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Person re-identification using vision transformer and centroid triplet loss

Multimedia Tools and Applications 2024年第29期83卷 73777-73788页

作者： Ijjina, Earnest Paul Medipelly, Rampavan Beerukuri, Santosh Kumar Vinnakota, Sowmya Nelakurthi, Vijay Chowdary Department of Computer Science and Engineering National Institute of Technology Warangal Telangana Warangal506004 India

In the current digital era, video surveillance has become a part of daily life. The person re-identification(re-ID) task involves choosing a person as a target in one camera feed and recognizing that target in footage from a different camera or the same camera at various points in time. The goal is to accurately identify a person despite variations in their appearance due to changes in pose, illumination, and occlusions. Person re-ID is a key component in video surveillance with practical applications in public safety, retail, and transportation, among others. However, it remains a difficult problem due to the inherent variability in appearance and the lack of robust features to capture the subtle differences between individuals. Even the existing Convolution Neural Networks (CNNs) based approaches for person re-ID task struggle to address the issues due to variations in pose, occlusions, and background clutter. To tackle these issues in person re-ID task, we propose an approach using Vision Transformers (ViT) with Centroid Triplet Loss (CTL). Experimental studies conducted on Market1501 and DukeMTMC datasets, yielded better results than the existing approaches, indicating the effectiveness of our approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cameras

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Monitoring the crowd emotion using valence and arousal of crowd based on prominent features of crowd

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Signal, Image and Video Processing 2025年第7期19卷 1-12页

作者： Manojkumar, K. Suji Helen, L. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

Analysing the crowd behaviour is a potential research component for ensuring societal safety by consistent video surveillance and management through officials. Public safety management is an inevitable process in developing and developed countries and possess a strong relationship with individual over a crowd behaviour. However, the challenges are doubled when the applications attempt to monitor or predict the crowd behaviours. The standard emotional cues utilized by the conventional machine learning algorithms highly rely on facial features, motion and gestures of the participating members in a crowd. The article concentrates on a novel approach to determine the crowd emotions through provocations captured due to emotional valence. The different features are combined in a fuzzy reasoning model to emphasize on the facial recognition of every individual in a crowd, along with other primary features of harmful substances or objects for measuring the public safety metrics. The common parameters considered from the crowd are magnitude, variance, confusion index, density of the crowd, along with provocations. Provocations of the crowd are measured based on the enthalpy metric and magnitude of the crowd. Density of the crowd, motion, trajectory and the confusion index are processed as the inputs of the reinforcement learning algorithm. The outcomes of the model described the probability of provocations and valence derived from the fuzzy reasoning of the crowd motion, emotions, and thus highlights the scenes with potentially harmful situations. The entire model was constructed using a DarkNet model in a Convolutional Neural Network with YOLOv8 and the simulation results have showcased that the proposed model outperforms the conventional approaches of crowd emotion sensing. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Reinforcement learning

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Comparing HAProxy Scheduling Algorithms during the DDoS Attacks

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 139-142页

作者： Kumar, Anmol Somani, Gaurav Agarwal, Mayank Indian Institute of Technology Patna Department of Computer Science and Engineering Patna800013 India Central University of Rajasthan Department of Computer Science and Engineering Ajmer305817 India

Web-services have become most common IT enablers today. Cyber attacks such as the distributed denial of service (DDoS) attacks pose availability concerns which may result into service outages and consequently financial and reputation losses. Load balancing software such as HAProxy (high availability proxy) is an important building block of the Web service delivery today. Load balancing software provides incoming request distribution among Web-servers and admission control which may even help with combating DDoS attacks. In this letter, we study and explore the performance of different load distribution or scheduling strategies in the presence of DDoS attacks. With the help of attack experiments, we find that First, HDR, Source, and URI scheduling algorithms performs best for both static and Poisson arrivals. © 2019 IEEE.

关键词： Scheduling algorithms

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration technology

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Chinese Journal of Electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

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Intelligent Automation & Soft Computing 2023年第3期35卷 2893-2907页

作者： C.Santhosh Kumar K.Vishnu Kumar Department of Computer Science and Engineering Priyadarshini Engineering College635751TamilnaduIndia Department of Computer Science and Engineering KPR Institute of Engineering&TechnologyCoimbatore641407India

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare *** patient data pro-cessing from remote places may lead to severe privacy ***,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to ofﬂoading of live patient data to remote cloud *** the privacy *** proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption *** can help maintain the privacy preservation and conﬁdentiality of patients’medical data during diagnosis of Parkinson’s *** addition,the energy and delay aware computational ofﬂoading scheme is proposed to minimize the uncertainty and energy consumption of end-user *** proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.

关键词： Peer-to-peer computing energy and delay aware ofﬂoading edge-cloud enabled healthcare system parkinson’s disease prediction

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Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

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Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte Kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology School of Computer Science and Engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Dopamine MoS_2 Biosensor Multiwalled carbon nanotubes Machine learning Complex mixture

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Marine Life Ecosystem Analysis Based on Climate Change Detection Using Deep Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 545-554页

作者： Rebecca, B. Sandhya, A. Pokkuluri, Kiran Sree Kanishka, Gunipati Shaik, Khasimbee Krishna, B. Murali Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Dundigal 500043 India Department of CSE SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India Computer Science and Engineering (Cyber Security) Madanapalle Institute of Technology & Science Madanapalle India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The surrounding environmental and climatic conditions have a significant impact on the utilisation of ecosystem services for recreational purposes. Climate change poses a threat to future natural leisure opportunities because of this reliance. The way society functions is being challenged by climate change and significant adaptation will probably be needed to deal with future changes in weather patterns. Machine learning (ML) methods have advanced to the point that they are now being offered as a tool to support climate studies, in addition to being the source of advances in other fields of study. This research proposes a novel technique in marine life ecosystem–based climate change detection using deep learning model. Input is collected as climate change detection–based marine life ecosystem and processed for noise removal with normalisation. Then this data has been segmented and classified using watershed semantic fuzzy component analysis with quantile shrinkage convolutional regression model. The experimental analysis has been carried out in terms of training accuracy, specificity, recall, AUC and NSE. Suggested technique achieved training accuracy 98%, specificity 96%, NSE 45%, recall 95% and AUC 97%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change detection Convolutional regression Deep learning Fuzzy component analysis Marine life ecosystem

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Quadratic and Lagrange interpolation-based butterfly optimization algorithm for numerical optimization and engineering design problem

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Soft Computing 2025年第1期29卷 157-194页

作者： Sharma, Sushmita Saha, Apu Kumar Chakraborty, Sanjoy Deb, Suman Sahoo, Saroj Kumar Department of Computer Science and Engineering Indian Institute of Information Technology Tripura Agartala India Department of Computer Science and Engineering National Institute of Technology Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India

This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques with adapted parameter settings to enhance the search strategy of BOA and to achieve a better balance of diversification and intensification. LQBOA’s performance was examined on 45 traditional benchmark issues as well as the IEEE CEC 2017 benchmark suites with 10, 30 and 50 dimensions, and the results were compared against popular state-of-the-art and modified algorithms. It was discovered that in more than 85% of cases, the proposed LQBOA outperformed the compared algorithms. The Friedman rank test and Wilcoxon rank test were used to validate the suggested LQBOA’s rank and significance. Additionally, convergence and diversity analyses were performed to investigate its convergence speed and searching behavior, respectively. Furthermore, LQBOA has been successfully deployed to solve twelve real-world issues including several engineering design problems and two multiple gravity assist spacecraft trajectory problems. The results of these problems were compared to those of a wide range of algorithms, demonstrating the superior performance of the proposed LQBOA. In conclusion, LQBOA makes a significant addition to the optimization area, and its application could greatly improve the performance of numerous optimization jobs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Lagrange multipliers

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Autism spectrum disorder based on squeezenet with fractional tasmanian rat swarm optimization

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Multimedia Tools and Applications 2024年第41期83卷 89029-89054页

作者： Muppidi, Satish Anuradha, G. Valarmathi, K. Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Kanuru India Department of Computer Science and Engineering Panimalar Engineering College Bangalore Trunk Road Chennai Pooñamallee600123 India

A complicated neuro-developmental disorder called Autism Spectrum Disorder (ASD) is abnormal activities related to brain development. ASD generally affects the physical impression of the face as well as the growth of the brain in children. An early and proper medical diagnosis is essential for ASD affected children to enhance their quality of life. However, the clinical detection of ASD is a difficult task and time-consuming, hence it is essential to design an ASD detection approach for precise diagnosis of ASD children. In this research, an algorithmic approach called Fractional Tasmanian Rat Swarm Optimization driven SqueezeNet (FTRSO-SqueezeNet) is designed for the detection of ASD. The median filter and Region of Interest (RoI) extraction are used to de-noise the input image initially and extract a particular region from the filtered image. Later, the nub regions are extracted by choosing the optimal grid from the pre-processed image and using different feature extractors the features from the input images are determined. Finally, the detection of ASD is done by using SqueezeNet, which is trained using the FTRSO approach. The performance of the method is estimated, where the designed model achieved higher performance with an accuracy of 94.55%, sensitivity of 92.53%, and specificity of 95.22% than other prevailing approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Rats

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Adaptive Attention Mechanisms Based Multicriteria Recommender Systems for Improved User Preference Discovery

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SN computer science 2025年第4期6卷 1-16页

作者： Rajput, Ishwari Singh Tewari, Anand Shanker Tiwari, Arvind Kumar Department of Computer Science & Engineering National Institute of Technology Patna Bihar Patna India Department of Computer Science & Engineering Kamla Nehru Institute of Technology Sultanpur (UP) India

Personalized recommender systems are becoming more popular to reduce the issue of information overload. It is also observed that the recommendations provided by multi-criteria recommender system (MCRS) are more accurate and precise in comparison to conventional single-criteria recommender systems. MCRS incorporates customers’ preferences based on multiple criteria while recommending any item. Recent research has shown that MCRS can efficiently use implicit and explicit input across various dimensions to make exact recommendations across multiple domains. Due to use of multiple criteria, existing MCRS methods often struggle with effectively aggregating and interpreting these criteria, making it hard to accurately determine user preferences. In this article, we introduce a novel adaptive attention mechanism that dynamically assigns weights to each criterion based on user preferences. Each criterion is initially weighted based on its importance to a user. An attention mechanism is used to understand the exact user’s preference for each criterion. Later, the nearest-neighbor method finds users with similar interest to predict unknown ratings which is further used for Top-N recommendations. The performance of the proposed approach (AM-MCRS) is validated on Yahoo! movies multi-criteria dataset having 1716 unique users and 965 different movies and a comparative analysis is performed with existing single and multi-criteria recommender systems. The results clearly show that the AM-MCRS methodology outperform baseline methods, achieving the highest precision (0.9412), F-score (0.9343), accuracy (0.929), and coverage (0.9407), while also observing a significant reduction in MAE (0.7623) and RMSE (0.9145). These findings highlight the capability of AM-MCRS to improve recommendation accuracy and dependability across various domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Attention mechanism Deep learning Dynamic user preference Multi-criteria recommender system Nearest-neighbor

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