimathi J. Relin Francis Raj P. Stella Rose Malar Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering J.P. College of Engineering Tenkasi India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The rapid advancement of Internet of Things (IoT) technology and deep learning has enabled real-time health monitoring for elderly individuals and patients. This work proposes an IoT-based health monitoring system integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to analyze physiological signals and detect anomalies effectively. A network of biomedical sensors, including ECG, EMG, EEG, heart rate, blood pressure, SpO2, temperature, accelerometer, gyroscope, and respiratory rate sensors, continuously collects health data. These sensors transmit real-time physiological signals using wireless communication protocols such as Wi-Fi, Bluetooth, LoRa, and NB-IoT, ensuring seamless data acquisition and remote monitoring. To enhance signal quality, the preprocessing stage employs Wavelet Transform (WT) for denoising biomedical signals, mitigating motion artifacts and environmental noise. Discrete Wavelet Transform (DWT) is applied to extract meaningful features while preserving critical health information. Additional preprocessing steps, including feature normalization, segmentation, and feature extraction, improve deep learning model efficiency. The proposed CNN-LSTM hybrid model leverages CNN for spatial feature extraction and LSTM for capturing temporal dependencies in time-series biomedical data. The architecture includes multiple convolutional layers with ReLU activation, batch normalization, max-pooling, bidirectional LSTM layers, dropout regularization, and an attention mechanism. Model training utilizes labeled medical datasets with an Adam optimizer and binary cross-entropy loss function. Performance evaluation considers accuracy, precision, recall, F1-score, and AUC-ROC metrics. Results demonstrate improved anomaly detection for conditions such as arrhythmias, respiratory irregularities, and early stroke indicators. Deployment in real-time health monitoring applications facilitates continuous patient assessment, fall detecti

关键词： Deep learning Medical services Feature extraction Real-time systems Convolutional neural networks Biosensors Biomedical monitoring Monitoring Medical diagnostic imaging Anomaly detection

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Modeling and Analyzing of Breast Tumor Deterioration Process with Petri Nets and Logistic Regression

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Complex System Modeling and Simulation 2022年第3期2卷 264-272页

作者： Xuyue Wang Wangyang Yu Zeyuan Ding Xiaojun Zhai Sangeet Saha Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourismand School of Computer ScienceShaanxi Normal UniversityXi’an 710100China School of Computer Science Shaanxi Normal UniversityXi’an 710100China School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK

It is important to understand the process of cancer cell metastasis and some cancer characteristics that increase disease *** the occurrence of the disease is caused by many factors,and the pathogenesis process is also *** is necessary to use interpretable and visual modeling methods to characterize this complex *** learning techniques have demonstrated extraordinary capabilities in identifying models and extracting patterns from data to improve medical prognostic ***,in most cases,it is *** formal methods to model can ensure the correctness and understandability of prediction decisions in a certain extent,and can well visualize the analysis *** Petri Nets(CPN)is a powerful formal *** paper presents a modeling approach with CPN and machine learning in breast cancer,which can visualize the process of cancer cell metastasis and the impact of cell characteristics on the risk of *** evaluating the performance of several common machine learning algorithms,we finally choose the logistic regression algorithm to analyze the data,and integrate the obtained prediction model into the CPN *** method allows us to understand the relations among the cancer cell metastasis and clearly see the quantitative prediction results.

关键词： coloured Petri nets visual modeling machine learning breast cancer

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