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IoT-Based Livestock Monitoring and Management System Using Machine Learning Algorithms

IoT-Based Livestock Monitoring and Management System Using M...

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science, Technology, engineering and Management (ICSTEM), International Conference on

作者： Nagendar Yamsani K. Muthukumaran B Santhosh Kumar Asha V Navdeep Singh JoshuvaArockia Dhanraj School of Computer Science and Artificial Intelligence SR UNIVERSITY Warangal Department of Biomedical Engineering DSCET-Anna University Chennai Institute of Aeronautical Engineering Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara Department of Computer Science and Engineering (AI&ML) School of Engineering Dayananda Sagar University Bengaluru Karnataka India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

This article provides a description of an Internet of Things (IoT) system that employs machine learning algorithms to provide real-time and precise insights into the health and behaviour of animals. A network of Internet of Things (IoT) sensors strategically placed throughout the cattle area of the proposed system is capable of monitoring a variety of attributes, including temperature, humidity, grazing patterns, and movements. The information gathered by these sensors is wirelessly transmitted to a central centre, where it is analysed by algorithms that acquire expertise through practice. To extract meaningful results from acquired data, supervised and unsupervised machine learning algorithms are implemented. The system maintains real-time surveillance of farmers or administrators and promptly alerts them to any anomalous events that may occur in regards to the animals' welfare or behaviour. This empowers them to expeditiously implement preventative measures against infections or optimise feeding regimens. Investigating historical data can yield significant insights that facilitate well-informed decision-making concerning enduring concerns like resource allocation or breeding initiatives. The incorporation of machine learning technology and the internet of things (IoT) into livestock management practices increases overall agricultural efficiency, productivity, and animal welfare.

关键词： Temperature sensors Temperature measurement Machine learning algorithms Animals Surveillance Sensor phenomena and characterization Real-time systems

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YOLOv8- Based Helmet and Vest Detection System for Safety Assessment

YOLOv8- Based Helmet and Vest Detection System for Safety As...

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

作者： Divyanka Thakur Priya Pal Amogh Jadhav Numaira Kable Bhagyalakshmi V Sonali Deshpande Department of Computer Science Engineering (AI&ML) Smt. Indira Gandhi College of Engineering Navi Mumbai India Department of Electronics and Telecommunication KC College of Engineering Management and Research Thane(E) India

Helmet and vest detection systems ensure worker safety in high-risk professions like construction, mining, and law enforcement. The latest version of the You Only Look Once (YOLO) object detection model, YOLOv8, offers significant advancements over its predecessors. YOLOv8 utilizes a fully convolutional neural network, comprising two major components: the backbone and the head, incorporating a self-attention mechanism to identify objects at various scales in the image. This model eliminates anchors, reducing box predictions and speeding up post-processing phases. Among the YOLOv8 family, YOLOv8-s stands out as the smallest and fastest model, with a size of 21 MB and a single epoch runtime of only 11 seconds. In this study, the YOLOv8-s model achieved an impressive 99.5% mean average precision (mAP) accuracy rate in real time. By implementing this system, the construction industry can significantly enhance safety practices through efficient and reliable monitoring of personal protective equipment usage at construction sites.

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Genetic Signal Processing for Categorizing Genomic Data using Convolutional Neural Networks

Genetic Signal Processing for Categorizing Genomic Data usin...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Pulicherla, Padmaja Aluvala, Srinivas Premchander, Munimanda Sai Sudha, Poranki V L R N Revanasiddappa Institute of Technology and Management Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Malla Reddy Engineering College for Women It Department Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Breakthroughs in high-throughput sequencing technology have resulted in exponential growth in acquired genetic data. Classic machine learning systems have difficulties in dealing with complex genetic data, leading to investigation of deep learning methods. A unique technique for categorizing genomic data using convolutional neural networks (CNNs) is described. The genetic signal processing approach can extract unique characteristics from genomic data. CNNs' ability to comprehend subtle patterns and correlations in cleaned-up data results in improved classification accuracy. A publicly accessible dataset is used to compare the proposed technique across various criteria. The study of genetic information using digital noise processing technology is known as genetic signal processing (GSP). GSP are used to turn the nucleotide sequence into a line graph, allowing for appropriate pattern classification. From the results, it clearly shows that proposed CNN achieves better results in terms of accuracy (0.96), precision (0.96), sensitivity (0.95), specificity (0.97) and f1-score (0.95). © 2023 IEEE.

关键词： Convolutional layers Convolutional neural networks genetic signal processing Genetic signals Genomic data

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Kullback–Leibler Divergence-Based Feature Selection Method for Image Texture Classification 5th

Kullback–Leibler Divergence-Based Feature Selection Method ...

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Rao, M. Subba Devi, Guntoju Kalpana Mubeen, Suraya Prashanth, Badam Fatima, Tazzeen Madhavi, K. Reddy Kumar, Voruganti Naresh Chintalacheri, Charan Yadav Andhra Pradesh Rajampet516126 India Department of CSE CMR Technical Campus Telangana Hyderabad India Department of ECE CMR Technical Campus Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Department of ECE Shadan Women’s College of Engineering and Technology Telangana Hyderabad India AI&ML School of Computing Mohan Babu University Tirupati India Masters in Computer Science University of Kansas Lawrence United States

ISBN: (纸本)9789819997039

Nowadays, with the Big Data phenomenon, the need of the usage of feature selection (FS) methods is critical. FS can assist to address photograph analysis, and in reality it has been gaining importance within a rapid few years ago, however also to relieve the computational burden required for extracting records from the snapshots. An exhaustive assessment and evaluation of the latest contributions of FS to the field of image analysis is necessary. By eliminating unimportant features from the original set, selection of features significantly enhances the efficiency of texture classification, which is a key factor in machine learning performance. By selecting a characteristic, one can achieve accuracy in database type while also accelerating the class rate. The primary goal of the endeavor is to choose the most substantial skills in the function set to carry out a particular undertaking. Kullback–Leibler (KL) divergence approach showed that accuracy in classification is proven with the use of the present methods when feature selection is applied to Feature extraction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Extraction

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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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Expression of Concern for: Building an Emotion Detection System in Python Using Multi-Layer Perceptrons for Speech Analysis

Expression of Concern for: Building an Emotion Detection Sys...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Regula Srilakshmi Vidya Kamma Shilpa Choudhary Sandeep Kumar Munish Kumar Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering (AI&ML) Neil Gogte Institute of Technology Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (数字)9798350342338

ISBN: (纸本)9798350342345

Human speech emotion recognition analyses a speaker's speech to determine their emotional state. Included are several applications in psychology, medicine, and human-computer interaction. Automated speech emotion identification techniques have been widely used in recent years thanks to machine learning algorithms. A number of speech processing libraries are available in Python, a popular programming language for system research. This focuses on creating a Python-based full speech popularity system that uses mlP classifiers. The goal of applications in psychology, healthcare, and human-computer interaction is to routinely discern the emotional state of a speaker based on their speech. With the aid of a multi-layer perceptron (mlP) classifier, we intend to improve a speech emotion reputation device written in Python. The proposed system will derive features from speech recordings, including mal-frequency cepstral coefficients (MFCCs) and prosodic functions. We will train and evaluate our model using an open-supply speech emotion popularity dataset, including RAVDESS. The extracted features may be supplied to an mlP classifier that has been trained using the classified emotional speech statistics dataset. Once mastered, the device can accurately categorise the speaker's emotional condition into a variety of categories, including happiness, despair, anger, and fear. Finally, we can evaluate the performance of our model using existing techniques on a distinct check out dataset.

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Acoustic Based Bird Species Classification Using Deep Learning

Acoustic Based Bird Species Classification Using Deep Learni...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Lade Gunakar Rao K Sridhar Sallauddin Mohmmad Bhargav Somisetty Rashmi Shivanadhuni School of Computer Science and Artificaial Intelligence SR University Warangal Telangana India Vaageswari College of Engineering Thimmapur Karimnager Telangana India Department of CSE(AI&ML) CVR College of Engineering Hyderabad Telangana India Computer Science and Engineering Malla Reddy Institute of Technology and Science Hyderabad Telangana

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This paper uses deep learning techniques to present a novel approach for acoustic-based bird species classification. Leveraging a dataset comprising 10,955 audio samples from six distinct bird species, we extracted diverse features, including chroma, spectrogram, and MFCCs. These features were stacked to form input tensors of size 48x128x1, subsequently fed into a CNN architecture. Our model achieved a commendable accuracy of 0.81 in classifying bird species, demonstrating the efficacy of the proposed methodology. This research contributes to bioacoustics and avian ecology by offering a reliable tool for automated bird species identification, which holds promise for various applications, including biodiversity monitoring and conservation efforts.

关键词： Deep learning Tensors Communication systems Birds Feature extraction Ecology Reliability

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Role of RPA to Seize Opportunities in 5G Edge Computing in Telecommunications

Role of RPA to Seize Opportunities in 5G Edge Computing in T...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Yendluri, Dileep Kumar Tatikonda, Reshmi Tatikonda, Ramya Ponnala, Jainath Kempanna, M. Bhuvanesh, A. University of the Cumberlands Department of Computer Science Williamsburg United States University of the Central Missouri Department of Computer Information Systems Warrensburg United States University of the Cumberlands Department of Information Technology Williamsburg United States Jawaharlal Technological University Department of Electronics and Communication Engineering Hyderabad India Bangalore Institute of Technology Department of Ai and Ml Bangalore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350388800

This paper explores the role of Robotic Process Automation (RPA) in leveraging opportunities presented by 5G edge computing in the telecommunications industry. Integrating RPA and 5G edge computing offers numerous benefits, including enhanced operational efficiency, real-time decision-making, improved service delivery, and cost savings. However, it also poses challenges such as complex integration, security concerns, and the need for effective change management. The paper comprehensively analyzes the potential benefits, challenges, and recommendations for implementing RPA in 5G edge computing. It explores use cases, future advancements, emerging technologies, and anticipated opportunities and challenges. The findings highlight the need for clear objectives, process optimization, collaboration between IT and business teams, and continuous monitoring to leverage RPA in 5G edge computing effectively. © 2024 IEEE.

关键词： Telecommunication industry

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Real-Time Wrist Fracture Detection Using YOLO11

Real-Time Wrist Fracture Detection Using YOLO11

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Advanced Computing Technologies (ICoACT), International Conference on

作者： Nellore Sai Nikhil Vemula Kesavaaditya Gupta Sai Harshavardhan Srungavarapu Gouranga Mandal School of Computer Science and Engineering VIT - AP University Amaravati Andhra Pradesh India Department of Computer Science and Engineering (AI & ML) Dayananda Sagar University Bengaluru Karnataka India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

More than 1.71 billion individuals globally are impacted by bone diseases, especially conditions related to the musculoskeletal system, with fractures being a major worry. Prompt diagnosis is crucial, however, overlooked fractures are frequent in emergency situations, resulting in care delays. ai, particularly deep learning (DL), is becoming increasingly popular for aiding radiologists in identifying fractures in medical images. This review examines how deep learning is used in bone imaging. We employed the YOLO11 model, an algorithm for detecting objects in real-time that can forecast both bounding boxes and class probabilities at once, to detect bone fractures in real-time. We analyzed a variety of bone fracture images from different sources. Sophisticated training methods were used to improve the model's precision and resilience. The YOLO11 model showed great efficiency in detecting and categorizing fractures, providing substantial promise for live clinical use and enhancing diagnostic speed in medical settings.

关键词： Deep learning Wrist YOLO Training Reviews Computational modeling Bones Real-time systems Medical diagnostic imaging Resilience

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ai Technology for the Practices of Societal Sustainable Development

AI Technology for the Practices of Societal Sustainable Deve...

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Computational Intelligence for Security, Communication and Sustainable Development (CISCSD), International Conference on

作者： N Santhosh Kumar CH Rajendra Prasad K Pavan Kumar P Department of Computer Science and Engineering ANURAG Engineering College Kodad Telengana India Department of Computer Science and Engineering (Cyber Security) Institute of Aeronautical Engineering Hyderabad India Department of Computer Science and Engineering (AI & ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365412

The importance of incorporating ai technology into the engineering process is growing as technology gets more integrated. In this paper, we will explore artificial intelligence (ai) and its applications in sustainability engineering and management, specifically looking at how ai may improve efficiency, optimize the use of resources, and have a lasting impact. The primary focus is on addressing sustainable sustainability through the use of ai technology in civil, medical, agricultural, and mechanical engineering fields, among others. ai is a symbol of cutting-edge technology that has a significant influence on the growth of industries, the decrease of waste, and the state of the environment. The purpose of this study is to review the literature in search of important trends and insights, and then to investigate how artificial intelligence might be used to encourage sustainable practices in the workplace. From optimizing a flexible workforce to intelligently planning tasks and managing risks.

关键词： Industries Reviews Employment Symbols Market research Planning Security Artificial intelligence Sustainable development Mechanical engineering

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