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APPLICATION OF DEVELOPING CLOTHING RECOMMENDATION SYSTEM WITH ARTIFICIAL INTELLIGENCE TECHNIQUES

Tekstil ve Muhendis 2024年第136期31卷 241-252页

作者： Özbek, Ahmet Altuntaş, Volkan Erdoğan, Naile Department of Textile Engineering Faculty of Technology Marmara University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Sciences Bursa Technical University Bursa Turkey Department of Fashion Design Gerze Vocational School Sinop University Sinop Turkey

This study aims to develop a clothing recommendation application for users who possess a large number of clothes but have limited time due to their intense work tempo. This application aims to assist them in using their clothes effectively, reducing the time spent on selecting outfits, and dressing fashionably. In the process of developing this application, firstly, the criteria influencing the user’s clothing preferences were determined. Subsequently, a wardrobe dataset was created based on the established criteria. Following this, methods for suggesting clothes were explored. As a result of the research, it was decided to utilize association rule analysis, multidimensional clothing representation coding, and weighted L1 distance methods for clothing recommendation in this study. In the application phase, experiments were conducted using the dataset associated with the chosen methods. It has been determined that the application developed in this study gives successful results in suggesting clothes suitable for user preferences. © (2024), (Chamber of Textile Engineers). All rights reserved.

关键词： Recommender systems

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A High-Performance Data Verification Mechanism in Blockchain-Based IoT Systems 21

A High-Performance Data Verification Mechanism in Blockchain...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Jingyu Zhou, Pian Sun, Yongtao Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

With the rapid development of the distributed network and communication, the Internet of Things (IoT) systems have been applied to all walks of life. Blockchain technology is one of the most popular research fields recently, which can provide a reliable storage solution and solve information security issues for IoT systems. Transaction information is the most critical fundamental data in the blockchain systems, which needs to be verified to avoid being tempered with by malicious nodes in the process of transmission. With the exponential growth of the number of IoT devices, the limitations of the current storage structure put too much pressure on the system nodes. How to improve verification efficiency has become a key challenge. To alleviate this problem, this paper proposes a novel high-performance verification mechanism for data security protection in blockchain-based IoT systems. We design a new storage structure based on Huffman trunk tree (HTT), and conduct the quantitative analysis of transaction weights. Transactions are stored in full-featured devices in the form of Huffman Merkle tree (HMT), and only the content of HTT is saved in lightweight devices. Finally, the performance superiority of our mechanism is proved through theoretical analysis and experimental evaluation. In blockchain-based IoT systems, our mechanism significantly reduces the data transmission cost and computation overhead, effectively improving the efficiency of data verification. © 2023 IEEE.

关键词： Efficiency

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Multi-Task Learning for UAV Trajectory and Caching With Federated Cloud-Assisted Knowledge Distillation

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2516-2533页

作者： Tietaa Maale, Gerald Arden Elorm Kuadey, Noble Arafat, Yeasin Thomas, Kwantwi Sun, Guolin Liu, Guisong Research Institute of Intelligent Computing University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China McCoy University College of Education Department of Mathematics and ICT Nadowli Ghana Research Institute of Intelligent Computing at the University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China Ho Technical University Department of Computer Science Ho Ghana University of Mines and Technology Department of Computer Science and Engineering Tarkwa Ghana Southwestern University of Finance and Economics Complex Laboratory of New Finance and Economics School of Computing and Artificial Intelligence Chengdu610074 China Ministry of Education Engineering Research Center of Intelligent Finance Chengdu610030 China

The proliferation of Internet of Things (IoT) technologies and ubiquitous connectivity has led to uncrewed aerial vehicles (UAVs) playing key role as edge servers, revolutionizing the wireless communications landscape by facilitating computing and caching resources closer to ground users (GUs). This advancement significantly alleviates core network loads, reduces latency, and guarantees content availability even in congested or remote areas. However, jointly optimizing UAV caching strategies and trajectories gives rise to a multi-task optimization (MTO) problem. This paper introduces a novel multi-task geo-temporal caching (MT-GTC) framework that addresses the interplay between UAV caching mechanisms and trajectory optimization in a cohesive manner. Leveraging a proposed multi-task learning (MTL) model for joint optimization of UAV caching and trajectory design, we develop a federated learning cloud-assisted knowledge distillation (FL-CAKD) scheme to preserve data privacy and adapt to data heterogeneity. FL-CAKD transfers knowledge from a cloud model orchestrator (CMO), which houses a large and sophisticated teacher model, to a lightweight on-device MTL student models using soft target distributions instead of large model parameters, significantly reducing communication costs. MT-GTC optimizes caching and trajectories to maximize cache hits and minimize latency. Evaluations on real-world mobility datasets demonstrate up to 95% cache hit rates and 21% lower delays compared to baselines. © 2004-2012 IEEE.

关键词： Multi-task learning

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NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detection Using Neuroimaging Data 4

NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detec...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Hema, V. Gowri, S. Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that impacts social communication, behavior, and cognitive functions. Early detection of autism is crucial for timely intervention, which can significantly improve developmental outcomes for individuals. This paper presents a novel methodology for detecting Autism Spectrum Disorder (ASD) using a Cascaded Convolutional Neural Network (CCNN) model. The proposed CCNN model processes neuroimaging data to detect patterns indicative of autism. The dataset used comprises 6,214 samples from the Autism Brain Imaging Data Exchange (ABIDE). Key preprocessing steps include resizing images to 224x224 pixels, noise reduction using a Median Filter, image cropping to focus on the region of interest (ROI), and contrast enhancement using histogram equalization. The CCNN architecture involves multiple convolutional layers, followed by max-pooling and fully connected layers, culminating in a Softmax layer for classification. The model is trained using the Adam optimizer with binary cross-entropy loss, achieving an accuracy of 93.19% on the test set, outperforming traditional models such as SVM, Random Forest, and Logistic Regression. © 2024 IEEE.

关键词： Median filters

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Towards Intelligent Attendance Monitoring for Scalable Organization with Hybrid Model Using Deep Learning

Towards Intelligent Attendance Monitoring for Scalable Organ...

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Srivastava, Akhilesh Kumar Mishra, Chandrahas Mishra, Anurag Srivastava, Atul Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science KIET Group of Institutions Ghaziabad India Amity School of Engineering and Technology AUUP Lucknow India

ISBN: (纸本)9789819706402

Face recognition technology using AI has seen paradigm shift in the evolving world. Automatic attendance monitoring using real-time face identification is a solution to handle attendance in any small/large as well as scalable organization. Traditional methods in the organization involve calling names or signing sheets with individuals, which is a very time-consuming process and provides insurance. This is also subjected to manual errors. Automation of attendance recording and monitoring through face recognition is a process of identifying the face for taking attendance by using the image of the human face as biometric parameter captured through a surveillance camera in the premise. This article presents an effective way of attendance monitoring by making use of deep learning technology and compares its results with the state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Pilot-Attacks Can Enable Positive-Rate Covert Communications of Wireless Hardware Trojans

Pilot-Attacks Can Enable Positive-Rate Covert Communications...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Bakirtas, Serhat Bloch, Matthieu R. Erkip, Elza New York University Tandon School of Engineering BrooklynNY United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9798350351255

Hardware Trojans can inflict harm on wireless networks by exploiting the link margins inherent in communication systems. We investigate a setting in which, alongside a legitimate communication link, a hardware Trojan embedded in the legitimate transmitter attempts to establish communication with its intended rogue receiver. To illustrate the susceptibility of wireless networks against pilot attacks, we examine a two-phased scenario. In the channel estimation phase, the Trojan carries out a covert pilot scaling attack to corrupt the channel estimation of the legitimate receiver. Subsequently, in the communication phase, the Trojan exploits the ensuing imperfect channel estimation to covertly communicate with its receiver. By analyzing the corresponding hypothesis tests conducted by the legitimate receiver in both phases, we establish that the pilot scaling attack allows the Trojan to operate in the so-called "linear regime"i.e., covertly and reliably transmitting at a positive rate to the rogue receiver. Our results highlight the vulnerability of the channel estimation process in wireless communication systems against hardware Trojans. © 2024 IEEE.

关键词： Channel estimation

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Decentralized Deep Reinforcement Learning for Intelligent Transportation Systems 2

Decentralized Deep Reinforcement Learning for Intelligent Tr...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Bhide, Suvarna Chakurkar, Priti School of Computer Engineering and Technology Dr. Vishwanath Karad Mit World Peace University Pune India

ISBN: (纸本)9798350370188

This study introduces a novel MA2C (Multi-Agent Advantage Actor-Critic) approach for adaptive traffic signal control in complex urban traffic networks. MA2C addresses the scalability issue of centralized reinforcement learning (RL) by distributing the control to local RL agents. One method is proposed to reduce the learning difficulty of each agent. The effectiveness of MA2C can be evaluated in real-world traffic simulators using evaluation metrics of queue length and intersection delay. Further research can explore its efficiency by comparing it to other multi-agent reinforcement learning algorithms and its practical implications for traffic management systems, contributing to enhanced traffic flow, reduced congestion, and improved city transportation efficiency. © 2024 IEEE.

关键词： Reinforcement learning

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Novel Anchor Generation Based Residual Network for Object Tracking in Video-Surveillance Applications

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SN computer Science 2025年第2期6卷 1-12页

作者： Mohanapriya, D. Rajalakshmi, K. Helen Josephine, V.L. Panneerselvam, Karthick Department of Computer Science (SF) Sourasthra College Tamil Nadu Madurai India Department of Computer Applications CMR Institute of Technology Karnataka Bangalore India Business Analytics School of Business and Management Christ University Karnataka Bangalore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamilnadu Uthupalayam 641407 India

The activity of the object in question is alerted directly upon completion of an effective object tracking. Dependent on hardware support or not, a strong object tracking protocol is required for a precise object tracking application. According to these methods, tracking an object accurately within a predetermined processing time window required a significant amount of computer complexity. In contrast, a variety of quality-degrading elements, including occlusion, shifting lighting, shadows, and so on, have an adverse effect on tracking. All of these tracking shortcomings will be fixed by a revolutionary residual network based on loss operator and anchor creation. Detection of object has concerns that rely on the process of feature extraction to afford efficient quality. For this purpose a model called ResNet has been used that comprises thirty layers and hence named as Resnet-thirty. These networks are a type of Convolutional Neural Network (CNN) that contain residual connections among various layers. The various merits of these connections is the network has the capability to learn the features of global, local and intermediate in parallel. As such, the system is robust against changes in lighting. These variations in light were understood in terms of tracking objects within a changing background. The proposed work uses MOT datasets. This dataset comprises of MOT 15, MOT16, MOT17 and MOT20. The results have been found by using these datasets. Hence, it evidently outperforms in terms of precision, recall, MOTA, IDF, MOTP, SAIDF and F1 measure to track the objects. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer vision Loss operator Object tracking Residual network

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A Novel Soft-Switching Scheme for Three-Phase DC/AC Universal Minimal Converters (UMCs)

A Novel Soft-Switching Scheme for Three-Phase DC/AC Universa...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Hao, Ruomu Belkhode, Satish Benzaquen, Joseph Divan, Deepak Georgia Institute of Technology Center for Distributed Energy School of Electrical and Computer Engineering AtlantaGA United States

ISBN: (纸本)9798350376067

In this paper, a novel multi-mode soft-switching scheme with improved operating-cycle utilization for three-phase DC/AC Universal Minimal Converters (UMC) is proposed. The UMC is a recently developed single-stage isolated bi-directional converter that achieves the desired power transfer through various modes of operation, which can be selected based on performance optimization criteria(s). The proposed approach builds on the earlier presented multi-mode control principles for UMCs over a wide range of voltages while providing improved performance over a switching cycle basis. It simplifies the control implementation while improving the converter utilization over a switching period. The proposed switching scheme also enables a novel switching sequence, achieving soft-switching by utilizing the bi-directional AC switches. Additional benefits of this scheme include avoiding transformer saturation and eliminating intermediate switching states and complex phase-shift-power relationships. Simulation results demonstrate the proposed operation and soft-switching scheme, and a comparison between the proposed method and previous UMC control is presented. © 2024 IEEE.

关键词： Voltage-source converters

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A Bilevel Hybrid Genetic Algorithm for Capacitated Electric Vehicle Routing Problem 13

A Bilevel Hybrid Genetic Algorithm for Capacitated Electric ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Feng, Chang-Tao Jia, Ya-Hui Yang, Qiang Chen, Wei-Neng Jiang, Huaiguang School of Future Technology South China University of Technology Guangzhou China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350308365

As electric vehicles become more prevalent, a novel vehicle routing problem (VRP) has emerged, known as the capacitated electric VRP (CEVRP). CEVRP requires determining not only the service order of customers but also the charging plans for vehicles, thereby increasing the complexity of solution construction. In response to this challenge, we propose a bilevel hybrid genetic algorithm (BHGA). BHGA models CEVRP as two levels of subproblem: 1) the upper level capacitated VRP, focusing on the service order and 2) the lower level fixed route vehicle charging problem, focusing on the charging plans. In dealing with the upper level subproblem, the hybrid genetic search algorithm is adopted to construct the routes to visit customers and an advanced screening strategy is proposed to optimize the local search process and effectively guide the evolution of population. For the lower level subproblem, an efficient heuristic method called focus enumeration is designed, which is specifically used to insert charging stations into routes to ensure battery constraint. The collaboration of the advanced screening strategy and the focus enumeration assists in more unified solving of the two subproblems. The experiments show that BHGA significantly surpasses state-of-the-art algorithms on benchmark instances and has successfully updated eleven best known solutions, demonstrating its outstanding performance. © 2024 IEEE.

关键词： Heuristic methods

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