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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Singh, Baldev Kavita Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9798350399264

The Internet's crucial domain name system is in charge of translating domain names into IP addresses. However, because of its dispersed structure with a hub, it is subject to significant security concerns. Since the creation of Bitcoin, blockchain has drawn a lot of attention owing to its decentralized, trustworthy, and secure features. In this article, we suggest SSHTDNS, a brand-brand-new domain name system built on the blockchain. SSHTDNS selects the consortium chain form to ensure the management of Top-Level Domain names, and these consortium nodes use linkable ring signature technology for identity anonymity and vote fairness. Shading protocol is used in multi-chain structures to increase the system's scalability and throughput. A domain name system that combines these methods can be safe, scalable, and high-throughput. © 2023 IEEE.

关键词： Blockchain

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Multi-Modal Emotion and Gesture Recognition System for Humans and Animals Using computer Vision and Deep Learning 5

Multi-Modal Emotion and Gesture Recognition System for Human...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Shivaswamy, Smitha Aruna, S. Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Application Karnataka Mangaluru India Acharya Institute of Technology Department of Computer Applications Bengaluru India School of Science and Computer Studies CMR University Bangalore India Hyderabad India

ISBN: (纸本)9798331518097

Emotion recognition through facial expressions and gesture analysis has emerged as a key area of research in computer vision and human-computer interaction. The ability to automatically detect and classify human emotions has wide-ranging applications, from enhancing user experiences to improving mental health monitoring and surveillance systems. This project builds on this background to develop a comprehensive system for recognizing emotions and gestures in both humans and animals. The system integrates facial emotion recognition, hand gesture interpretation, and animal emotion detection into a single platform. Using advanced deep learning models and computer vision libraries, the system classifies facial expressions into categories such as happiness, sadness, anger, fear, surprise, and neutral. The gesture recognition component interprets hand signals to convey messages like 'need help' or 'need water.' An innovative aspect of this work is the extension of emotion recognition to animals, particularly focusing on species like dogs and monkeys. Implemented primarily in Python, the system leverages libraries such as OpenCV and DeepFace. By combining these different modalities of nonverbal communication analysis, the project presents a versatile solution with potential applications in human-computer interaction, mental health monitoring, security, and animal welfare. © 2024 IEEE.

关键词： Gesture recognition

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Resource Management allocation in the CC Environments with Multi-Objective Optimization system 3

Resource Management allocation in the CC Environments with M...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Bhambu, Pawan Kaur, Gursimran Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9798350399264

These days, the topic of cloud computing has become a very popular paradigm for providing services online. For the cloud data center to deliver cloud infrastructure services in response to user demand, a set of virtualized resources are required. In order to lower the degree as a result of resource loss and SLA violations, the necessity for optimum resource management becomes a crucial problem in cloud data centers. These issues are classified as NP-hard, and meta heuristics approaches are well suited to solving them. The article develops a multi-objective hybrid fruit fly optimization (MOHFO)-based method for SLA-conscious dynamic resource management in cloud data centers. For the fruit fly optimization technique, the Bald Eagle Search (BES) optimization behavior search capabilities. To balance resource waste and SLA breaches, the proposed solution employs dynamic consolidation and deployment of virtual machines (VMs) mechanism. The suggested technique utilizing Clouds to simulate different data center designs, using QoS measures to assess the experimental outcomes. The proposed MOHFO technique is assessed and contrasted to existing optimization strategies in terms of resource wastage, energy use, communication costs, and migration frequency in order to provide cloud computing with QoS-aware optimal resource provisioning. © 2023 IEEE.

关键词： Virtual machine

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Weakly Supervised Object Localization with Background Suppression Erasing for Art Authentication and Copyright Protection

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Machine Intelligence Research 2024年第1期21卷 89-103页

作者： Chaojie Wu Mingyang Li Ying Gao Xinyan Xie Wing W.Y.Ng Ahmad Musyafa School of Computer Science and Engineering South China University of TechnologyGuangzhou 511442China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's HospitalGuangzhou 510180China

The problem of art forgery and infringement is becoming increasingly prominent,since diverse self-media contents with all kinds of art pieces are released on the Internet every *** art paintings,object detection and localization provide an efficient and ef-fective means of art authentication and copyright ***,the acquisition of a precise detector requires large amounts of ex-pensive pixel-level *** alleviate this,we propose a novel weakly supervised object localization(WSOL)with background su-perposition erasing(BSE),which recognizes objects with inexpensive image-level ***,integrated adversarial erasing(IAE)for vanilla convolutional neural network(CNN)dropouts the most discriminative region by leveraging high-level semantic ***,a background suppression module(BSM)limits the activation area of the IAE to the object region through a self-guidance ***,in the inference phase,we utilize the refined importance map(RIM)of middle features to obtain class-agnostic loc-alization *** experiments are conducted on paintings,CUB-200-2011 and ILSVRC to validate the effectiveness of our BSE.

关键词： Weakly supervised object localization erasing method deep learning computer vision art authentication and copyright protection

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Enhanced Electrocardiogram Data Compression and Classification Using 2D Convolutional Neural Networks 5

Enhanced Electrocardiogram Data Compression and Classificati...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Yadav, Amrendra Singh Mehta, Preeti Mishra, Deepti Barik, Rabindra Kumar AVM-Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India National Institutes of Technology Delhi Department of Computer Science and Engineering Delhi India Kiit Deemed to be University School of Economics and Commerce Bhubaneshwar India Kiit Deemed to be University School of Computer Application Bhubaneshwar India

ISBN: (纸本)9798331522100

The task of electrocardiogram (ECG) classification is a combination of data compression and classification of signals based on the patient's health. This paper introduces a novel technique for compressing electrocardiogram (ECG) images and utilizing these compressed images for accurate classification. We transform ECG signals into images and compress them to reduce their size while preserving classification accuracy significantly. Our approach employs a 2-dimensional Convolutional Neural Network (2-D CNN) model and achieves an impressive accuracy of 97% even after compressing the images by up to 76%. The proposed technique was tested on the widely-used MIT-BIH Arrhythmia Dataset, demonstrating its robustness and efficacy. Maintaining high classification accuracy despite significant image compression is crucial for efficient storage and transmission in resource-constrained environments. This method holds substantial potential for real-time monitoring and diagnosis in telemedicine applications, emphasizing the importance of our contribution to the field. © 2025 IEEE.

关键词： Electrocardiography

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Prediction of Fetal Brain Anomalies Using Deep Learning Models 5

Prediction of Fetal Brain Anomalies Using Deep Learning Mode...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Subburaj, Venkateshwari Pandiyarajan, Pandiselvam School of Computing Department of Computer Application Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India School of Computing Department of Computer Science and Engineering Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India

ISBN: (纸本)9798331518097

Fetal brain anomaly prediction is important for fetal medicine, as well as for prenatal health care. Fetal anomalies are classified into two types, anomalies in the fetus' body parts including heart, lung, and kidney anomalies in facial features. Thus, to predict the anomalies in the fetal brain, various deep-learning models are used in this paper. The LeNet, AlexNet, ResNet, VGGNet, GoogLeNet and ZFNet were used. From the results obtained, the ResNet method achieved 99% accuracy with the given 3D ultrasound images of the fetal brain compared with other models and benchmark algorithms like SVM, Logistic regression, and KNN. The results of this paper are useful to identify fetal anomalies. Hence, this approach will be useful for improving outcomes for individuals and society as much as possible. © 2024 IEEE.

关键词： Prediction models

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Comparative Evaluation of Machine Learning Algorithms for Detecting Credit Card Fraud 8

Comparative Evaluation of Machine Learning Algorithms for De...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Prashanth, Banagani Gangadhar, Suddapalli Bhardwaj, Vivek Reddy, Kovvuru Diwakar Reddy, Puli Shiva Mehra, Manik Lovely Professional University Department of Computer Science and Engineering Phagwara India Manipal University Jaipur Department of Computer Science and Engineering Jaipur India Lovely Professional University School of Computer Application Phagwara India

ISBN: (纸本)9798350396638

Detecting credit card fraud is an extremely important issue in the financial sector, as it results in significant financial losses and negatively impacts customer trust. The main motive of CCFD is to develop methods to identify fraudulent transactions accurately and efficiently. This research typically involves the use of statistical and machine-learning algorithms to analyze large amounts of transaction data. Utilizing machine learning is a potential solution for addressing credit card fraud. Studies suggest that machine learning techniques can help overcome the challenges of identifying fraudulent transactions with high detection rates, both directly and indirectly. While supervised ML algorithm and unsupervised ML algorithms have been proposed, the limitations of each approach underscore the need for hybrid methods. From the results, it is evident that Logistic Regression has the highest accuracy of 94.86%, highest recall value of 98.97% and highest F1-score of 95.09%. © 2023 IEEE.

关键词： Feature extraction

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Time Series Analysis and Forecasting of Water Quality Parameters along Yamuna River in Delhi 2

Time Series Analysis and Forecasting of Water Quality Parame...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Gupta, Neetu Yadav, Surendra Chaudhary, Neha School of Engineering & Technology Career Point University Kota324005 India Department of Computer Science and Application Vivekananda Global University Jaipur303012 India Department of Computer Science and Engineering Manipal University Jaipur 303007 India

The extent of pollution in areas near industrial activities and Yamuna River has increased tremendously and to determine the overall water quality status in these locations is utmost important. This paper presents a comprehensive study on assessing the water quality and gain insights into the overall water quality status in the vicinity of industries and along the Yamuna River in Delhi. To achieve this, water samples were collected from Central Pollution Control Board (CPCB) for the last eight years (2013-2021) and was converted into a machine-readable format to facilitate further analysis. These samples were analysed for several water quality parameters, including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), nutrient levels, heavy metals, and other relevant pollutants. By understanding these factors, a deeper understanding of the pollution sources and their impact on water quality is gained. Time series methods are applied to forecast future trends and values of the water quality parameters. This allows for predicting potential changes in water quality over time. A remarkable accuracy of 93.6% is attained in predicting water quality values up to the present time. The analysis reveals that water quality in the NCR region falls below acceptable standards. However, there has been a marginal enhancement in the water quality at Khajori Paltoon (Location L3) post-COVID. To achieve a more significant improvement in water quality, it is imperative to implement new policies. © 2024 Elsevier B.V.. All rights reserved.

关键词： Water quality

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Utilizing Mixture Methods for Classifier in NLP: An Essential Consideration

Utilizing Mixture Methods for Classifier in NLP: An Essentia...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kavita Singh, Harveer Chandigarh School of Business Department of Master of Computer Application India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350322309

The primary purpose of natural language program is to use it for desktop programming tool. This is very effective, and it can transform common biological conversation into a device which can be easily usable form. The robot may receive inputs in the form of data, voice, or visual. Currently, the machine earning is growing and finding usage in a variety of fields. The machine learning tool is developed by leading companies are having very big effect on the mobile and communication industry. Previous generations' daily tasks will be made easier by chat bots that are educated for all kind of issues which need to be discussed and or taken guidance. This well-known platform was mostly constructed using machine learning algorithms (ML) methods. In order to obtain the best outcomes and also the accuracy rate, the study intends to offer a principle component analysis gives the answer to a text categorization issue. © 2023 IEEE.

关键词： Natural language processing systems

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Review on Enhancing Predictive Accuracy in Alzheimer Detection: A Hybrid Approach 3

Review on Enhancing Predictive Accuracy in Alzheimer Detecti...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ambooj Khan, Ihtiram Raza Agarwal, Parul Kaur, Harleen School of Engineering Sciences & Technology Jamia Hamdard New Delhi India Bvicam Department of Computer Science and Application New Delhi India

ISBN: (纸本)9798331518592

Effective early detection techniques are essential because to the tripled global prevalence of dementia, which is mostly caused by Alzheimer's disease (AD) and is predicted to reach 152 million by 2050. AD pathology, which has a preclinical phase lasting 10-20 years before severe cognitive impairment, is associated to about 70% of dementia cases. Early detection of AD is essential since medication trial success rates may be increased and risk factor management may avoid 40% of cases. Currently, lengthy cognitive tests that are insensitive to the pre-clinical stage are used to detect dementia. Initiatives to make open data available have gathered so much data on AD that it is now too much for humans to analyze. In order to diagnose AD accurately, machine learning (ML) has become essential, especially when it comes to early-stage AD prediction. AI and ML-driven healthcare research places a strong emphasis on automated disease prediction systems, including dementia detection. Finding those who is susceptible of 'Alzheimer's disease' (AD) progression is still difficult, especially in light of the disease's severity and fast advancement. Preventing the progression of the disease and planning the course of treatment depend on early detection. Reviewing AD detection research conducted since 2016 has highlighted the contribution of machine learning (ML) to improving prediction accuracy. The reviews provide information on participant demographics, data modalities, feature extraction strategies, and ML models used. By combining earlier studies, promoting critical analysis, and directing future investigations for early AD diagnosis, this thorough review helps researchers. © 2024 IEEE.

关键词： Adversarial machine learning

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