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3rd International Conference on Automation, Computing and Renewable systems, ICACRS 2024

作者： Jumlesha, Shaik Raju, S. Hrushikesava Adinarayna, S. Sesadri, U. Choudhury, Nabanita Jadala, Vijaya Chandra AP Tirupati517501 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Guntur522502 India Department of Computer Science and Systems Engineering College of Engineering Andhra University Visakhapatnam531162 India Department of CSE Vardhaman College of Engineering Telangana Hyderabad India Faculty of Computer Technology Assam down town University Assam Guwahati India School of Computer Science and Artificial Intelligence SR University Ananthasagar Hasanparthy Hanumakonda Telangana Warangal506371 India

ISBN: (纸本)9798331532420

Precision health is emerging as a significant trend in healthcare, emphasizing the customization of medical care and interventions to align with the unique characteristics of each patient, ultimately maximizing health outcomes and well-being. Traditional approaches to personalized care are often time-consuming and lack the necessary focus on individual patient needs, leading to inadequate monitoring of disease progression and insufficient patient engagement. To address these challenges, the proposed approach employs an Improved Adaptive Health Navigator (IAHN) that continuously monitors each patient and formulates ecofriendly medications tailored to their specific symptoms. This innovative process incorporates various factors such as lifestyle choices, genomic data, family history, and other relevant parameters to inform precision medicine recommendations. The resulting precision medicine approach is designed to enhance recovery speed and improve patient outcomes. By adopting a proactive strategy, this model facilitates early detection of health issues, reduces the incidence of illnesses, enhances quality of life, and optimizes resource allocation. The IAHN plays a crucial role in the ongoing development of personalized medicine by ensuring continuous health monitoring, empowering healthcare providers, and enabling diagnostic tools to make informed decisions for disease prevention. © 2024 IEEE.

关键词： Personalized medicine

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Street Light Cum Garbage System Optimal Design Based on IOT 2

Street Light Cum Garbage System Optimal Design Based on IOT

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Rajesh, G. Deepa, J. Ezilarasan, M.R. Syed Fiaz, A. Siva Rama Lingham, N. Jagannathan, J. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350328202

Street lights currently use more energy than other types of lighting because of an inefficient mechanism that makes the bulbs use a lot of electricity. The suggested approach uses various sensors on intelligent streetlights to keep an eye on and manage the lights. It has sensors for measuring temperature, brightness, and power that control dimming levels and keep track of status. One Zigbee network serves as the connection point for these lights. In the modification, a cloud- and IoT-based installation was made. Through Zigbee -based data transmission, sensor values are recorded on a distant server known as the Cloud. Additionally, it lowers electricity theft. We include a trash can notice in addition to this street light idea. When the garbage can is whole, a machine automatically notifies the business as people approach the sun, and the brightness of the street lights changes or is increased. © 2024 IEEE.

关键词： Light emitting diodes

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A New Tree Structure for Local Diagnosis

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Journal of Information science and engineering 2024年第2期40卷 231-244页

作者： Chen, Meirun Li, Xiao-Yan Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen361024 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taoyuan30010 Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City243303 Taiwan

Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of local diagnosis. Chiang and Tan provided an extended star structure to diagnose a node under comparison model. In this work, we evaluate the local diagnosability better by proposing a tree structure around this node. We provide the corresponding algorithm to diagnose the node. Simulation results are presented for different failure probability of a node in the tree and different percentage of faulty nodes in the tree, showing the performance of our algorithm. © 2024 Institute of Information science. All rights reserved.

关键词： comparison model diagnosis algorithm fault diagnosis local diagnosability tree

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Analysis of Significant Cell Differences Between Cancer Patients and Healthy Individuals 28th

Analysis of Significant Cell Differences Between Cancer Pati...

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28th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2023

作者： Sun, Pei-Chi Yan, Xiaowei Li, Yu-Wei Chen, Hao-Xiang Li, Hsu-Ching li, Cheng-Yu Cheng, Wan-Shu Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Institute for Systems Biology SeattleWA United States Department of Computer Science and Information Management Providence University Taichung Taiwan

ISBN: (纸本)9789819717132

At the end of 2019, a global outbreak of a new coronavirus ravaged the world, and to this day, many people’s bodies are still deeply affected by the virus. In order to find out if there is a correlation between different levels of cancer diagnosis and healthy samples, and to further compare the differences between healthy samples and cancer samples, we have identified a significant set of genes that highlights the differences between the two samples. Therefore, we used statistics, immunology, and artificial intelligence to analyze the blood sampling data of cancer patients diagnosed with the new coronavirus during the diagnostic period, and compared them with healthy donors. We initially found that the proportion of significant cells in the two stem cell gene sets of the cancer samples was more than about two times greater than that of the healthy samples. Therefore, in the future, we will be able to further analyze the differences between the two samples and find out the key factors that can assist in medical diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coronavirus

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Enhancing Rehabilitation Outcomes: A Hybrid Cognitive-Machine Learning Approach 9

Enhancing Rehabilitation Outcomes: A Hybrid Cognitive-Machin...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Jadala, Vijaya Chandra Raju, S. Hrushikesava Adinarayna, S. Choudhury, Nabanita Jumlesha, Shaik Sesadri, U. School of Computer Science and Artificial Intelligence Sr University Ananthasagar Hasanparthy Hanumakonda Telangana Warangal506371 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Green Fields Vaddeswaram Guntur522502 India College of Engineering Andhra University Department of Computer Science and Systems Engineering Visakhapatnam531162 India Assam down town University Faculty of Computer Technology Assam Guwahati India AP Tirupati517501 India Vardhaman College of Engineering Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798350377972

Traditional rehabilitation methods often focus on a single impairment, leading to challenges such as low motivation and inadequate responses to the diverse needs of patients, time taken for appointments, and lack of personal monitoring of health status. To address these issues, The model employs meta-cognitive strategies, dynamic adaptive training, and modified Convolutional Neural Networks (CNN) within an ensemble framework, targeting multiple impairments while tailoring interventions to individual patient requirements. A hybrid model proposed would integrate cognitive and machine learning methodologies to enhance personalized rehabilitation. By selectively employing meta-cognitive strategies, dynamic adaptive training, and modified Convolutional Neural Networks (CNN) within an ensemble framework, the proposed system aims to bridge existing gaps in rehabilitation practices. The hybrid system is designed to efficiently manage patient data, reduce the complexity of rehabilitation tasks, and optimize computational resources. The use of stacking as an ensemble method further mitigates biases and uncertainties associated with individual models, thereby improving the reliability of the rehabilitation framework. Preliminary outcomes indicate that this hybrid approach achieves superior accuracy and efficiency compared to traditional methods, ultimately enhancing adaptability to the varied needs and preferences of patients. © 2024 IEEE.

关键词： Convolutional neural networks

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The Efficient Cluster Head Selection for NarrowBand Internet of Things

The Efficient Cluster Head Selection for NarrowBand Internet...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Huang, Shih-Yun Lee, Shou-Yu Wu, Tin-Yu Chang, Yao-Chung Chao, Han-Chieh Tunghai University Department of Computer Science Taichung Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Taiwan National Taitung University Department of Computer Science and Information Engineering Taitung Taiwan National Dong Hwa University Department of Electrical Engineering Hualien Taiwan

ISBN: (纸本)9798350324174

The future of 5G will focus on massive machine-type communication (mMTC), and many applications will be based on this application and gradually mature. These applications include smart cities, e-health, and the Internet of Vehicles (IoV). In addition, the Narrowband Internet of things (NB-IoT) is a critical technology in mMTC. NB-IoT can bring many benefits, such as efficient bandwidth use and long-distance transmission. However, although NB-IoT has many advantages, issues such as throughput, energy consumption, and security still need to be solved. In order to effectively solve these problems, the choice of cluster heads is very important. Considering that the energy consumption of the CH will be more severe than that of other devices, which will also shorten the network;therefore, we will focus on the dynamic cluster head selection. To solve this problem, we introduced the concept of the pseudo-rigid body into this paper and proposed a method similar pseudo-rigid body to determine the CHs;at the same time, we also defined the maximum range of each cluster. The experimental results show that the proposed method can dynamically select the CH and increase the network lifetime. © 2023 IEEE.

关键词： Internet of things

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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Discrete ripplet-II transform feature extraction and metaheuristic-optimized feature selection for enhanced glaucoma detection in fundus images using least square-support vector machine

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Multimedia Tools and Applications 2024年 1-33页

作者： Sharma, Santosh Kumar Muduli, Debendra Rath, Adyasha Dash, Sujata Panda, Ganapati Shankar, Achyut Dobhal, Dinesh Chandra Department of Computer Science and Engineering C.V. Raman Global University Odisha India Department of Information Technology School of Engineering and Technology Nagaland University Dimapur India Department of Cyber Systems Engineering WMG University of Warwick CoventryCV74AL United Kingdom University Centre for Research & amp Development Chandigarh University Punjab Mohali140413 India School of Computer Science Engineering Lovely Professional University Punjab Phagwara144411 India Center of Research Impact and Outcome Chitkara University Punjab Rajpura India Department of Computer Science & amp Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

Recently, significant progress has been made in developing computer-aided diagnosis (CAD) systems for identifying glaucoma abnormalities using fundus images. Despite their drawbacks, methods for extracting features such as wavelets and their variations, along with classifier like support vector machines (SVM), are frequently employed in such systems. This paper introduces a practical and enhanced system for detecting glaucoma in fundus images. The proposed model adresses the chanallages encountered by other existing models in recent litrature. Initially, we have employed contrast limited adaputive histogram equalization (CLAHE) to enhanced the visualization of input fundus inmages. Then, the discrete ripplet-II transform (DR2T) employing a degree of 2 for feature extraction. Afterwards, we have utilized a golden jackal optimization algorithm (GJO) employed to select the optimal features to reduce the dimension of the extracted feature vector. For classification purposes, we have employed a least square support vector machine (LS-SVM) equipped with three kernels: linear, polynomial, and radial basis function (RBF). This setup has been utilized to classify fundus images as either indicative of glaucoma or healthy. The proposed method is validated with the current state-of-the-art models on two standard datasets, namely, G1020 and ORIGA. The results obtained from our experimental result demonstrate that our best suggested approach DR2T+GJO+LS-SVM-RBF obtains better classification accuracy 93.38% and 97.31% for G1020 and ORIGA dataset with less number of features. It establishes a more streamlined network layout compared to conventional classifiers. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Radial basis function networks

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Weather Satellite Tracking with RTL-SDR: NOAA 15 Image Reception and Signal Decoding 2

Weather Satellite Tracking with RTL-SDR: NOAA 15 Image Recep...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Waseem, Ahsan Kamil, Mohd Azhan Umar Bhardwaj, Abhisehek Razim, Abdul Qadeer, Mohammed Abdul Ghazal, Taher M. Aligarh Muslim University Department Of Electrical Engineering Aligarh India Aligarh Muslim University Department Of Computer Engineering Aligarh India Stony Brook University Department Of Electrical And Computer Engineering New York United States Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

This study examines the growing number of satellites, this paper explores the importance of satellite tracking and highlights NOAA-15 for information transmission and weather monitoring. It offers a low-cost configuration for receiving circularly polarized signals, using a Software Defined Radio (SDR) at 137.62 MHz and a V-dipole antenna. The directional characteristics of the V-dipole maximize capture efficiency by improving signal reception during particular satellite orbits. The flexible SDR, in particular the RTL SDR USB dongle, is used to detail signal decoding for Automatic Picture Transmission (APT), allowing for effective processing and tracking. High-quality APT photos are produced by this hardware-software integration, highlighting NOAA's crucial role in international weather forecasting. The study also emphasizes how simple and inexpensive it is to use the V-dipole antenna to receive NOAA weather satellite signals. © 2024 IEEE.

关键词： Weather forecasting

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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