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Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zhou, Renjie Jin, Zhenming Wan, Jian Zhang, Jilin Guo, Xingyu Liu, Qiang Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou China Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Itmo Joint Institute Hangzhou China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou China Zhejiang Police College Dept. of Computer and Information Technology Hangzhou China

ISBN: (纸本)9798350304855

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user's shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods. © 2023 IEEE.

关键词： Forecasting

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High-Capacity, A Secret Video Sharing Using DM-Stego and Genetic Algorithm

High-Capacity, A Secret Video Sharing Using DM-Stego and Gen...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Logeshwari, R. Velswamy, Rajasekar Subhashini, R. Karunakaran, V. Srm Institute of Technology Department of Network and Communication Engineering Chennai Kattankulathur India Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Information Technology Salem India Sri Eshwar College of Engineering Dept. of CSE-AI and Ml Coimbatore India

ISBN: (纸本)9798350300857

Electronic media knowledge is unprecedently increasing in recent years. In almost all security control areas, traffic control, weather monitoring, video conferences, social media etc., videos and multimedia data analysis practices are used. As a consequence, it is necessary to retain and transmit these data, by considering the security and privacy issues. IN this research study, a new Div-Mod Stego algorithm is combined with the Multi-Secret Sharing method along with temporary frame reordering and Genetic algorithm to implement high-end security in the process of video sharing. The qualitative and quantitative analysis has also been carried out to compare the performance of the proposed model with the other existing models. A computer analysis shows that the proposed solution would satisfy the requirements of the real-time application. © 2023 IEEE.

关键词： Genetic algorithms

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Plant Leaf Disease Detection Incorporating IoT and XAI-Enhanced Deep Learning

Plant Leaf Disease Detection Incorporating IoT and XAI-Enhan...

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International Conference on computer and Information technology (ICCIT)

作者： Pijush Kanti Roy Partho Md. Abid Hasan Rafi Pankaj Bhowmik Dept. of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Dept. of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Plant diseases represent a significant global threat, affecting both food supply and economic growth. Despite technological advances, early plant disease recognition remains difficult for small-scale farmers because of the lackings of infrastructure and resources. Thus, early disease detection using machine learning, deep learning, and computer vision techniques can improve the prediction process and make it easier. Previous studies that were implemented for plant-specific purposes, require high-resolution images and lack real-time detection methods. Therefore, to address these issues, we proposed a deep learning and IoT-based system that uses convolutional neural networks (CNN) and other supporting models, such as InceptionNetV3, VGG16, and EfficientNetB5, with a fine-tuning process to achieve higher accuracy in predicting 38 diseases over 14 different plant species in lower resolution, noisy data. Besides, to facilitate the system, we incorporated an ESP32CAM microcontroller and a server host that can be used to detect diseases in a real-time environment, which makes our research study robust and meaningful. We also implemented Explainable Artificial Intelligence (XAI), particularly Grad-CAM, along with our CNN model for visualizing the infected region in a plant leaf. The individual-trained models achieved an average accuracy of around 99%. We believe, our study can contribute to the agricultural sector by providing real-time disease detection, smart decision support systems, and low-cost solutions for farmers, mainly in resource-limited territories.

关键词： Deep learning Plant diseases Accuracy Microcontrollers Explainable AI Computational modeling Biological system modeling Real-time systems Convolutional neural networks Internet of Things

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Latency-Minimized Computation Offloading in Vehicle Fog Computing with Improved Whale Optimization Algorithm

Latency-Minimized Computation Offloading in Vehicle Fog Comp...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Xue, Xiangdong Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Fog computing provides lower latency and higher bandwidth compared to cloud computing and is widely used in Internet of Vehicles (IoV). Vehicles cannot compute all tasks locally due to their limited computing power and battery capacity. Thus, it is a useful way to offload some tasks of vehicles to other resource-rich servers. However, due to the high mobility of vehicles, there may be a failure of returning computing results. Thus, it is a challenge to minimize the latency of tasks while meeting the constraint of energy consumption. This work proposes a vehicle-fog offloading system that offloads tasks to fog servers or idle vehicles which is a probabilistic offloading problem. So this work proposes an improved optimization algorithm called an adaptive Lévy flight-based Whale optimization algorithm with Hierarchical learning (LWH) to solve this problem. Simulation experiments show that LWH has strong global search capability and outperforms its five typical and widely used algorithms. © 2023 IEEE.

关键词： computation offloading fog computing Lévy flight Vehicular networks whale optimization algorithm

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A Real-Time Performance Model for NFT Chains

A Real-Time Performance Model for NFT Chains

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Blockchain Computing and Applications (BCCA), International Conference on

作者： K. Kentner J. Seol N. Park Computer Science Dept. Oklahoma State University Stillwater OK USA Information Technology Dept. Middle Georgia State University Macon GA USA Dept. of Computer Science Oklahoma State University Stillwater OK USA

This paper sets forth a bivariate, real-time NFT (nonfungible token) model based on a Markovian queueing process. This model is proposed with the goal of simulating and observing the performance of an NFT system as it behaves under real-time constraints, as well as to see how various on-/off-chain transaction rates, service rates, blocksizes, and deadline-miss rates affect the overall performance of the system. The system is assumed to operate at a steady state, thus allowing for each probabilistic state (which at a given time consists of ion-chain transactions and j off-chain transactions that have arrived) to be calculated using balance equations (all incoming transactions to a state equal all outgoing from the state when the relevant probabilities are considered). The Markovian assumption necessitates that the full state space must sum to 1.0, and because all states derive from $\mathrm{p}_{0,0}$ , P0,0 may be factored out and solved using this constraint. This in turn facilitates the computation of the remaining probabilities. The on-/off-chain transaction rates $\lambda_{on}$ and $\lambda_{off}$ , service rate $\mu$ , and deadline-miss rate $\lambda_{F}$ are the primary parameters within the model, and $\lambda_{F}$ is the key parameter for modeling the real-time behavior of the system $(\lambda_{F}$ represents a failure for a transaction to meet the deadline, and it causes the current state to self-loop). An analysis of the system via metrics including average wait time, average number of transactions, and throughput is presented, and the implications of the simulation on the overall performance of the system are discussed.

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Explainable AI-Driven Vision Transformers for Assessing Fruit Freshness via Transfer Learning

Explainable AI-Driven Vision Transformers for Assessing Frui...

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International Conference on computer and Information technology (ICCIT)

作者： Aritra Das Hadiur Rahman Nabil Fahad Pathan Momotaz Rahman Ouishy M. F. Mridha Jungpil Shin Dept. of Computer Science American International University-Bangladesh Dhaka Bangladesh Institute of Information and Communication Technology Shahjalal University of Science and Technology Sylhet Bangladesh School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

The assessment of fruit freshness is a critical factor in ensuring the quality and safety of food products, impacting both consumer satisfaction and market value. This paper presents a comparative evaluation of Vision Transformer (ViT) models for assessing fruit freshness using transfer learning techniques. Three pretrained ViT models—ViT model A (vit_base_patch16_clip_384), ViT model B (vit_betwixt_patch16_reg4_gap_384), and ViT model C (vit_mediumd_patch16_reg4_gap_384)—were employed on two publicly available datasets: Dataset A from the Mendeley Data Repository and Dataset B from the Kaggle Repository. Data augmentation techniques were applied to enhance the generalization capabilities of the models. Superior performance was exhibited by ViT model B on Dataset A, with an accuracy of 98.44%, precision of 98.46%, an F1-score of 98.44%, and a recall of 98.44%. Again, exceptional results were achieved by ViT model B on Dataset B, reaching an accuracy of 99.57%, precision of 99.56%, an F1-score of 99.56%, and a recall of 99.56%. The effectiveness of ViTs in accurately classifying fruit freshness is underscored, demonstrating their potential for practical applications in food quality assessment. Later, Local Interpretable Model-agnostic Explanation (LIME) was incorporated to improve the interpretability of the model.

关键词： computer vision Accuracy Computational modeling Transfer learning Customer satisfaction Transformers Data augmentation Data models Real-time systems Quality assessment

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DViTPolyp: Small Polyp Detection in Colonoscopy Videos based on Deep Vision Transformer 4

DViTPolyp: Small Polyp Detection in Colonoscopy Videos based...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Ma, Ming Xia, Likun Seo, Hyunseok Katz School of Science and Health Yeshiva University Dept. of Graduate Computer Science and Engineering New York United States College of Information Engineering Capital Normal University Beijing China Biomedical Research Institute Korea Institute of Science and Technology Seoul Korea Republic of

ISBN: (纸本)9798350395914

Early detection of colorectal polyps and removal play a crucial role in the prevention of colon cancer. computer-aided detection (CAD) methods can be employed to help endoscopists reduce the polyp miss-rates. Numerous automatic polyp detection methods have been explored. However, it is still challenging to automatically detect colon polyps in small size in colonoscopy videos. This paper presents a method termed DViTPolyp for detecting small colorectal polyp in colonoscopy videos. The proposed method leverages the power of the deep vision transformer as the detection head in the network to aggregate rich global features, and uses the re-attention mechanism to regenerate attention maps to capture the features from the backbone model and DViT block. Also, we propose to use a power regularized Intersection Over Union (IoU) as the loss function to further enhance the accuracy of detection. Experiments are conducted on a mix of several public polyp datasets. Comparative results show that our proposed method has a better performance than other methods. © 2024 IEEE.

关键词： colonoscopy videos loss function polyp detection vision transformer

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A Smart Surveillance System for Pedestrian Tracking and Counting using Template Matching 4

A Smart Surveillance System for Pedestrian Tracking and Coun...

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4th International Conference on Robotics and Automation in Industry, ICRAI 2021

作者： Shahzad, Ali Raza Jalal, Ahmad Air University Dept. of Creative Technology Islamabad Pakistan Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9781665423434

Human Detection and tracking have become a focal area of research as it plays a major role in computer vision applications. Methods and equipment for the detection and tracking of humans are constantly being changed as advanced methodologies are being discovered. Even though this is a huge topic in the computer vision field, pedestrian detection in infrared images is relatively new. In this Research paper, we designed a new methodology for pedestrian detection, tracking, and counting in infrared images. The main objective of our research is to give machines a technique for the detection of pedestrians in real-time. Our proposed method of human detection detects and tracks pedestrians effectively and efficiently using the OCTVBS benchmark Pedestrian Infrared/visible Stereo Video Dataset. First, we used Template matching for Pedestrian detection. After detections tracking is done using Kalman filter. Lastly, headcount is acquired from head detections gained from applying Haar-like features. We evaluated our method on the OCTVBS dataset and gained detection accuracy of 91.04%, tracking accuracy of 92.47%, and count accuracy of 82.11%. The presented method should be useful for the monitoring of pedestrians in IR video surveillance. © 2021 IEEE.

关键词： Template matching

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An Evaluation and Comparison for Phishing Attack Detection using Machine Learning Approaches

An Evaluation and Comparison for Phishing Attack Detection u...

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Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 International Conference on

作者： Ajeet Kumar Sharma Anushree Nitin Rakesh Pawan Kumar Verma Dept. of Computer Science & Engineering School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Dept. of Computer Engineering & Applications GLA University Mathura Uttar Pradesh India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

The persistent and evolving threat of phishing attacks demands effective and adaptive detection techniques. This research paper presents a comprehensive evaluation and comparison of various machine learning approaches to detect phishing attacks. We investigated five prominent algorithms: Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naive Bayes, and Extreme Gradient Boosting (XGBoost), to determine their efficacy in identifying phishing activities. Our methodology involved a systematic analysis using a large dataset of phishing and legitimate URLs, where each model was trained, tested, and validated to ensure robustness and reliability. The performance of each algorithm was assessed based on accuracy, precision, recall, and F1 score. Among the evaluated models, XGBoost demonstrated superior performance, achieving an exceptional accuracy of 99.75%. This result underscores the potential of XGBoost in phishing attack detection, offering a promising tool for cybersecurity applications.

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Cost-Minimized Partial Computation Offloading in Cloud-Assisted Mobile Edge Computing Systems

Cost-Minimized Partial Computation Offloading in Cloud-Assis...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, smart mobile devices (SMDs) support various computation-intensive and delay-sensitive applications, e.g., online games, and figure compression. However, SMDs have limited computing resources and battery energy and cannot execute all tasks of the above applications in a real-time manner. Cloud computing provides enormous computing resources and energy that can easily execute tasks offloaded from SMDs. However, could data centers (CDCs) are often located in remote sites, which leads to long transmission time. Small base stations (SBSs) offer high-bandwidth and low-latency services for SMDs, which solves the problem of cloud computing. However, it becomes a challenge to achieve the lowest cost in such a heterogeneous architecture including multiple SMDs, SBSs, and the CDC while meeting delay requirements of tasks. This work proposes a cost-minimized computation offloading strategy to minimize the total cost of the system. A constrained optimization problem is first formulated based on the hybrid architecture. Afterward, a two-stage optimization algorithm called a Lévy flights and Simulated Annealing-based Grey wolf optimizer (LSAG) is developed to optimize the total cost of the system. In the first stage, the optimal edge selection policy is determined given multiple available SBSs. In the second stage, task offloading and resource allocation among SMDs, SBSs, and the cloud are determined. Experiments with real-life tasks prove that LSAG significantly achieves lower cost with faster convergence speed than state-of-the-art peers. © 2023 IEEE.

关键词： cloud computing computation offloading intelligent optimization algorithms Lévy flights Mobile edge computing

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