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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 1-19页

作者： Sarker, Prodip Kumar Zhao, Qingjie Uddin, Md. Kamal School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Bangladesh

In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE

关键词： Cameras

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Deep Neural Network Based Cardio Vascular Disease Prediction Using Binarized Butterfly Optimization

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Intelligent Automation & Soft Computing 2023年第5期36卷 1863-1880页

作者： S.Amutha J.Raja Sekar Department of Computer Science and Engineering Mepco Schlenk Engineering College(Autonmous)SivakasiIndia

In this digital era,Cardio Vascular Disease(CVD)has become the lead-ing cause of death which has led to the mortality of 17.9 million lives each *** Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent *** becomes inevitable to pro-pose a solution to predict the CVD with high accuracy.A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterﬂy Optimization Algorithm(DNN–BBoA)is *** BBoA is incorporated to select the best *** optimal features are fed to the deep neural network classiﬁer and it improves prediction accuracy and reduces the time *** usage of a deep neural network further helps to improve the prediction accu-racy with minimal *** proposed system is tested with two datasets namely the Heart disease dataset from UCI repository and CVD dataset from Kag-gle *** proposed work is compared with different machine learning classiﬁers such as Support Vector Machine,Random Forest,and Decision Tree Classiﬁ*** accuracy of the proposed DNN–BBoA is 99.35%for the heart dis-ease data set from UCI repository yielding an accuracy of 80.98%for Kaggle repository for cardiovascular disease dataset.

关键词： Deep neural network cardio vascular disease binarized butterﬂy optimization algorithm feature selection

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Human-computer Interaction Using Deep Fusion Model-Based Facial Expression Recognition System

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computer Modeling in engineering & sciences 2023年第5期135卷 1165-1185页

作者： Saiyed Umer Ranjeet Kumar Rout Shailendra Tiwari Ahmad Ali AlZubi Jazem Mutared Alanazi Kulakov Yurii Department of Computer Science&Engineering Aliah UniversityKolkata700156India Department of Computer Science and Engineering National Institute of TechnologySrinagarJammu and Kashmir190006India Department of Computer Science&Engineering Thapar UniversityPatiala147004India Computer Science Department King Saud UniversityRiyadh11451Saudi Arabia Department of Computer Engineering National Technical University of UkraineIgor Sikorsky Kyiv Polytechnic InstituteKyiv03056Ukraine

A deep fusion model is proposed for facial expression-based human-computer Interaction ***,image preprocessing,i.e.,the extraction of the facial region from the input image is ***,the extraction of more discriminative and distinctive deep learning features is achieved using extracted facial *** prevent overfitting,in-depth features of facial images are extracted and assigned to the proposed convolutional neural network(CNN)*** CNN models are then ***,the performance of each CNN model is fused to obtain the final decision for the seven basic classes of facial expressions,i.e.,fear,disgust,anger,surprise,sadness,happiness,*** experimental purposes,three benchmark datasets,i.e.,SFEW,CK+,and KDEF are *** performance of the proposed systemis compared with some state-of-the-artmethods concerning each *** performance analysis reveals that the proposed system outperforms the competitive methods in terms of various performance ***,the proposed deep fusion model is being utilized to control a music player using the recognized emotions of the users.

关键词： Deep learning facial expression emotions recognition CNN

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PROGRESSIVE VORONOI DIAGRAM SUBDIVISION ENABLES ACCURATE DATA-FREE CLASS-INCREMENTAL LEARNING 11

PROGRESSIVE VORONOI DIAGRAM SUBDIVISION ENABLES ACCURATE DAT...

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11th International Conference on Learning Representations, ICLR 2023

作者： Ma, Chunwei Ji, Zhanghexuan Huang, Ziyun Shen, Yan Gao, Mingchen Xu, Jinhui Department of Computer Science and Engineering University at Buffalo United States Computer Science and Software Engineering Penn State Erie United States

Data-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs). In this paper, we present iVoro, a novel framework derived from computational geometry. We found Voronoi Diagram (VD), a classical model for space subdivision, is especially powerful for solving the CIL problem, because VD itself can be constructed favorably in an incremental manner - the newly added sites (classes) will only affect the proximate classes, making the non-contiguous classes hardly forgettable. Furthermore, we bridge DNN and VD using Power Diagram Reduction, and show that the VD structure can be progressively refined along the phases using a divide-and-conquer algorithm. Moreover, our VD construction is not restricted to the deep feature space, but is also applicable to multiple intermediate feature spaces, promoting VD to be multilayer VD that efficiently captures multi-grained features from DNN. Importantly, iVoro is also capable of handling uncertainty-aware test-time Voronoi cell assignment and has exhibited high correlations between geometric uncertainty and predictive accuracy (up to ∼0.9). Putting everything together, iVoro achieves up to 25.26%, 37.09%, and 33.21% improvements on CIFAR-100, TinyImageNet, and ImageNet-Subset, respectively, compared to the state-of-the-art non-exemplar CIL approaches. In conclusion, iVoro enables highly accurate, privacy-preserving, and geometrically interpretable CIL that is particularly useful when cross-phase data sharing is forbidden, e.g. in medical applications. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Deep neural networks

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Millets Industry Dynamics: Leveraging Sales Projection and Customer Segmentation

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SN computer science 2024年第8期5卷 1063页

作者： Suhaas, K.P. Deepa, B.G. Shashank, D. Narender, M. Department of Information Science and Engineering The National Institute of Engineering Karnataka Mysuru India Department of Computer Science Christ University Karnataka Bangalore India Department of Computer Science and Engineering The National Institute of Engineering Karnataka Mysuru India

Millets delves into the dynamics of the millets industry, with a particular focus on sales projection and customer segmentation as strategic levers for growth. The research commences with an in-depth analysis of the millets market, encompassing production patterns, consumption trends, and emerging market opportunities. It explores the diverse range of millets varieties, their nutritional profiles, and the factors driving consumer preference. By understanding the market landscape, the study identifies key trends and challenges shaping the industry. A core component of this research is the development of a robust sales projection model. Employing advanced statistical and data-driven techniques, the model forecasts future sales based on historical data, market trends, and relevant economic indicators. The model incorporates factors such as consumer demographics, purchasing behavior, and competitive landscape to provide accurate and actionable insights. Customer segmentation is another critical aspect of the study. By applying clustering and profiling methodologies, the research identifies distinct customer segments based on factors such as age, income, dietary preferences, and purchasing habits. This segmentation enables a deeper understanding of customer needs and preferences, facilitating targeted marketing strategies and product development. The integration of sales projection and customer segmentation empowers businesses to make informed decisions, optimize resource allocation, and enhance overall market performance. By aligning product offerings and marketing efforts with customer segments, companies can achieve higher customer satisfaction, increased market share, and improved profitability. This research contributes to the growing body of knowledge on the millets industry by providing valuable insights into market dynamics, sales forecasting, and customer segmentation. The findings offer practical guidance for industry stakeholders, including farmers, processors

关键词： Customer Segmentation Forecast Millets Sales Projection

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Optimizing Round Robin Scheduling with DBSCAN Clustering and Machine Learning

Optimizing Round Robin Scheduling with DBSCAN Clustering and...

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2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, SPICSCON 2024

作者： Hossain, Md Mehrab Biva, Nanziba Khan Nahid, Nafiz Rifat, Ariful Islam Islam, Ashraful Department of Computer Science and Engineering Sylhet Engineering College Sylhet Bangladesh Department of Computer Science and Engineering Faridpur Engineering College Faridpur Bangladesh Department of Computer Science and Engineering American International University Dhaka Bangladesh Department of Computer Science University of Regina Regina Canada

ISBN: (纸本)9798331510213

In the intricate realm of operating systems, scheduling algorithms play a pivotal role in resource allocation and process completion, directly impacting overall system performance. The quest for an efficient and optimized scheduling algorithm is perpetual in the pursuit of optimal operating system utilization. The Round Robin algorithm, known for its robust time-sharing methodology, emerges as a stalwart in the scheduling context. Its versatility spans various domains, making it a preferred choice in both general-purpose computing environments and real-time systems. This study aims to elevate the performance of the Round Robin algorithm in terms of average waiting time and number of context switches without altering its core algorithmic structure. A novel approach is proposed, utilizing DBSCAN to group related processes and determine a more accurate and efficient time quantum. Departing from the conventional practice of using a single time quantum for the entire schedule, we divide the schedule into groups. The optimal time quantum for each group is derived through machine learning and deep learning algorithms, leveraging rigorous features extracted from the schedule. Notably, the LSTM model emerges as the top performer, achieving an impressive 97% accuracy. The proposed modified version consistently outshines its traditional counterpart. In 92% of cases, the modified version demonstrates superior performance in average waiting time while achieving a 96.5% improvement in context switching. Considering both metrics, the modified version showcases a notable enhancement in 87.8% of cases. This holistic assessment unveils a 5% reduction in average waiting time and a substantial 15% decrease in context switching, signifying a meaningful advancement in overall system performance. © 2024 IEEE.

关键词： Scheduling algorithms

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Adaptive Partial Task Offloading and Virtual Resource Placement in SDN/NFV-Based Network Softwarization

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computer Systems science & engineering 2023年第5期45卷 2141-2154页

作者： Prohim Tam Sa Math Seokhoon Kim Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

Edge intelligence brings the deployment of applied deep learning(DL)models in edge computing systems to alleviate the core backbone network *** setup of programmable software-defined networking(SDN)control and elastic virtual computing resources within network functions virtualization(NFV)are cooperative for enhancing the applicability of intelligent edge *** offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization,this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows,link delays,and allocatable bandwidth *** partial task offloading policy considered the DL-based recommendation to modify efficient virtual resource placement for minimizing the completion time and termination drop *** optimization problem of resource placement is tackled by a deep reinforcement learning(DRL)-based policy following the Markov decision process(MDP).The agent observes the state spaces and applies value-maximized action of available computation resources and adjustable resource allocation *** reward formulation primarily considers taskrequired computing resources and action-applied allocation *** defined policies of resource determination,the orchestration procedure is configured within each virtual network function(VNF)descriptor using topology and orchestration specification for cloud applications(TOSCA)by specifying the allocated *** simulation for the control rule installation is conducted using Mininet and Ryu SDN *** delay and task delivery/drop ratios are used as the key performance metrics.

关键词： Deep learning partial task offloading software-defined networking virtual machine virtual network functions

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Stacked autoencoder with weighted loss function for intrusion detection in IoT application

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Multimedia Tools and Applications 2024年 1-29页

作者： Gangula, Rekha Vutukuru, Murali Mohan Kumar, M. Ranjeeth Department of Computer Science and Engineering Vaagdevi Engineering College Telangana BollikuntaWarangal506005 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur522502 India School of Computer Science & amp Artificial Intelligence SR University Anantasagar Telangana Warangal506371 India

The fast increase of network traffic in recent times causes significant detection of intrusions in Internet of Things (IoT) environments. Currently, Deep Learning (DL) models play a crucial role in cyber security for malicious identification and intrusion detection in IoT networks. The existing methods have drawbacks like overfitting, data imbalance, and not completely capturing complex dependencies and relationships among input and features that are significant for intrusion detection. To overcome these limitations, Stacked Autoencoder (SAE) with weighted loss function is proposed for effective Intrusion Detection System (IDS). The SAE includes weighted loss function to minimize the overfitting issue and finally, the One Class-Support Vector Machine (OCSVM) is used in the classification layer to classify the intrusions. The database used for intrusion detection in IoT environment are Bot-IoT and ToN-IoT databases, which undergo pre-processing by using standard scalar and min–max normalization to remove duplicates and inconsistent data in the databases. Then, the optimal features are selected by using firefly optimizer which selects active features values for classification. In the resulting phase, the stacked autoencoder obtains 99.99%, and 99.70% of classification accuracy on the Bot-IoT and ToN-IoT databases, respectively, which are superior while compared to the traditional autoencoder model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Support vector machines

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Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks 4

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Ahmed, Marzia Mohamad, Ahmad Johari Hassan, Md Maruf Bhuiyan, Touhid Department of Software Engineering Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Malaysia Southeast University Department of Computer Science and Engineering Dhaka Bangladesh School of IT VA United States

ISBN: (纸本)9798331518882

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN's hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model's potential for accurate real-time air quality forecasting, offering a robust, sustainable solution. © 2025 IEEE.

关键词： Air Quality GBO Metaheuristic Neural Network Time series forecasting

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Enhancing drug discovery with AI: Predictive modeling of pharmacokinetics using Graph Neural Networks and ensemble learning

Intelligent Pharmacy

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Intelligent Pharmacy 2025年第2期3卷 127-140页

作者： Satheeskumar, R. Department of Computer Science and Engineering Narasaraopeta Engineering College Andhra Pradesh 522601 India

Accurately predicting pharmacokinetic (PK) parameters such as absorption, distribution, metabolism, and excretion (ADME) is essential for optimizing drug efficacy, safety, and development timelines. Traditional experimental methods are often slow and expensive, driving the need for advanced AI-based approaches in PK modeling. This study compares cutting-edge machine learning models, including Graph Neural Networks (GNNs), Transformers, and Stacking Ensembles, against traditional models like Random Forest and XGBoost, using a dataset of over 10,000 bioactive compounds from the ChEMBL database. The Stacking Ensemble model achieved the highest accuracy (R² of 0.92, MAE of 0.062), outperforming GNNs (R² of 0.90) and Transformers (R² of 0.89). These AI models excelled in capturing complex molecular interactions and long-range dependencies, significantly improving PK predictions. The high accuracy achieved (R² = 0.92) by the Stacking Ensemble method indicates that AI models can streamline the drug discovery process by reducing costly in vivo experiments, enabling faster go/no-go decisions during preclinical evaluations, and ultimately accelerating the development of new therapeutics. This reduction in time and cost could facilitate broader industry adoption of AI-driven PK modeling. Furthermore, Bayesian optimization was employed to fine-tune hyperparameters, further enhancing the performance and robustness of these predictive models. © 2024 The Authors

关键词： Drug discovery Graph neural networks (GNNs) Machine learning models Pharmacokinetics (PK) Stacking ensemble

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