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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Rekha R Nair Tina Babu Kishore S Smitha Shivaswamy Aruna S Manchikalapudi Bhargavi Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Application Nitte Institute of Professional Education Nitte(Deemed to be University) Mangaluru Karnataka India Department of Computer Applications Acharya Institute of Technology Bengaluru India School of Science and Computer studies CMR University Bangalore India Department of Computer Science and Engineering(AIML) Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

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.

关键词： Hands Human computer interaction Emotion recognition computer vision Accuracy Animals Face recognition Computational modeling Gesture recognition Mental health

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Prediction of Crop Yield Using Machine Learning Approaches for Agricultural Data

TechRxiv

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TechRxiv 2023年

作者： Hazra, Simanta Karforma, Sunil Bandyopadhyay, Abhishek Chakraborty, Sayantani Chakraborty, Debasis Dept. of Computer Science and Engineering Asansol Engineering College West Bengal 713305 India Dept. of Computer Science University of Burdwan West Bengal 713104 India The Dept. of Computer Application Asansol Engineering College West Bengal 713305 India The Dept. of Computer Science and Engineering Asansol Engineering College Asansol713305 India

For many developing nations, agriculture is the fundamental source of economic engine. Without a significant increase in food production, the rise in global population during the 21st century would not have been conceivable. Thus, a key issue in agricultural expansion is the accurate forecast of healthy crop output / crop type utilizing machine learning (ML) algorithms. In this paper, the aim is to identify the most effective model that can help farmers in predicting the crop yield / crop type for the benefit of farmers. Seven popular algorithms such as K-Nearest Neighbor (KNN), Random Forest (RF) Classifier, Extreme Gradient Boosting(XGBoost), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN), Support Vector Machine(SVM) and Multi-layer Perceptron (MLP) are investigated on the numerical crop dataset and performances are studied. Experimental results reveal that XGBoost gives the highest accuracy among the seven. Subsequently, Another crop image dataset is explored to identify crop type using transfer learning in convolutional neural networks (CNN). Different models for crop image classification with weights trained on ImageNet such as VGG-19 (Visual Geometry Group-19), VGG-16, Inception v3, ResNet-50 and EfficientNet-B0 are studied. Results based on the accuracy and kappa coefficient show that VGG-19 outperforms other techniques. © 2023, CC BY.

关键词： Image classification

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SBA: A Swift and Stealthy Backdoor Attack Framework for Federated Learning

SBA: A Swift and Stealthy Backdoor Attack Framework for Fede...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Junhan Wang Zhangming Wu Zhuoyue Wang Lu Dong School of Cyber Science and Engineering Southeast University Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Southeast University Ministry of Education Department of Electrical Engineering and Computer Sciences University of California Berkeley USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Learning (FL) enables collaborative model training while protecting the privacy of individual participants. However, this decentralized framework is vulnerable to certain risks, particularly backdoor attacks. In such attacks, malicious actors embed triggers into the global model, causing incorrect classifications for inputs with specific features. Traditional backdoor attacks typically use uniform triggers, which makes them more likely to be detected. Furthermore, These methods tend to overlook the fact that poisoned samples may cause abnormal confidence levels in non-target classes. These methods also require an extended attack window to achieve the desired success rate, which reduces their efficiency and increases the chances of detection. This paper introduces a novel backdoor attack method called Swift Backdoor Attack (SBA), designed to overcome these challenges. SBA improves both the stealth and success rate of backdoor attacks by generating adaptive triggers tailored to specific inputs. It also incorporates Non-Target Class Suppression (NTCS) loss and an entropy gain mechanism to enhance the attack’s effectiveness. Our approach suppresses unnecessary activations in the neural network when encountering poisoned samples, making the attack harder to be detected. Experimental results demonstrate that this method significantly increases the success rate of backdoor attacks within a shorter attack window, while evading existing detection mechanisms. This provides a more efficient and covert strategy for executing backdoor attacks in FL. Our code is available at https://***/bigcheeses/SBA.

关键词： Training Privacy Codes Federated learning Neural networks Collaboration Signal processing Entropy Robustness Speech processing

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A Concise Overview of Cloud-based Platform for Intelligent Converters

A Concise Overview of Cloud-based Platform for Intelligent C...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Narmadha T Ritu Shree Intekhab Alam Computer Science Engineering Department JAIN (Deemed to be University) Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Engineering Department Maharishi University of Information Technology Uttar Pradesh India

This research presents the mechanical implementation and management of freely available modules for the construction of smart conversions systems. Converts that are scattered or unsophisticated are not clever converts. To transmit the maximum power, they have stabilizers that have comparable impedance online. In current energy environments, intelligent adaptations provide fascinating solutions; in fact, their uses have become more and more varied. The suggested method was developed and tested in a laboratory environment using both real photovoltaic units and a solar cell simulator. Innovative conversions are shown in research, and enhanced intelligent capabilities allow for on-the-fly configuration adjustments, real-time diagnostics, and the management of the system's programs. Furthermore, the project's capabilities allows for a wide range of combinations and groups of various energy sources, generators, and modulation techniques. To the best of our knowledge, this project remains the sole platform for smart conversions that is available for free and is currently being utilized by research, industry, and government.

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Design and Modeling of Smart Intellegent Converters in Super Smart Grids

Design and Modeling of Smart Intellegent Converters in Super...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： R Raghavendra Surendra Yadav Savita Computer Science Engineering Department Jain (Deemed to be University) Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Engineering Department Maharishi University of Information Technology Uttar Pradesh India

The foundation of smart microgrid DC topologies are EPCs, or electrical converters of power. Most of the components are intended to be linked to the various busses via EPCs in order to improve the system's autonomy. Thus, the behavior of all EPCs and their control loops determines the microgrid's electrical supply, peace of mind, the dynamic responsiveness. Furthermore, because of the significant degree of unpredictability in their operating circumstances brought about by the intermittent nature of renewable sources of energy and user utilization of energy, DC micro grids represent an innovative model in energy distribution networks. Moreover, the power converters might alter their mode of operation in order to cope with this unpredictability, which complicates the system's fluidity and evaluation of stability. A brief description of the many quantitative experimental blackbox is modeling techniques used with smart DC micro/nanogrids is provided in this work. A study of several nonlinear as well as linear modeling techniques is conducted, outlining both their advantages and disadvantages. Lastly, using illustrated instances, distinctions between blackbox architectures will be shown.

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Hindi News Article's Headline Generation based on Abstractive Text Summarization

Hindi News Article's Headline Generation based on Abstractiv...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Jeetendra Kumar Shashi Shekhar Rashmi Gupta Dept. of Computer Science and Application Atal Bihari Vajpayee University Bilaspur India Dept. of Computer Engineering and Applications GLA University Mathura India

Text summarization is a technique that converts large text into shorter ones by keeping its important information. Most of the available summarizers, summarize English language text. The availability of summarizers in other languages is rare. In the proposed work, abstractive text summarization (ATS) for the Hindi language has been used to generate headlines from news text. To build the model, a Hindi news dataset has been used that contains different news articles related to sports, politics, crime, etc. After pre-processing the text, Word2Vec method has been used for word embedding. ATS model has been built using the Seq2Seq model with attention. After that, we have calculated evaluation metrics like ROUGE and BLEU scores of the result summaries. We have also fine-tuned two pre-trained models, indicBART and multi-lingual variant of T5. After result comparison, it was found that the indicBART perform better than the other two models, considered for the experiment.

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Advances in Medical Image Processing: Enhancing Diagnostics and Treatment Planning

Advances in Medical Image Processing: Enhancing Diagnostics ...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Manik Rakhra Prikshat Kumar Angra Pritpal Singh Anuj Sharma Gagandeep Singh Cheema Bhanu Sharma School of Computer Science & Engineering Lovely Professional University Phagwara India School of Computer Application Lovely Professional University Phagwara India Mittal School of Business Lovely Professional University Phagwara India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Recent advancements in medical image processing have significantly enhanced the capabilities of diagnostic imaging and treatment planning. This paper provides a comprehensive overview of the latest methodologies and technologies in image processing that contribute to more precise diagnoses and improved treatment strategies. We explore several key areas including deep learning algorithms for image segmentation, enhancement techniques for improving image clarity and detail, and innovative approaches for 3D visualization. Furthermore, we discuss the integration of artificial intelligence with traditional imaging techniques to accelerate decision-making processes and personalize patient care. The impact of these advancements is evaluated through case studies and clinical feedback, which demonstrate improved outcomes in patient diagnosis and treatment planning. Our findings suggest that the integration of advanced image processing techniques can transform medical imaging into a more dynamic tool for healthcare professionals, ultimately leading to better patient outcomes.

关键词： Deep learning Technological innovation Accuracy Machine learning algorithms Three-dimensional displays Medical services Transforms Planning Biomedical image processing Artificial intelligence

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Construction Equipment Performance with Cloud Data Analysis for Predictive Maintenance 6

Construction Equipment Performance with Cloud Data Analysis ...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Seeni, Senthil Kumar Maheswari, P. Konidhala, Jothibabu Suguna, M. Raja Ishwarya, M.V. Srinivasan, S. Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science and Technology Faculty of Management Studies Kattankulathur Chennai India University of Technology and Applied Sciences- Al Musannah Department of Electronics and Communication Engineering South Al Batina Musanna Oman Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Agni College of Technology Artificial intelligence and Data science Department Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381689

The research uses Internet of Things technology for predictive maintenance in the construction sector. The system addresses incorporating Internet of Things sensors into construction machinery to enable real-time monitoring of operating parameters. The study suggests a proactive maintenance strategy that may anticipate equipment breakdowns by examining the data obtained and using sophisticated prediction models. With this strategy, construction teams can arrange maintenance activities systematically, which cuts down on downtime and helps avoid expensive interruptions. The abstract emphasizes the advantages of this practice, which include reducing unexpected downtime as much as possible, extending the life of equipment, and making the most of available resources. The study proves the usefulness of the IoT-based predictive maintenance approach via empirical investigations and the analysis of case studies. This validates the strategy's efficacy, contributing to enhanced equipment performance and increased work efficiency in the construction industry. © 2024 IEEE.

关键词： Predictive maintenance

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3-D Near-Field Localization by Jointly Exploiting Spatial and Temporal Information Based on a Nonuniform Cross Array

3-D Near-Field Localization by Jointly Exploiting Spatial an...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zelong Yi Hua Chen Zhiwei Jiang Wei Liu Qing Wang Gang Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China School of Electronic Engineering and Computer Science Queen Mary University of London London UK School of Electronic Informatin Engineering Tianjin University Tianjin China

In this paper, an underdetermined three-dimensional (3-D) near-field source localization method is proposed, based on a two-dimensional (2-D) symmetric nonuniform cross array. Firstly, the fourth-order cumulant of the near-field observations with multiple delay lags is exploited to construct virtual far-field pseudo-observations, leading to increased degrees of freedom (DOF); then, 2-D angles of the nearfield sources are jointly estimated by employing the recently proposed sparse and parametric approach (SPA) and Vandermonde decomposition technique, eliminating the need for parameter discretization. To estimate the range term, the one-dimensional (1-D) MUSIC algorithm is applied by resorting to the conjugate symmetry property of the signal's autocorrelation function. Numerical results are provided to demonstrate the superiority of our method.

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A classifier ensemble approach for prediction of rice yield based on climatic variability for coastal Odisha region of India

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Informatica (Slovenia) 2021年第3期45卷 367-380页

作者： Mishra, Subhadra Mishra, Debahuti Mallick, Pradeep Kumar Santra, Gour Hari Kumar, Sachin Department of Computer Science and Application CPGS Odisha University of Agriculture and Technology Odisha Bhubaneswar India Department of Computer Science and Engineering Siksha’O’ Anusandhan Deemed to be University Odisha Bhubaneswar India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India Department of Soil Science and Agricultural Chemistry IAS Siksha’O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science South Ural State University Chelyabinsk Russia

Agriculture is the backbone of Indian economy especially rice production, but due to several reasons the expected rice yields are not produced. The rice production mainly depends on climatic parameters such as rainfall, temperature, humidity, wind speed etc. If the farmers can get the timely advice on variation of climatic condition, they can take appropriate action to increase the rice production. This factor motivate us to prepare a computational model for the farmers and ultimately to the society also. The main contribution of this work is to present a classifier ensemble based prediction model by considering the original rice yield and climatic datasets of coastal districts Odisha namely Balasore, Cuttack and Puri for the period of 1983 to 2014 for Rabi and Kharif seasons. This ensemble method uses five diversified classifiers such as Support Vector Machine, k-Nearest Neighbour, Naive Bayesian, Decision Tree, and Linear Discriminant Analysis. This is an iterative approach;where at each iteration one classifier acts as main classifier and other four classifiers are used as base classifiers whose output has been considered after taking the majority voting. The performance measure increases 95.38% to 98.10% and 95.38% to 98.10% for specificity, 88.48% to 96.25% and 83.60% to 94.81% for both sensitivity and precision and 91.78% to 97.17% and 74.48% to 88.59% for AUC for Rabi and Kharif seasons dataset of Balasore district and also same improvement in Puri and Cuttack District. Thus the average classification accuracy is found to be above 96%. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Support vector machines

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