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27th International computer Science and engineering Conference, ICSEC 2023

作者： Badriyah, Tessy Ramadhan, Yunaz Gilang Syarif, Iwan Informatics and Computer Engineering Department Kampus PENS JL. Arief Rahman Hakim Sukolilo Surabaya60111 Indonesia

ISBN: (纸本)9798350342109

E-Commerce is a prominent application of information technology in business, offering convenience in transactions. However, as more products and users join the platform, the complexity of E-Commerce systems increases. This necessitates the implementation of a recommendation system to enhance the user experience and address individual preferences. E-Commerce platforms often face the challenge of the cold start problem, where new products are introduced to the platform or when there is limited information available about new users. In such cases, traditional recommendation systems struggle to provide accurate recommendations due to the lack of historical data or user preferences. In this research, a comprehensive framework for an efficient multi-mode Hybrid Recommendation System is proposed, focusing on addressing the cold start problem. The system leverages user behavior tracking, specifically search history and product visit history, to capture user preferences effectively. Through experimental evaluation, the system demonstrates its adaptability to different dataset conditions, achieving a high precision rate of 90%. This research addresses the challenges of data sparsity and the cold start problem in recommendation systems, particularly in the E-Commerce context. The flexibility of the recommendation system ensures optimal recommendations in various scenarios, with potential applications beyond E-Commerce. © 2023 IEEE.

关键词： Recommender systems

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Towards intelligent and trustworthy task assignments for 5G-enabled industrial communication systems

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Digital Communications and Networks 2025年第1期11卷 246-255页

作者： Mingfeng Huang Anfeng Liu Neal N.Xiong Athanasios V.Vasilakos School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China School of Electronic Information Central South UniversityChangsha410083China Department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA College of Mathematics and Computer Science Fuzhou UniversityFuzhou350116China Center for AI Research University of AgderGrimstad 999026Norway

With the unprecedented prevalence of Industrial Internet of Things(IIoT)and 5G technology,various applications supported by industrial communication systems have generated exponentially increased processing tasks,which makes task assignment inefficient due to insufficient *** this paper,an Intelligent and Trustworthy task assignment method based on Trust and Social relations(ITTS)is proposed for scenarios with many tasks and few ***,ITTS first makes initial assignments based on trust and social influences,thereby transforming the complex large-scale industrial task assignment of the platform into the small-scale task assignment for each ***,an intelligent Q-decision mechanism based on workers'social relation is proposed,which adopts the first-exploration-then-utilization principle to allocate *** when a worker cannot cope with the assigned tasks,it initiates dynamic worker recruitment,thus effectively solving the worker shortage problem as well as the cold start *** importantly,we consider trust and security issues,and evaluate the trust and social circles of workers by accumulating task feedback,to provide the platform a reference for worker recruitment,thereby creating a high-quality worker ***,extensive simulations demonstrate ITTS outperforms two benchmark methods by increasing task completion rates by 56.49%-61.53%and profit by 42.34%-47.19%.

关键词： Industrial Internet of Things Insufficient workers Trust evaluation Social relation Task assignment

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Diabetic Retinopathy Detection using an Improved ResNet 50-InceptionV3 and hybrid DiabRetNet Structures 20

Diabetic Retinopathy Detection using an Improved ResNet 50-I...

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20th OITS International Conference on Information Technology, OCIT 2022

作者： Patra, Payel Singh, Tripty Department of Computer Science and Engineering Amrita School of Engineering India

ISBN: (纸本)9781665493482

Diabetic Retinopathy (DR) could be a mortal eye ailment that happens in people who have the disease named diabetics which hurts mainly on retina and after a long duration, it may lead to visual lacking. Diabetic Retinopathy Detection (DRD) through the integration of state of the art Profound Proficiency styles. This research used dataset, which was obtained from Eye Foundation Hospital Bangalore and Narayana Netralaya Bangalore, In this paper authors designed the frameworks within the field of profound Convolutional Neural Networks (CNNs), which have demonstrated progressive changes in numerous areas of computer vision counting therapeutic imaging, and researchers bring their control to the conclusion of eye fundus images. This proposed outline is combination of three stages. To begin with, the fundus picture is pre-processed utilizing an intensity of normalised procedure and augmented method. 2nd, the pre-processed picture is input to distinctive foundations of the CNN architecture in arrange to extricate a point vector for the evaluating process. 3rd, a classification is utilized for DRD and decides its review (e.g., no DR, mild, severe, moderate, or Proliferative Diabetic Retinopa-thy). A trained model with Resnet50, Inception V3, VGG-19, DenseNet-121 and MobileNetV2 architectures will extricate the Indus images of the eye. The outcome is coming with amazing exactness of 93.79 percentile, which is better by 7% than earlier work, by utilizing several activation functions in the new DiabRetNet architecture. © 2022 IEEE.

关键词： Convolutional neural networks

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Automating Optimal Healthcare Delivery Through Hyper Spectral Imaging in Sustainable Medical Environments

Automating Optimal Healthcare Delivery Through Hyper Spectra...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Seth, Jyoti Kavitha, R. Yadav, Dhyan Chandra Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Hyperspectral imaging (HSI) is a progressive imaging method being explored, which will aid in the automation of foremost healthcare transport in sustainable scientific environments. HSI requires an enormous variety of spectral bands compared to other spectral imaging methods, including visible mild imaging. This more statistics let in for a greater sure and correct evaluation of the spectral information within an image, presenting more remarkable sturdy outcomes while assessing the health of clinical situations. The information accrued thru HSI can be used to create automatic healthcare shipping models which could autonomously adjust and reply to changes in the clinical situation of an affected person. Similarly, this technology may be used to hit upon ailments, including cancer, offering a treasured perception of a patient's health and considering the selection of timely and appropriate treatments. Moreover, HSI can be used to create green and sustainable clinical surroundings, as it requires reduced personnel, area and fewer patient trips to and from the health facility. This technology can lower prices and improve patient consequences by providing automated selections for healthcare professionals. © 2024 IEEE.

关键词： autonomously Hyperspectral information progressive imaging

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Hyper-Space: Blockchain-based Decentralized Cloud Storage System 3

Hyper-Space: Blockchain-based Decentralized Cloud Storage Sy...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Rawat, Akshat Karande, Gaurav Balaji, Mukundan Bhamare, Vedant Jadhav, Poonam Sies Graduate School of Technology Department of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350348347

The emergence of cloud storage services have revolutionized the way we store, access, and share data. However, centralized cloud storage systems have inherent weaknesses such as security and privacy concerns, and a single point of failure. Decentralized cloud storage systems based on blockchain technology have emerged as a solution to these problems. In this paper, we present Hyper-Space, a blockchain-based decentralized cloud storage system that offers a secure, reliable, and efficient way to store and access data. Hyper-Space utilizes Swarm peer-to-peer network for data storage and retrieval, Polkadot blockchain network for metadata storage, and AES and ECC encryption algorithms for data security. The system is designed to enable renters to rent out their system's storage space, and clients to access the storage service through a smart contract signed by both parties. Our experimental results demonstrate that Hyper-Space is a promising solution for decentralized cloud storage systems. © 2023 IEEE.

关键词： Blockchain

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Improving Accuracy of Object Detection in Autonomous Drones with Convolutional Neural Networks

Improving Accuracy of Object Detection in Autonomous Drones ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Goyal, Himanshu Rai Shrivastava, Anurag Dixit, Krishna Kant Nagpal, Amandeep Reddy, B. Ravali Kumar, Jaysheel Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India GLA Universit Department of Electrical Engineering Mathura India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Bangalore India

ISBN: (纸本)9798331512965

Increasing use of autonomous drones in such areas as agriculture, disaster response and surveillance means that an effective and precise method of object recognition is becoming more important. In this study, we use Convolutional Neural Networks (CNNs) to bring additional precision to autonomous drone object detecting systems. While conventional machine learning approaches fail miserably in having complicated contexts and real time processing, CNN have sucessfully handles visual identification task. Based on state of the art methods being data augmentation and transfer learning, plus being real time with data augmentation of the raw data on the edge processor, this research introduces a convolutional neural network (CNN) model which has been fine-tuned and can be used for drones. The model is trained and tested on a big dataset of aerial photos acquired by drones. The item classification on this dataset varies from lighting conditions, to weather variables, etc. A considerable increase in the object detection accuracy is found from experimental findings in reducing false positives and improving resilience in dynamic contexts. Furthermore, the suggested model allows for detection with little latency, making it a good model for drones that must be deployed in real-time. Not only does this work contribute to bolstering rapidly growing autonomous drone navigation, it has great promise in environmental monitoring, SAR, and precision agriculture, and other applications. The components of the models are being researched on how scalable they are and how do they scale up on hardware that has limited resources. © 2025 IEEE.

关键词： Convolutional neural networks

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TROI: Cross-Subject Pretraining with Sparse Voxel Selection for Enhanced fMRI Visual Decoding

TROI: Cross-Subject Pretraining with Sparse Voxel Selection ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Ziyu Pan, Tengyu Li, Zhenyu Wu, Ji Li, Xiuxing Wang, Jianyong Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350368741

fMRI (functional Magnetic Resonance Imaging) visual decoding involves decoding the original image from brain signals elicited by visual stimuli. This often relies on manually labeled ROIs (Regions of Interest) to select brain voxels. However, these ROIs can contain redundant information and noise, reducing decoding performance. Additionally, the lack of automated ROI labeling methods hinders the practical application of fMRI visual decoding technology, especially for new subjects. This work presents TROI (Trainable Region of Interest), a novel two-stage, data-driven ROI labeling method for cross-subject fMRI decoding tasks, particularly when subject samples are limited. TROI leverages labeled ROIs in the dataset to pretrain an image decoding backbone on a cross-subject dataset, enabling efficient optimization of the input layer for new subjects without retraining the entire model from scratch. In the first stage, we introduce a voxel selection method that combines sparse mask training and low-pass filtering to quickly generate the voxel mask and determine input layer dimensions. In the second stage, we apply a learning rate rewinding strategy to fine-tune the input layer for downstream tasks. Experimental results on the same small sample dataset as the baseline method for brain visual retrieval and reconstruction tasks show that our voxel selection method surpasses the state-of-the-art method MindEye2 with an annotated ROI mask. © 2025 IEEE.

关键词： Computational neuroscience multi-modal learning region of interest analysis small sample learning

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TSN Experiments Using COTS Hardware and Open-Source Solutions: Lessons Learned

TSN Experiments Using COTS Hardware and Open-Source Solution...

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2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Rezabek, Filip Bosk, Marcin Carle, Georg Ott, Jorg Tum School of Cit Technical University of Munich Department of Computer Engineering Germany

ISBN: (纸本)9781665453813

Time-Sensitive Networking (TSN) brings deterministic behavior to Ethernet-based systems, resulting in hardware and software supporting various TSN standards. Using TSN-capable Commercial off-the-Shelf (COTS) hardware and open-source software brings several challenges. These are especially visible while performing performance evaluation of various TSN standards. In this work, we present the most significant challenges we faced using such deployments. Starting with the Precision Time Protocol, we observe its implementation being incompatible with that of the Time-Aware Priority Shaper. We present several solutions on how to overcome the identified behavior and compare them proposing best fitting solution for any setup. Next, we focus on the Network Interface Cards (NICs) and their behavior in presence of various TSN standards. We observe that the hardware offload features aiming to improve performance sometimes introduce performance artifacts worthwhile of investigation. Further, even though the Credit-Based Shaper configuration parameters can theoretically be computed for various NICs, due to the internal optimization of some, the calculated parameters may not hold. Our findings are intended to help the community improve observed results and solve challenges in using the COTS hardware and open-source software. We believe additional documentation detailing the implementation aspects of TSN standards in hardware would be beneficial in explanation of observed behavior. © 2023 IEEE.

关键词： Open systems

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Accessible e-News Summary Browser Extension for Visually Challenged Users 3

Accessible e-News Summary Browser Extension for Visually Cha...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Rajyalakshmi, Tamma Kuppusamy, K.S. School of Engineering and Technology Pondicherry University Department of Computer Science Puducherry India

ISBN: (纸本)9798350303360

The news media is a vital global information source, evolving from physical newspapers to digital platforms. Technology has revolutionized dissemination, ensuring broader accessibility. The main goal of news is to provide significant information and raise public awareness about a wide range of issues, notably jobs, healthcare, governmental initiatives, and alerts about natural disasters. This study delves into the challenges faced by individuals with visual impairments when accessing e-news online. The paramount goal is to ensure unbridled access to news for everyone, despite barriers. In this paper, We undertake a web accessibility evaluation of the homepages of 12 Indian news websites under three conformance levels of the WCAG 2.0 recommendation using AChecker and the WAVE Tool. Our evaluation is grounded in quantifying accessibility barriers. In particular, We address issues related to web accessibility and highlight the adverse effects of pop-up advertisements and automatic video play on news websites. We propose a browser extension for ad-free summaries, enhancing the news experience for the visually impaired. Evaluation insights guide ad format decisions, optimizing characteristics and placements on websites. © 2023 IEEE.

关键词： Websites

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People Counting in Public Spaces using Deep Learning-based Object Detection and Tracking Techniques

People Counting in Public Spaces using Deep Learning-based O...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Krishnachaithanya, N. Singh, Gurdit Sharma, Smita Dinesh, Rangisetti Sihag, Sumeet Ramsingh Solanki, Kamna Agarwal, Abhishek Rana, Mrinalini Makkar, Ujjwal School of Computer Science and Engineering Lovely Professional University Punjab Kapurthala India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India University Institute of Engineering and Technology Maharshi Dayanand University Rohtak Department of Computer Science and Engineering Haryana Rohtak India Lovely Professional University Department of Mechanical Engineering Punjab Kapurthala India

ISBN: (纸本)9798350323917

Recent advancements in deep learning and machine learning have enabled exact people counting in various applications including crowd management, security, and retail analytics. Deep learning algorithms have proven tremendous promise for accurate and efficient people counting in difficult contexts. This paper offers a technique for counting people that utilises deep learning with MobileNet SSD, centroid tracking, and trackable object script. Gathering and preparing a labelled dataset, training a MobileNet SSD model, implementing centroid tracking and the trackable object script, increasing system performance, testing it on real-world scenarios, and deploying it in a production environment are all part of the approach. The recommended approach provides a wide framework for creating and deploying a deep learning-based people-counting system that can be customized and tuned to match a number of applications and purposes. Additionally, we have added alerts on maximum capacity, timely scheduling and input feed from the internet. © 2023 IEEE.

关键词： Object detection

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