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2022 IEEE Symposium on Wireless Technology and Applications, ISWTA 2022

作者： Pebrianti, Dwi Ariawan, Angga Bayuaji, Luhur Mahdiana, Deni Rusdah, Rusdah Universitas Budi Luhur Magister of Computer Science Faculty of Information Technology Jakarta Indonesia International Islamic University Malaysia Faculty of Engineering Dept. of Mechanical Engineering Gombak Malaysia Universitas MNC Faculty of Computer Science Department of Computer and Media Jakarta Indonesia Universitas Budi Luhur Faculty of Information Technology Department of Information System Jakarta Indonesia

ISBN: (数字)9781665484824

ISBN: (纸本)9781665484824

Works distribution is a routine carried out every day by the head of the branch in the SHARP Service Center. The accuracy of the labor division is very important to get customer satisfaction. Inappropriate work distribution can increase complaints from customers. Currently, works distribution in SHARP Service Center is carried out manually, where the works received on the selected system is then shared through the document provided. Time taken for this process is about 1.42 minutes on average for each damage reports. Speed of Service also depends on the Head of department's expertise and experience. In this study, an automatic system based on Machine Learning will be designed for the technicians work distribution by using a combination of k Nearest Neighbor (k-NN) and Naïve Bayes. Naïve Bayes algorithm is used to improve the feature extraction accuracy by considering the feature below the average (α). Meanwhile, k-NN algorithm is used to classify the experimental data. from the study, it is found that the best of k value for k-NN algorithm is 15. It is known that a high number of accuracy values, the labor distribution can be more accurate. The validation of the proposed method is conducted by using a confusion matrix with a composition of 80% training data and 20% test data. The single Classifier test with the Naïve Bayes algorithm produces the highest accuracy value of 72.7%, while using k-NN algorithm is 81.5%. With a combination of Naive Bayes and k-NN algorithms, the accuracy value is increasing to 86%. This result shows that the proposed method improves the accuracy by 13.3% on single Naive Bayes algorithms and 4% on a single k-NN algorithm. The results obtained show that in the manual process, the average time per job is 1.42 minutes, while by using the proposed method, the average processing time is around 0.03 seconds per job. An increase of 2480 times faster is found and confirmed during the implementation of the proposed method. © 2022 IEEE.

关键词： Customer satisfaction

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Munikrishnaiah Sundararamaiah Chandrasekhar Rao Katru Chandra Sekhar Kolli Kruti Sutaria K.B. Venkata Brahma Rao Ramya Maranan Indian Land SC USA Department of Computer Science and Engineering Aditya University Surampalem Andhra Pradesh Computer Science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Gujarat Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

People are making daily purchases and sales through the internet, and most of them require them to use their credit cards. Besides making things easier for the customer, this heightens the probability of online credit card frauds. Credit card fraud detection has increased in several nations in the recent past due to the increased incidence of fraud that requires it to become even more of a security issue than before. In this paper, a new deep-learning-based approach for credit card fraud detection is proposed. It is based on the preprocessing stage, hybrid feature selection, and classification stage. In the first instance, the proposed method is used on the Credit Card Fraud Detection (CCF) data set. A prescreening step that uses Z-score Min-Max normalization is applied to ensure that the ranges of each data set are the same. The Crayfish Optimization Algorithm (COA) and the Emperor Penguin Optimizer (EPO) are integrated and used to identify the better attributes. Next, a similarity-navigated graph neural network (SNGNN) is utilized for classification so that the fraudulent transactions and the transactions that are not fraudulent can be differentiated easily. The overall system is also enhanced with Greylag Goose Optimization (GGO) that maximizes the chances of detection. The proposed method achieves a high accuracy in detecting fraudulent transactions, which means the proposed process has virtually limitless potential to speed up the entire process. Thus, high effectiveness is proved by the 99% accuracy rate of the suggested method. This exceptional outcome proves the robustness of the approach and its ability to adapt to various patterns of dependence and data changes.

关键词： Accuracy Machine learning algorithms Credit cards Feature extraction Graph neural networks Robustness Fraud Classification algorithms Security Optimization

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Enhancing Network Intrusion Detection with Deep Learning: A Comprehensive Analysis

Enhancing Network Intrusion Detection with Deep Learning: A ...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Mahbubur Rahman Md. Shahriar Rahman Chinmay Bepery Md. Atikqur Rahaman Md. Samsuzzaman Department of Computer Science and Information Technology Patuakhali Science and Technology University Patuakhali Bangladesh Faculty of Computer Science and Engineering Patuakhali Science and Technology University Patuakhali Bangladesh Department of Computer and Communication Engineering Patuakhali Science and Technology University Patuakhali Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

With the growing sophistication of cyberattacks, traditional intrusion detection systems (IDS) are often inadequate. This research addresses these shortcomings by leveraging deep learning techniques to enhance the detection of network intrusions. Previous studies have either retained unnecessary features, leading to biased results, or overly simplified feature sets, resulting in low accuracy. This study systematically refines feature selection and employs advanced deep learning models to improve detection performance. The result is a robust IDS that mitigates bias and enhances accuracy, offering a significant improvement over existing methods. Our findings provide valuable insights into the effective application of machine learning for network security.

关键词： Deep learning Support vector machines Accuracy Computational modeling Training data Network intrusion detection Network security Nearest neighbor methods Feature extraction Principal component analysis

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PIVODL: Privacy-Preserving Vertical Federated Learning Over Distributed Labels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 988-1001页

作者： Zhu, Hangyu Wang, Rui Jin, Yaochu Liang, Kaitai University of Surrey Department of Computer Science GuildfordGU27XH United Kingdom Delft University of Technology Department of Intelligent Systems Delft2628XE Netherlands Bielefeld University Nature Inspired Computing and Engineering Group Faculty of Technology BielefeldD-33619 Germany

Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Nonparametric models like gradient boosting decision trees (GBDTs) have been commonly used in FL for vertically partitioned data. However, all these studies assume that all the data labels are stored on only one client, which may be unrealistic for real-world applications. Therefore, in this article, we propose a secure vertical FL framework, named privacy-preserving vertical federated learning system over distributed labels (PIVODL), to train GBDTs with data labels distributed on multiple devices. Both homomorphic encryption and differential privacy are adopted to prevent label information from being leaked through transmitted gradients and leaf values. Our experimental results show that both information leakage and model performance degradation of the proposed PIVODL are negligible. Impact Statement - Federated learning is a distributed machine learning framework proposed for privacy preservation. Most federated learning algorithms work on horizontally partitioned data, with only a few exceptions considering vertically partitioned data that is widely seen in the real world. However, existing vertical federated learning makes an unrealistic assumption that data labels are distributed on only one device and no research has been reported so far that considers data labels distributed on multiple client devices. The PIVODL framework reported in this article allows us to build a secure vertical federated XGBoost system, in which the labels may distributed either on one device or on multiple devices, making it possible to apply federated learning to a wider range of real-world problems. © 2020 IEEE.

关键词： Decision trees

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An Optimal Link cum State Routing method for channelized data MANET 1

An Optimal Link cum State Routing method for channelized dat...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Saxena, Ashendra Kumar Prakash, Deepak Bhatt, Pujita Pandey, Deepak Mehta, Shiv Singh, Devesh Pratap Teer Thanker Mahaveer University Faculty of Engineering Department of Computing Sciences &iT Uttar Pradesh Moradabad India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Parul University Department of Mechatronics Gujarat Vadodara India Computer Science & Engineering RCET Bhilai India Dhirubhai Ambani International School India Graphic Era Deemed to Be University Department of CSE Uttarakhand Dehradun India

ISBN: (纸本)9781665456876

NDN served as a noteworthy illustration of data communication. When implementing NDN in routing, studying novel routes is a key problem for this research subject. Periodic LSAs flood is a well-liked method for acquiring topology for route design. However, this LSA overflow might sometimes trigger a huge storm while MANET is growing. The broadcasting storm might be further handled by effectively lowering the number of LSAs engaged in the route beginning creation and using the Node set indicated there in Aodv protocol. The MPR set is initially created in this paper using an improved neighbor discovery technique. The vertical connection problem, which affects the connection speeds of the mobile body, may be effectively avoided by the protocol. Then, a brand-new and proactive router NOLSR based on OLSR was demonstrated in order to allow NDN in MANET. A noteworthy achievement is the NOLSR implementation in front of Network Routing Device Converting. In order to compare NOLSR with the four most similar schemes, including traditional Annotation and the approach, we undertake simulation experiments in the NDN emulation tiny. © 2022 IEEE.

关键词： Storms

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Milestones of Wireless Communication Networks and Technology Prospect of Next Generation(6G)

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computers, Materials & Continua 2022年第6期71卷 4803-4818页

作者： Mohammed H.Alsharif Md.Sanwar Hossain Abu Jahid Muhammad Asghar Khan Bong Jun Choi Samih M.Mostafa Department of Electrical Engineering College of Electronics and Information EngineeringSejong University209 Neungdong-roGwangjin-guSeoul05006Korea Department of Electrical and Electronic Engineering Bangladesh University of Business and TechnologyDhaka1216Bangladesh Department of Electrical&Computer Engineering University of OttawaOttawaON K1N 6N5Canada Department of Electrical Engineering Hamdard University KarachiIslamabad CampusIslamabad44000Pakistan School of Computer Science and Engineering&School of Electronic Engineering Soongsil UniversitySeoulKorea Faculty of Computers and Information South Valley UniversityQena83523Egypt Academy of Scientific Research and Technology Cairo11516Egypt

Since around 1980,a new generation of wireless technology has arisen approximately every 10 ***-generation(1G)and secondgeneration(2G)began with voice and eventually introduced more and more data in third-generation(3G)and became highly popular in the fourthgeneration(4G).To increase the data rate along with low latency and mass connectivity the fifth-generation(5G)networks are being installed from ***,the 5G technology will not be able to fulfill the data demand at the end of this ***,it is expected that 6G communication networks will rise,providing better services through the implementation of new enabling technologies and allowing users to connect everywhere.6G technology would not be confined to cellular communications networks,but would also comply with non-terrestrial communication system requirements,such as satellite *** ultimate objectives of this work are to address the major challenges of the evolution of cellular communication networks and to discourse the recent growth of the industry based on the key scopes of application and *** main areas of research topics are summarized into(i)major 6G wireless networkmilestones;(ii)key performance indicators;(iii)future new applications;and(iv)potential fields of research,challenges,and open issues.

关键词： Wireless networks beyond 5G 6G communications terahertz frequency terahertz communications holographic calls

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Automated Detection of Alzheimer's Disease Using EfficientNet-B3 Architecture with Transfer Learning 25

Automated Detection of Alzheimer's Disease Using EfficientNe...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Aburomman, Ahmad Abuowaida, Suhaila Al-Momani, Adai Al Henawi, Esraa Elsoud, Esraa Abu Salah, Zaher Alzoubi, Haneen Chan, Huah Yong Khouj, Mohammed Zarqa University Department of Data Science and Ai Zarqa Jordan Al-Bayt University Department of Computer Science Ai Mafraq Jordan Zarqa University Department of Computer Science Zarqa Jordan The Hashemite University Department of Information Technology Zarqa Jordan Applied Science Private University Faculty of Information Technology Amman Jordan Universiti Sains Malaysia School of Computer Sciences Pulau Pinang Gelugor11800 Malaysia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

In order to bring forward expansion in both the accuracy and the processing speed of the detection of Alzheimer's disease, an automated approach for Alzheimer's disease diagnosis with passable EfficientNet-B3 neural network and transfer learning techniques is provided in this work. After direct fitness on a large number of annotated brain images, the model of EfficientNet-B3 is further trained, in an effort to obtain worse depicted quality connected with Alzheimer's Disease. To hasten the process of training and to make the model fit for this specific task, transfer learning was employed. The results of these experiments prove the efficiency of our approach to the diagnostic Alzheimer's disease compared to the most advanced and regular deep learning methods. Thus, the model may also withstand the test amidst diverse clinical populations by performing well in dementia classification with precision and recall performance coupled with over 99% accuracy. These findings demonstrate how the EfficientNet-B3 architecture and transfer learning enhance generalization across. © 2024 IEEE.

关键词： Image annotation

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The applications of nature-inspired algorithms in Internet of Things-based healthcare service: A systematic literature review

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Transactions on Emerging Telecommunications Technologies 2024年第6期35卷

作者： Amiri, Zahra Heidari, Arash Zavvar, Mohammad Navimipour, Nima Jafari Esmaeilpour, Mansour Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Istanbul Turkey Department of Computer Engineering Sari Branch Islamic Azad University Sari Iran Department of Computer Engineering Kadir Has Universitesi Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Computer Engineering Dept. Hamedan Branch Islamic Azad University Hamedan Iran

Nature-inspired algorithms revolve around the intersection of nature-inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature-inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real-world implementation issues, and the efficacy of nature-inspired algorithms in IoT-based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature-inspired algorithms in IoT-based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature-inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature-inspired algorithms in the dynamic landscape of IoT-based healthcare services. © 2024 John Wiley & Sons, Ltd.

关键词： MATLAB

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NeuroSparse: An Unsupervised Framework for Inferring Brain Connectivity in Autism Diagnosis

NeuroSparse: An Unsupervised Framework for Inferring Brain C...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Shuqi Zhang, Lijuan Lan, Qing Ma, Qiujie Huang, Huan Miao, Jiaqing Mou, Shiyu Luo, Cheng Yao, Dezhong Tan, Ying Southwest Minzu University Key Laboratory for Computer Systems of State Ethnic Affairs Commission Chengdu China Moe Key Lab for Neuroinformation University of Electronic Science and Technology of China Clinical Hospital Chengdu Brain Science Institute Chengdu China University of Malaya Faculty of Engineering Department of Biomedical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350386226

Autism spectrum disorder (ASD) is a mental disorder that severely affects social interaction and communication skills. A timely and accurate diagnosis is crucial for effective intervention. However, as an objective diagnostic tool, the existence of spurious connections and noise within the functional connectivity (FC) matrix in the brain contribute to the complexity of ASD diagnosis. In this study, we propose an unsupervised sparse graph structure learning framework, called NeuroSparse, for reasoning regarding the topological connectivity relationships between brain regions. First, the framework fuses spatiotemporal information from static and dynamic FC to provide a comprehensive representation of complex brain networks. Next, the context and neighborhood information of nodes are captured as their local and global embeddings, and the latent representations of node embeddings are learned, which are then decoded to infer the connectivity relationships among the regions of interest. Finally, we introduce three innovative loss functions to regularize and optimize the sparse representation generation process. The experimental results showed that, after learning using NeuroSparse, the FC matrix achieved state-of-the-art performance in ASD diagnosis using only a simple multilayer perceptron, indicating its wide-ranging application potential. The code is available at https://***/yangshuqigit/NeuroSparse. © 2024 IEEE.

关键词： Unsupervised learning

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Comparative Study of the Resolution of Ge-on-Si Photodetectors for 1μ m Infrared Signals 1

Comparative Study of the Resolution of Ge-on-Si Photodetecto...

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1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

作者： Jahangiri, Mojtaba Sberna, Paolo Sammak, Amir Nihtianov, Stoyan Mathematics and Computer Science Delft University of Technology Faculty of Electrical Engineering Department of Microelectronics Delft Netherlands Delft Netherlands

ISBN: (纸本)9798350398069

Most of the studies on narrow-band near-infrared detection reported so far are related to the 1.3μm and 1.55μm spectral windows. There is insufficient research work done on radiation detection in the narrow band around 1 μm wavelength, which is just outside the Si (0.95μ m) and GaAs (0.85μ m) effective cut-off spectral sensitivity. This paper presents a p+n Ge-on-Si detector with a customized large active window, employing the PureGaB technology, to detect radiation in a very narrow band around 1μ m. The advantages of the proposed detector are: (1) CMOS-compatibility and micro-spectroscopic capability;(2) low dark current and high photoresponsivity, compared to similar devices reported in the literature;(3) enhanced sensitivity to weak radiation by realizing an ultra-shallow and very thin depletion region. These detectors can be good candidates for measuring the YAG laser radiation and measuring stray radiation in photolithography. © 2022 IEEE.

关键词： III-V semiconductors

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