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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Ramakanth Kumar P Pranav V Jambur Diptanshu Kumar Computer Science & Engineering R. V. College of Engineering Bengaluru India Artificial Intelligence & Machine Learning Engineering R. V. College of Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The paper addresses the challenges of vehicle detection and identification in road safety and surveillance, especially in hit-and-run incidents and vehicle theft cases. The solution combines computer vision with YOLO models for real-time vehicle detection, colour classification, and a custom CNN for accurate vehicle type classification. YOLO is also used for license plate recognition, paired with Tesseract OCR to extract alphanumeric details for precise identification. The colour identification process is two-fold: YOLO detects vehicles in video frames and isolates their bounding boxes, which are then processed through a YOLO-based classifier trained on the VCoR dataset with 15 vehicle colour labels. Trained over 25 epochs, the model achieved 85.6% top-1 accuracy and 99.5%top-5 accuracy, proving reliable across diverse lighting, weather, and traffic conditions. This system automates vehicle detection and identification, reducing the time and manual effort needed to review large video datasets. Integrating vehicle type classification, colour recognition, and license plate identification, it provides a scalable and efficient tool for law enforcement and surveil- lance, enabling rapid suspect vehicle identification in complex traffic environments. The solution enhances traffic monitoring and security, delivering high accuracy in real-world scenarios. Scalable across urban areas, highways, toll booths, and smart city infrastructure, it offers a versatile approach to modern traffic surveillance.

关键词： YOLO Accuracy Smart cities Law enforcement Surveillance Computational modeling Color Traffic control Real-time systems License plate recognition

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Enhancing Crop Yield: A Deep learning Approach to Detect Tomato Leaf Diseases

Enhancing Crop Yield: A Deep Learning Approach to Detect Tom...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Shreedevi S Ronihal Sheba Selvam Kusuma P Information Science and Engineering BNM Institute of Technology Bangalore Artificial Intelligence and Machine Learning BNM Institute of Technology Bangalore Computer Science and Engineering BNM Institute of Technology Bangalore

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Agriculture represents a vital pillar of the global economy, with crop production significantly influenced by a broad variety of Phyto pathological conditions, particularly plant leaf-diseases. The early and accurate detection of these diseases are paramount, as they have a direct impact on crop yield, which consequently influences the economic viability and market profitability of agricultural outputs. However, many farmers and agricultural practitioners often lack the specialized knowledge required for precise diagnosis of leaf diseases, resulting in suboptimal crop production and economic losses. Consequently, the development of robust and efficient disease detection methodologies is imperative for the sustainability and success of agricultural practices. This research investigates the application including sophisticated deep learning algorithms like VGG19 and CNN, to identify and classify diseases in tomato plant leaves. The developed model attains an accuracy of 99.17%, surpassing both VGG19 and CNN, with the goal of enhancing crop management strategies and optimizing overall agricultural productivity.

关键词： Deep learning Accuracy Profitability computer architecture Feature extraction Robustness Convolutional neural networks Sustainable development Crop yield Diseases

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Remote Sensing–Based Food Processing for Changing Climatic Conditions

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 484-495页

作者： Raj, Roop Gondi, Dedeepya Sai Nitnaware, Swati R. Banerjee, Sudipta Athithan, Senthil Arpita D, Gopinath Department of Economics HNBG Central University Uttarakhand Srinagar India Department of Artificial Intelligence and Machine Learning The University of Texas at Dallas Richardson TX United States Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Maharashtra Pune India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science Kristu Jayanti College (Autonomous) Karnataka Bengaluru India

A product must pass quality control inspections before it can be shipped to customers. In terms of quality assurance, this is crucial. Having a high-quality product is more significant for customer happiness than supplying a larger number of the same thing. As the climatic conditions change, the shelf time also varies. Quality can also be understood as the sum of all the factors that go into making something that the target audience values highly. There has been a dramatic uptick in recent years in the usage of the application of remote sensing and advanced image processing to enhance the appearance of fruits and further commodities. This is because these tools are especially helpful in overcoming the places where people’s eyesight misses the mark. This points out that the use of AI and imaging technology can replace overdue and unreliable techniques of quality assurance in manufacturing with quicker and more objective procedures. This article uses remote sensing and picture segmentation to describe how to analyze and rate food for different climatic conditions. It can tell the difference between several fruit varieties and also tell if a specific piece of fruit has gone bad. Gaussian elimination is first used to clean up the images by removing any visible noise. The images are then subjected to histogram equalization, which improves their overall quality. To carry out the segmentation process, the K-means clustering method is used. Then, photographs of fruits are classified using a number of strategies for machines to learn, including KNN, SVM, and C4.5 formulas to analyze the condition of a fruit and report back on its wholesomeness. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Agriculture artificial intelligence Food Processing Remote sensing Technology

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Enhanced Model for Mango Detection and Quality Classification Using Optimized Feature Extraction Techniques

Enhanced Model for Mango Detection and Quality Classificatio...

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IEEE Students' Conference on Electrical, Electronics and computer Science (SCEECS)

作者： Adla Aryan Abdul Aleem Mohammed Manish Chabra Syed Saarib Rasheed Mohammed Adnan Mohammed Abdul Raoof Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telangana India Department of Computer Science and Engineering Muffakham Jah College of Engineering and Technology Hyderabad India Department of Artificial Intelligence & Data Science Methodist College of Engineering and Technology Hyderabad Telangana India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This paper introduces an automated grading system for mangoes, enhancing efficiency and accuracy compared to human-based methods. The system uses the Lion Assisted Firefly Algorithm (LA-FF) to extract the best features from multiple highlights, enhancing grading efficiency and accuracy. The LA-FF algorithm is then used to fine-tune the convolutional layers of a deep CNN based on the specific requirements of mango grading. The system integrates the latest algorithms, automation, and adaptation to create an even more effective and precise grading system suitable for rural agricultural contexts. The LA-FF algorithm is used to extract the best features from multiple highlights, resulting in a more accurate and efficient grading process.

关键词： Measurement computer science Accuracy Feature extraction Classification algorithms Convolutional neural networks Reliability Optimization Consumer electronics Convergence

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ParkinSense: A Tri-Sensory AI-Powered Framework for Early Detection of Parkinson’s Disease 9th

ParkinSense: A Tri-Sensory AI-Powered Framework for Early De...

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9th International Conference on Information, Communication and Computing Technology, ICICCT 2024

作者： Akshaya, M. Joy, Emmanuel Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9783031724824

Parkinson’s disease (PD) is a brain dysfunction condition that affects thousands of people across the globe. Early detection of Parkinson’s disease is crucial for effective treatment and control of symptoms. This study employs a multi-modal methodology coded as ParkinSense that combines spiral drawings, gait analysis, and Magnetic Resonance Imaging (MRI) analysis for timely Parkinson’s disease identification. MRI analysis provides insight into structural and functional changes in the brain, revealing potential anomalies. GAIT analysis assesses alterations in walking patterns, such as reduced arm swing and slow gait velocity. Spiral drawings, serve as a surrogate for motor dexterity and fine motor control, both of which are compromised in Parkinson’s disease. In this study, data from Parkinson’s patients and healthy candidates are collected and analyzed using advanced machine-learning techniques to create models for accurate disease prediction and classification. Specifically, the gait analysis is conducted using a Support Vector machine and XGBoost algorithm. The spiral drawings are analyzed using a Convolutional Neural Network. The MRI data is processed using a MobileNet-based neural network. These algorithms are integrated to create models for accurate disease prediction and classification. The results demonstrate that this multimodal technique is equipped to identify Parkinson’s disease with an accuracy rate of more than 90%. Early detection using speech analysis, gait analysis, and spiral drawings can lead to earlier intervention and treatment, resulting in a better excellence of life for those affected. This study adds to the expanding body of knowledge about non-invasive, cost-effective approaches for the timely identification of Parkinson’s disease. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Predictive Channel State Information Feedback in Multiple Input Multiple Output Using Improved Recurrent Neural Network 3

Predictive Channel State Information Feedback in Multiple In...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Bittla, Srinivasa Rao Balasm, Zaed Khan, Zabiha Saravanan, T. Senbagam, K. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering Technology Department of Computer Science and Engineering Mamallapuram India

ISBN: (纸本)9798331508456

In modern era, Multiple Input Multiple Output (MIMO) technology is a crucial element in wireless technology, where accurate prediction of Channel State Information (CSI) feedback is critical for beamforming, optimal resource allocation. However, due to traditional compression techniques based on quantization such as scalar and vector quantization which introduces errors in CSI feedback, which leads to inaccurate channel estimation. To overcome these challenges, an Improved Recurrent Neural Network (IRNN) based CSI feedback mechanism is proposed with an integrated self-attention mechanism to improve the prediction accuracy. Initially, a system based on downlink massive MIMO and Orthogonal Frequency Division Multiplexing (mMIMO-OFDM) is considered. This system consists of multiple gNodeBs (gNBs), which serve multiple User Equipments (UEs). These multiple gNBs transmits Reference Signals (RSs) to enable UEs, to estimate and provide CSI feedback. Furthermore, IRNN based channel predictors are deployed at both the gNBs and UEs, which learn the temporal relationships among CSI data and refine the feedback information. The proposed IRNN is improved by integrating self-attention mechanism. Finally, the proposed IRNN outperformed RNN by achieving better results in Normalized Mean Squared Error (NMSE) of -26.500, precoding gains of 0.998, cosine similarity of 0.998 and spectral efficiency of 1.425. © 2025 IEEE.

关键词： Orthogonal frequency division multiplexing

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Unleashing WSN Potential: Bowerbird Optimized Energy-Conscious Multipath Clustering with Causal Dilated Cosine for Enhanced Load Balancing 4

Unleashing WSN Potential: Bowerbird Optimized Energy-Conscio...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Joon, Rakesh Kumar Anitha, R. Selvan, Saravana Latha Mageshwari, P.S. Gayathri Priya, S. Malathi, K. Ganga Institute of Technology and Management Department of Electronics and Communication Engineering Haryana Kablana124104 India S.A. Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai600077 India School of Professional Engineering Manukau Institute of Technology Auckland2104 New Zealand Tamil Nadu Kavaraipettai601206 India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai601206 India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

Wireless Sensor Networks (WSNs) are essential in the collection of real time data across different fields, including environmental monitoring and process control. However, due to the bounded amount of energy in sensor nodes, network lifetime becomes an issue, which hinders performance and reliability. Thus, optimization of WSN performance in an attempt to minimize energy consumption is essential for reliable data transmission. In conventional routing and clustering models, network load distribution is not achieved adequately, resulting in a more significant loading of nodes and faster exhaustion of energy. To solve these problems, it is necessary to carefully design load distribution and path finding algorithms to support long-term WSN operation. The objective of this research is to design a framework-Ski-CDiCo-BO-that integrates Skill Optimization for clustering, and ensuring an energy-efficient paths while reducing redundant transmissions on nodes and conserving node energies, and uses the Causal Dilated Cosine Architecture for sophisticated load balancing, and Bowerbird Optimization for improving the parameterization of neural networks. Analysis reveals that the presented Ski-CDiCo-BO framework provides improved performance of various parameters including data delivery success rate of above 99.8% and network lifetime gain by up to 99.5%. Also, load distribution accuracy remains above 99.3%, which indicates that the framework is capable of avoiding node overload and end-to-end delay improved upon by 99.6%, indicating that the solution maintains high parameters even in dynamically changing conditions. In conclusion, Ski-CDiCo-BO maximizes WSN efficiency by implementing cutting-edge clustering, load balancing, and optimization techniques, setting new standards for energy-conscious and reliable sensor network performance across diverse applications. © 2025 IEEE.

关键词： Sensor nodes

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An Optimal Real-Time Routing and Clustering Protocol for UAV Networks Based on Security

An Optimal Real-Time Routing and Clustering Protocol for UAV...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Sophia S Getzi Jeba Leelipushpam Paulraj Yuvarajan D Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Unmanned Aerial Vehicle (UAV) networks play an important role in different application areas such as military surveillance, emergency services, and infrastructure. However, these networks face significant challenges such as data leakage, unauthorized access, limited processing capacity, and energy constraints. Available techniques such as Elliptic Curve Cryptography (ECC), hash-based techniques, and dynamic clustering algorithms have been proposed to enhance security and operational performance; however, they often encounter challenges concerning scalability, adaptability to real-time changes within the network, and resource-related constraints. This research presents a streamlined authentication framework with the intent to facilitate secure and effective functionalities in turbulent and fluctuating settings. The proposed protocol uses Dijkstra's algorithm for optimal real-time routing, Adaptive Optimization Clustering (AOC) for dynamic resource distribution, and K-means clustering for noise classification. The proposed methodology achieves substantial improvements in power efficiency, latency, and Packet Delivery Rate (PDR) due to reduced computational overhead as well as risks such as data interception and replay attacks. Simulation results conclude that the system suggested here would be secure, scalable, and energy-efficient to be used for applications in demanding scenarios, such as mission-critical scenarios.

关键词： Protocols Heuristic algorithms Clustering algorithms Autonomous aerial vehicles Routing Elliptic curve cryptography Real-time systems Security Vehicle dynamics Optimization

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Detection of Emergency Vehicles in Traffic and Assign Traffic Free Path Using Deep learning 4

Detection of Emergency Vehicles in Traffic and Assign Traffi...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Vikruthi, Sriharsha Singasani, Tejesh Reddy Ram Pavan Kumar, V.T.M. Nagendrudu, P.V.V.S.D. Raghavendra, Ch Sahith, R. B v Raju Institute of Technology Dept of Cse Telangana Medak Narsapur502313 India University of Cumberlands LouisvilleKY United States Kakaraparti Bhavanarayana College Dept of Computer Science Andhrapradesh Vijayawada India Aditya Univeristy Department of Artificial Intelligence and Machine Learning Suramplaem India Andhra Pradesh Guntur India Cvr College of Engineering Dept of Computer Science and Engineering Telangana Hyderabad501510 India

ISBN: (纸本)9798331523923

Too much of Traffic jams are in a highly populated country. Sometimes emergency vehicles such as fire-fighter and ambulance get stuck in traffic, putting lives at threat in many cases. Priority should be given to special vehicles and their way should be cleared. However, traffic police find it difficult or sometimes impossible to handle with it. Emergency vehicles have visual and audio warning devices help them to overtake traffic by demanding as little space as possible from other vehicles. Many death happen due to delay in doctors, ambulance and fire engine vehicles while reaching their destinations. The traditional systems didn't provide path free route accurately. Therefore, by using detection of emergency vehicles in traffic and assign traffic free path using deep learning system, emergency vehicles can get free path or fast route by observing the traffic cameras. So, those emergency vehicles can reach the location on time by using RNN system. Therefore, the main objective is to improve this system for better results interms of accuracy, detection rate and precision. © 2025 IEEE.

关键词： Recurrent neural networks

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Leveraging Neural Networks with BiLSTM and CNN Models for Enhanced HR Management Practices 3

Leveraging Neural Networks with BiLSTM and CNN Models for En...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kalva, Yamini Parasar, Anuradha Pujar, Rajlaxmi Pramod Balasubramanian, K. Pasha, Zameer Ramya, S. Sri Ramachandra Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India School of Liberal Education Galgotias University Dankaur India Indira School of Business Studies Pgdm Department of Marketing Pune India Kalasalingam Academy of Research and Education-Deemed to be University Department of Computer Applications Krishnankoil India Department of Management Bengaluru India M.Kumarasamy College of Engineering Department of Artificial Intelligence and Machine Learning Karur India

ISBN: (纸本)9798331508456

The most valuable asset of any business is its employees. Even with technological advances, organizations rely heavily on human resources for system administration. An increasingly competitive sector makes managing and retaining talented workers difficult. An organization's ability to develop, survive, and compete depends on its employees' skills. These challenges can be overcome using preprocessing, feature extraction, and model training. Data preparation for machine learning is necessary yet time-consuming. Features selection is critical since it enhances accuracy and reduces overfitting. CNN-BiLSTM trained the model. The CNN- BiLSTM model outperformed BiLSTM and CNN. The model's 90.62 percent accuracy showed it could handle HR tasks. For effective human resource management, this study recommends sophisticated machine learning models. Today's businesses need reliable tools for managing and keeping people, and the CNN-BiLSTM model helps. © 2025 IEEE.

关键词： Personnel selection

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