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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Kanagaraj, R. Elakiya, E. Kumaresan, A. Selvakumar, J. Suganya, J. Shruthi, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India Vellore Institute of Technology Department of Computer Science and Engineering Chennai India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Mailam Engineering College Mailam India Bosch Global Software Technologies Coimbatore India

ISBN: (纸本)9798350321425

Agriculture is under tremendous pressure to produce more productivity with fewer resources as a result of the expanding global population. Decision-making along the entire supply chain needs to be improved considering competing economic, environmental, and societal demands. Data from satellites, drones, sensors, and other sources must be processed in order to make informed judgments. However, sophisticated machine learning and data analytics methods must be used due to the complexity and volume of the data. This initiative covers crucial duties in yield prediction and precision agriculture. Management zones are areas of a field that have high inter-regional variability and low intra-regional variability, dividing the field into uniform zones for which agronomic practices should be modified independently. For fertilizer optimization and post-harvest logistics, yield prediction is crucial. In order to make up for the nutrients that have been taken out of the soil, fertilizer type and quantity must be adjusted accordingly. The yield data is also essential for enhancing harvesting, shipping, storage, and sales. So, utilizing web scraping and convolution neural networks, the proposed model has been forecast yields from input satellite photos. The result of investigations is illustrated that the artificial neural network algorithm is used to predicting the agricultural data rules and powerfully decrease the error rate from crop yield data. © 2023 IEEE.

关键词： Neural networks

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Real-Time Crop Analysis with Enhanced Image Classification Using Deep Learning and Fuzzy Logic Techniques 2

Real-Time Crop Analysis with Enhanced Image Classification U...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Kanimozhi, S. Deepika Roselind, J. Shah, Neal Sai Ramesh, L. School of Computer Science and Engineering VIT University Chennai India School of Electronics Engineering VIT University Chennai India Department of Computer Science and Engineering St. Joseph's Institute of Technology Chennai India

ISBN: (纸本)9781837242672

The proposed research presents a smart irrigation system is designed to optimize agricultural water management using a robotic framework that integrates deep learning and fuzzy logic for real-time crop classification and decisionmaking. It employs a combination of VGG-19 and a fusion model RTCropNet (Real-time crop network), leveraging the strengths of each architecture: VGG-19 for robust feature extraction and the RTCropNet fusion for enhanced spatial localization and fine-grained crop classification. Pre-processing is performed through advanced fuzzy logic techniques to improve image quality, enhancing classification accuracy across diverse crop types. Evaluation metrics indicate that the RTCropNet model achieves a training accuracy of 80.15% and a validation accuracy of 81.77%, with precision, recall, and F1 scores of 0.83, 0.82, and 0.82, respectively, outperforming traditional methods on complex agricultural imagery. Comparative analysis shows the model's superiority over EfficientNet-B3, DenseNet-121, and MobileNetV2, particularly in handling high-resolution spatial details, while maintaining efficiency in real-time conditions. This integration of deep learning with fuzzy logic and sensor data represents a novel approach in precision irrigation, offering a dynamic, datadriven response system that adjusts irrigation based on real-time environmental conditions, distinguishing it from conventional SOTA approaches. © The Institution of engineering & technology 2024.

关键词： Deep learning

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An optimized MCHVD approach to curtail handover failures in wireless networks

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International Journal of Information technology (Singapore) 2024年第3期16卷 1989-1995页

作者： Gupta, Sachin Deepak, Shashikant Bhuvana, J. Singh, Vaishali Jindal, Kanika Vekariya, Vipul Chandrasekhar, Malepati Narula, Gagandeep Singh Department of Management Sanskriti University Uttar Pradesh Mathura India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed to be University) Bengaluru India Maharishi School of Engineering and Technology MUIT Lucknow India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Noida India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Computer Science Engineering Presidency University Bengaluru India Guru Gobind Singh Indraprastha University Delhi Dwarka India

The applications of wireless local area networks (WLAN’s) are highly accepted by mobile terminals (MT’s) due to their high transmission bandwidth and reduced costs. However, in such high speed networking platforms, there is a possibility of unnecessary handover failure (UHF) due to limited coverage of WLAN’s. Therefore, the given paper provides a multi-criteria hysteresis vertical handover decision (MCHVD) approach for wireless systems in order to minimize handover failure by analysing and validating its performance with conventional received signal strength (RSS) scheme of source and target cells. Using OPNET simulator, the proposed approach is tested over 60 samples, which are divided equally among networks such as Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), and Universal Mobile Telecommunication System (UMTS) based on parameters like G power as 0.8, threshold energy limit as 0.05%, and confidence interval as 95%, thereby keeping the maximum accepted error as 0.05 and beta as 0.2. An independent sample T-test is done, which reveals that the probability of handover failure (p) is 0.01 (p < 0.05), which is found to be statistically significant in the MCHVD approach when compared with the conventional RSS scheme. The results show that the handover failure rate for MCHVD is significantly lower than that for conventional RSS-based methods with respect to 20 iterations. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： RSS UMTS Vertical handover Wi-Fi WiMAX Wireless networks

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Deep Learning Algorithm for Brain Tumor Detection and Classification using MRI Images 1

Deep Learning Algorithm for Brain Tumor Detection and Classi...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Harshavardhan, A. Maheswari, N. Uma Prakash, M. Sammeta, Naresh VNR Vignana Jyothi Institute of Engineering and Technology Department of CSE Hyderabad India Panimalar Engineering College Department of Computer Science and Business Systems India Vellore Institute of Technology School of Computing Science and Engineering Vellore India R.M.K College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350323795

Brain tumor classification is beneficial for identifying and diagnosing the tumor's specific location. According to the medical imaging system, early diagnosis and categorization of a tumor extend a person's life. Clinical specialists rely heavily on magnetic resonance imaging (MRI) among numerous imaging modalities since it provides contrast information on brain malignancies. The primary purpose of this project is to use a competent automated approach that improves tumour identification accuracy. Several segmentation strategies have been developed throughout the years to achieve and improve the categorization precision of brain tumours. Brain picture segmentation has long been recognised as a difficult and time-consuming aspect of medical image processing. This method for detecting brain tumors Brain pictures are classified using the Full Resolution Convolutional Network (FRCN) classification architecture after pre-processing and segmentation. This study presents a Full Resolution Convolutional Network (FRCN) with Support Vector Machine (SVM) approach for detecting tumors on MRI scans. The procedure is broken down into four steps. In the first phase, the anisotropic filter is utilized to pre-process raw MRI images, followed by segmentation using the Support vector machine (SVM) and skull classification. The singular value decomposition and primary component analysis operations are performed in the third step. Tumors are then detected and classified using the Full Resolution Convolutional Network (FRCN) approach. Simultaneously, the Support Vector Machine (SVM) technique is employed to improve the classification precision of the study model. The experimental results showed an amazing accuracy rate of nearly 100% in detecting both normal and diseased tissues from brain MR images, confirming the efficacy of the suggested technique. © 2023 IEEE.

关键词： Support vector machines

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Automation of Traffic Signal for Better Commutation of Vehicles 3

Automation of Traffic Signal for Better Commutation of Vehic...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Vaghela, Aarzoo Maruf, Padaya Meitei, Naorem Yaipharenba Singh, Laiphrakpam Dolendro National Institute of Technology Department of Computer Science and Engineering Assam Silchar India Indian Institute of Technology NineLabs Electronics & Electrical Engineering Assam Guwahati India

ISBN: (纸本)9798331523893

Traffic congestion poses a significant challenge in urban areas globally, resulting in wasted time, increased fuel consumption, and heightened pollution levels. Traditional traffic light systems often rely on fixed timers, failing to adjust to fluctuating traffic patterns and causing unnecessary delays. To tackle this issue, an adaptive traffic signal timer is designed to dynamically respond to real-time traffic conditions. The proposed system harnesses live camera feeds from traffic junctions and employs object detection techniques to analyze traffic density accurately. The proposed method represents a proactive approach to addressing urban congestion challenges, promising more efficient traffic management and improved commuting experiences for residents and commuters. A comprehensive simulation platform is developed to validate our system's efficacy and facilitate comparative analysis with existing static systems. © 2025 IEEE.

关键词： Vehicle detection

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Smart Device to Check Harmful Chemicals in Fruits and Vegetables 7

Smart Device to Check Harmful Chemicals in Fruits and Vegeta...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Saranya, R. Kumar, Antony K. Rajendran, P. Kumar, Niresh S. Keerthana, P. Department of Computer Science & Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science & Engineering CMR Institute of Technology Hyderabad India Department of Computer Science & Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Tamilnadu Chennai India Electronics and Communication Engineering Selvam College of Technology Tamilnadu Namakkal India

ISBN: (纸本)9781665464086

Fruits and vegetables square measure extraordinarily nutritive and kind as key food merchandise among the human consumption. They are extraordinarily destructible due to their low amount. Fruits and vegetables square measure the necessary provide of carbohydrates, proteins and minerals. These food commodities square measure reported to be contaminated with toxic and health unsafe chemicals. To overcome this problem, proposed model uses IOT to detect the chemical levels. In this method gas detector has placed and estimate the chemical level in fruits and vegetables. If any chemicals detected immediate alert is distributed through GSM to the owner. In most of the hostel mess and government schools, everybody is getting tormented by the food they consume. Milk and fruits like banana and alternative foods utilized in existence, as all of them don't provide quality since the existence of Fulani gases vary from time to time. To confirm food safety, it's tough to be monitored at every stage of the provision chain. Variation in hydrogen ions concentration is going to be fetch, style, flavor, shelf-life of farm product. These gases increase with time. The aim of this method is to note early food spoilage before signs square measure visible. © 2023 IEEE.

关键词： Proteins

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Multilevel Discrete Cosine Transform and CNN-Based Image Steganography 3

Multilevel Discrete Cosine Transform and CNN-Based Image Ste...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Subburaj, Brindha Sai, Aditya Shantanu, Shantanu Won, Daehan Vellore Institute of Technology School of Computer Science and Engineering Chennai India Binghamton University Department of Systems Science and Industrial Engineering United States

ISBN: (纸本)9798331523893

This paper investigates the growing role of steganography in cybersecurity and presents a hybrid implementation called Multi-level Discrete Cosine Convolution (MDCC) that applies a Multi-level Discrete Cosine Transform (MDCT) and a Convolutional Neural Network (CNN). Steganography provides some advantages compared to cryptography, mainly its invisibility, but there are weaknesses in information capacity and resistance to attacks in traditional techniques. The paper suggests solving these difficulties with a new steganography method using deep learning. The proposed MDCC method provides an opportunity to encrypt large amounts of data in realistic images that are beyond human visual perception. The system is trained on the ImageNet dataset by using a loss function to balance the quality of the cover image and the information it conceals within it. The MDCC model proposed yields accurate results for all metrics since PSNR, SSIM, and bits per pixel come out to be 80.13dB, 0.9918, and 12.5 bpp, respectively. © 2025 IEEE.

关键词： Discrete cosine transforms

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End to End Model to Reduce the Inference, Jamming, and to Increase the Trust from the Compromised Secondary Nodes in Cognitive Radio Networks 2

End to End Model to Reduce the Inference, Jamming, and to In...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Dhananjaya, Shashank Narender, M. Sunitha, R. The National Institute of Engineering Department of Information Science and Engineering Mysore India The National Institute of Engineering Department of Computer Science and Engineering Mysore India B. N. M. Institute of Technology Dept. of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350306415

Wireless technology has advanced dramatically in recent years. Multimedia applications have exploded in popularity over wireless networks. The demand for radio spectrum has expanded as the demand for large capacity for multimedia applications has grown. The traditional fixed spectrum assignment approach has been challenged by the shortage of radio spectrum. As a result, Cognitive Radio (CR) has arisen as a new unique option for addressing spectrum underutilization by allowing users to access unused spectrum bands on the fly. In cognitive radio networks, radio spectrum scarcity, spectrum access, signal interference, security at various layers, trust, jamming, and spectrum management have all become key issues. Proposing a unique approach to address the interference problem produced by secondary nodes that have been hacked. To fulfil the need, we develop a mathematical modelling of the system that incorporates intelligence in the SU to cope with intelligent jammer. Intelligent jammer is cognitive in nature, having the ability to learn, to reason and to adjust its strategies against SU for maximum damage to the CRN. Many researchers have been working on reducing inference and the effect of jamming in the network. But still it is not resulted with the proper solution for the jamming problem in cognitive radio wireless sensor network. Hence proposing the methodology aiming to minimize the effect of Jamming in Cognitive Radio Wireless Sensor-Networks. At the end, the proposed research is aiming to solve the security problem to increase the trust in the cognitive radio network. The secondary users must be able to detect and forecast spectrum holes in the CR environment. The overall result shows the improved network performance by reducing the inference, jamming, spectrum sensing and trust issues in cognitive radio network. © 2024 IEEE.

关键词： Cognitive Radio Network Inference Jamming Q-Learning Software Defined Radio Trust

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computer Vision and Control Algorithm for Sustainable and Precision Agriculture

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SN computer science 2025年第4期6卷 1-13页

作者： Adebayo, Segun Aworinde, Halleluyah O. Ojerinde, Stephen A. Oyedoja, Kayode O. Adeniyi, Abidemi Emmanuel Olanloye, Odunayo Dauda Osueke, Christian O. Ogundokun, Roseline Oluwaseun Das, Abhishek Mechatronics Engineering Bowen University Iwo Nigeria Computer Science Bowen University Iwo Nigeria Computer Science Western Illinois University IL United States Technology Education Emmanuel Alayande University of Education Oyo Nigeria Department of Software Engineering Kaunas University of Technology Kaunas Lithuania Department of Computer Science Landmark University Omu-Aran Kwara State Nigeria Department of Computer Science and Engineering Aliah University Kolkata India Tshwane University of Technology Pretoria South Africa Durban University of Technology Durban South Africa Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The world population continues to increase with a large proportion of this increase happening in Africa just as predicated by Food and Agriculture Organization of the United Nation. To meet the demand of feeding this increasing population, innovative farming practices are being developed to increase the yield of the major stable food supply. This research developed a computer vision and control algorithm on a quadcopter for farm surveillance, crop protection from the invasion of pest, early disease detection as well as crop yield estimation. A process of developing a model capable of detecting and classifying these pests in the environment was presented in this study. The process involves selecting, preprocess and transforming the sensed data from a vision sensor to train classification algorithm and to detect and track object of interest in the environment. This study addresses a notable research gap in computer vision, control systems, big data analytics, and robotics, specifically within the context of smart farming. It primarily utilizes conventional learning algorithms, including Artificial Neural Networks and Support Vector Machines, typically found in widely-used commercial software. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Algorithm computer vision Machine learning Quadcopter UAV

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Real Time Object Detection & Recognition: A ComparativeStudy of YOLOv3 and YOLOv7 in OpenCV 15

Real Time Object Detection & Recognition: A ComparativeStudy...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Geethanjali, T.M. Prithviraj, B. Prajwal, K.M. Gowda, Prajwal C.M. Priyanka Sujithra, M. Chaba, Yogesh PES College of Engineering Department of ISE Mandya India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Real-time object detection is a fundamental task in computer vision, finding applications in various domains such as autonomous vehicles, surveillance systems, robotics, and *** proposed work presents the design and implementation of a real-time object detection system using OpenCV (Open-Source computer Vision Library).The system aims to accurately and efficiently detect and localize objects in video streams or captured *** proposed work begins with dataset collection and annotation, acquiring a diverse dataset of images with annotated bounding boxes representing objects of *** annotated dataset is used for model training and *** deep learning algorithms are considered for object detection, including Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO), and Faster R-CNN, and their performance is compared to identify the most suitable *** techniques like resizing, normalization, and noise reduction are applied to enhance the quality of the input *** extraction is performed using deep learning models VGG16, which is fine-tuned on the annotated *** selected deep learning model is integrated into the real-time system using OpenCV's *** system is evaluated using standard metrics like precision, f1 score, recall, and mean average precision (mAP) to assess its detection *** evaluation is carried out on benchmark datasets and real-world scenarios to gauge the system's robustness and generalization capabilities using two different YOLO models i.e., YOLOv3 and YOLO v7. © Grenze Scientific Society, 2024.

关键词： Deep learning

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