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Climatic Condition–Based Comparative Study of Deep Learning Models for Yield Forecasting in Smart Agriculture

Remote Sensing in Earth Systems sciences 2025年第2期8卷 365-374页

作者： Dhanke, Jyoti A. Khandekar, Sachin Dadu Raj, Roop Chunduru, Madhava Rao Shahade, Aniket K. Mani, P. Balaram, Allam Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department of Civil Engineering Sinhgad College of Engineering Maharashtra Pune India Department of Economics HNBG Central University Uttarakhand Srinagar India Department of Computer Science and Engineering Koneru Lakshmaiah Educational Foundation Vaddeswaram Guntur District Andhra Pradesh Guntur India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Department of ECE K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The novel idea of “smart farming” employs cutting-edge information technology to boost agricultural productivity. Modern developments in networking, robotics, and AI allow farmers to keep a closer eye on every process and use treatments calculated by machines with an accuracy level comparable to that of a person. Engineers, data scientists, and farmers are all persistent in their pursuit of methods that will enable them to maximize the efficiency of human labor in farming. Smart farming becomes an ever-improving learning system as vital information resources continue to improve. Artificial intelligence (AI) techniques from deep learning (DL), including CNN, RNN, and GAN, have been extensively researched and used in several industries recently, including agriculture. Software frameworks are often used by agricultural researchers without a thorough examination of a technique’s concepts and operations. Failure of crops owing to low rainfall, soil infertility, and other similar issues have been plaguing farmers recently. The planned effort aids in determining how to intelligently manage harvests and crops in light of the environmental changes occurring. A person may use it as a guide for smart farming. The main objective of this project is to provide a means for a person to effectively grow crops, allowing them to achieve great output while keeping costs down. The entire cost of cultivation may also be better estimated with its aid. A comprehensive approach in farming would be achieved if this helped a person to pre-plan the actions before growing. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Algorithmic neural networks Deep learning Machine learning Models of artificial intelligence Smart farming

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Ad Hoc Network based Android Messaging Apps 5

Ad Hoc Network based Android Messaging Apps

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Singh, Prachee Srivastava, Yatindra Kumar Rahul Sharma, Shelja Department Of Computer Science & Engineering Galgotias College Of Engineering & Technology Greater Noida201306 India Department Of Computer Science & Engineering School Of Engineering & Technology Sharda University Greater Noida201306 India

ISBN: (纸本)9798350330861

In areas with weak internet and cellular connectivity, mobile communication can become unreliable or even impossible. Even though there are numerous Android chat messenger applications available for this purpose, connectivity issues caused by external factors like physical link breakdowns or congestion in the network are significant obstructions that disrupt the process. In this paper, various existing chat applications have been analyzed and it has been intended to analyze the existing hurdles in communication due to various factors. Overall, the study suggests that the Bluetooth-based chat messenger application may offer a better user experience and lower memory requirements as compared to existing internet- based chat applications. This paper would also provide insights to other scholars, researchers & academicians to work further in the area. © 2023 IEEE.

关键词： Bluetooth

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Convolutional Block Attention Augmented Convolutional Neural Network for Indian Sign Language Recognition System

Convolutional Block Attention Augmented Convolutional Neural...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Ghorai, Anudyuti Nandi, Utpal Changdar, Chiranjit Paul, Bachchu Laya, Biswajit Si, Tapas Department of Computer Science Vidyasagar University West Bengal Paschim Medinipur India Department of Computer Science Belda College West Bengal Belda India Department of Computer Science and Engineering Bankura Unnayani Institute of Engineering West Bengal Bankura India

ISBN: (纸本)9789819613472

Despite being a common method of communication for speech-impaired individuals, sign language often goes unnoticed due to a lack of understanding among the general public and many deaf and dumb individuals. This paper aims to design a mediator system that can translate sign language alphabets, addressing the lack of systematic communication methods for these individuals. The system will help identify the alphabets of Indian Sign Language, enhancing their communication capabilities. In recent years, the neural network has extensively used sign language recognition. The CNN network, which is commonly used in sign language recognition, struggles to achieve optimal accuracy due to the equal importance of each image component. This problem can be solved with attention. The proposed model uses the convolution block attention module (CBAM) to focus on important areas. This helps the model make decisions based on the most relevant data, which makes the CNN network more accurate overall. The model gives 99.89% accuracy on the validation test, while only the CNN model gives 97.21% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Exploring the Use of Advanced Imaging Diagnostics: Deep Learning for 3D-MRI Analysis

Exploring the Use of Advanced Imaging Diagnostics: Deep Lear...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Baldev Anand, Kul Bhushan Pawar, Rahul Vivekananda Global University Computer Science & Engineering Jaipur India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

recent advancements in imaging technologies have allowed for astonishing advances in scientific analysis and ultra-rapid real-time imaging or automated evaluation of large information sets. One of the most considerable imaging modalities is three-D-MRI, which provides a complete view of frame tissues and organs. This approach has been utilized in scientific programs, from detecting most cancers to diagnosing coronary heart situations. However, manual assessment based on the statements is frequently tricky and timeingesting due to the high complexity of MRI data units. Deep gaining knowledge of algorithms can be used to automate the evaluation system, supplying quicker and greater unique diagnoses. In this paper, we discover the usage of deep studying to evaluate three-D-MRI photos. We discuss the demanding situations and potential advantages of this technique, in addition to the present-day kingdom of studies inside the discipline. Ultimately, we present our outcomes, which display the capacity of deep studying for three-D-MRI analysis. Deep mastering algorithms offer a green and accurate way of analyzing complex scientific situations. © 2024 IEEE.

关键词： advancements approach assessment modalities technique

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Visual Intelligence in Supply Chain Management with Power BI 15

Visual Intelligence in Supply Chain Management with Power BI

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kitey, Sankalp Kotambkar, Deepali M. Shri Ramdeobaba College of Engineering and Mangement Department of Electronics Engineering Nagpur440013 India Shri Ramdeobaba College of Engineering and Mangement Department of Electronics and Computer Science Nagpur440013 India

ISBN: (纸本)9798350370249

An essential skill set for supply chain management is the ability to visualize and interpret data well. In addition to providing insightful information, these capacities are essential for directing decision-making procedures, which in turn improves the resilience and efficiency of the supply chain as a whole. A range of tools are frequently used in supply chain management to visualize and analyze data. The choice of a specific tool depends on factors like the organization's preferences, the complexity of data analysis required, and the skill set of the users. In this paper, Power BI is explored for its use in visualizing and interpreting data in the context of Supply Chain Management. The database used here focuses on the supply chain complexities of fashion and beauty products. This work introduces an interactive visualization and analysis system to investigate the internal relationships between shipping costs, manufacturing costs, location-based revenue, and defect rates specific to skin care, hair care, and cosmetics products. © 2024 IEEE.

关键词： Supply chain management

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Security and Privacy Preservation Method Based on data Disturbance for Collaborative Edge Computing for Social-Internet-of-Things (S-IoT)

Security and Privacy Preservation Method Based on Data Distu...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Rahul Venkatesh Venugopal, K.R. University Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bangaluru India

ISBN: (纸本)9798350315455

Socia1 Internet of Things (SIoT) is new paradigm that integrate human social with physical Internet of Things (IoT) systems, social IoT is ray of hope in providing ubiquitous connectivity among users. The collaborative edge computing for SIoT is significant because it ensures human social features and connections among IoT devices and users. But it is formidable to perform CEC for social IoT due to user privacy, data security and network heterogeneity and so on. The problem of computing and storage in IoT devices is solved by Collaborative edge computing (CEC) with deep learning (DL). To prevent privacy breach and security threats at edge network and edge devices in SIoT system. This research work discusses the data protection method based on data disturbance approach. To ensure correctness of model adversarial training viewpoint is introduced. The proposed method is capable of predicting contextual filtering words in adversarial text examples, and adaptively determine word replacement order. The proposed method BESAW, a BERT Based Simulated Annealing Word algorithm is used. BESAW compare with five existing methods includes TextFooler, BAE, BERT-Attack, PWWS, and PSO in experimental analysis. © 2023 IEEE.

关键词： Collaborative edge computing (CEC) Internet-of-Things (IoT) Sentence similarity analysis Simulated Annealing (SA)

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Real-World Wireless Network Modeling and Optimization:From Model/data-Driven Perspective

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Chinese Journal of Electronics 2022年第6期31卷 991-1012页

作者： LI Yang ZHANG Shutao REN Xiaohui ZHU Jianhang HUANG Jiajie HE Pengcheng SHEN Kaiming YAO Zhiqiang GONG Jie CHANG Tsunghui SHI Qingjiang LUO Zhiquan Shenzhen Research Institute of Big Data Pengcheng Lab School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) College of Mathematics and Computational Sciences Xiangtan University School of Computer Science and Engineering Sun Yat-Sen University School of Software Engineering Tongji University

With the rapid development of the fifth-generation wireless communication systems, a profound revolution in terms of transmission capacity, energy efficiency, reliability, latency, and connectivity is highly expected to support a new batch of industries and applications. To achieve this goal, wireless networks are becoming extremely dynamic, heterogeneous, and complex. The modeling and optimization for the performance of realworld wireless networks are extremely challenging due to the difficulty to predict the network performance as a function of network parameters, and the prohibitively huge number of parameters to optimize. The conventional network modeling and optimization approaches rely on drive test, trial-and-error, and engineering experience,which are labor intensive, error-prone, and far from optimal. On the other hand, while the research community has spent significant efforts in understanding the fundamental limits of radio channels and developing physical layer techniques to operate close to it, very little is known about the performance limits of wireless networks, where millions of radio channels interact with one another in complex manners. This paper reviews the very recent mathematical and learning based techniques for modeling and optimizing the performance of real-world wireless networks in five aspects, including channel modeling, user demand and traffic modeling, throughput modeling and prediction, network parameter optimization, and IRS empowered performance optimization, and also presents the corresponding notable performance gains.

关键词： network parameter optimization telecommunication computing optimisation mathematical techniques wireless channels performance optimization learning based techniques telecommunication traffic mathematical analysis physical layer techniques channel modeling wireless network modeling fifth generation wireless communication systems learning (artificial intelligence) model-data-driven perspective IRS empowered performance optimization radio channels traffic modeling radio networks

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Integrated Cognitive Detection and Alert System for Mitigating Driver Drowsiness: A Comprehensive Approach towards Enhanced Driver Safety 15

Integrated Cognitive Detection and Alert System for Mitigati...

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

作者： Banavathu, Sivaramakrishna Kukkadapu, Sri Manikanta Surya Vinay Kumar Uddagiri, Arjun Korada, Renuka Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering NTR Dt. Andhra Pradesh Mylavaram521230 India

ISBN: (纸本)9798331300579

This research paper focuses on the development of a sophisticated cognitive detection system for driver drowsiness utilizing OpenCV and Python, presenting an advanced solution to enhance driver safety. In response to the alarming statistic from Road Transport and Highways India, revealing that 22.8% of accidents result from driver drowsiness, this initiative aims to mitigate the detrimental consequences of such incidents, which lead to loss of lives, vehicle damage, road infrastructure destruction, and financial burdens on individuals. The proposed solution involves a precise driver drowsiness detection system coupled with a multi-modal alert system. Upon detecting signs of drowsiness, the system employs nuanced alert mechanisms, such as controlled subtle jerks, to prompt the driver to regain alertness. Additionally, a red-light warning is projected onto the driver's face to counteract drowsiness effectively. Notably, instead of employing abrupt braking, our system adopts a gradual deceleration strategy to ensure a smoother and safer response, minimizing the risk of sudden movements and potential collisions with the windshield. Furthermore, the integration of cruise control mechanisms contributes to reducing the overall fatality rate. This comprehensive solution not only addresses the critical issue of driver drowsiness but also strives to enhance overall road safety by leveraging advanced technology and strategic alerting methodologies. © Grenze Scientific Society, 2024.

关键词： Advanced driver assistance systems

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Real Time Object Detection of Aerial Images using Deep Learning on Jetson Nano

Real Time Object Detection of Aerial Images using Deep Learn...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Wadhwa, Shruti Saini, Poonam Kumar, Rakesh Kashyap, Nishita Singh Siag, Tushar Punjab Engineering College Computer Science and Engineering Department Punjab Chandigarh160012 India Punjab Engineering College Aerospace Engineering Department Punjab Chandigarh160012 India Florida State University Aerospace Engineering Department Tallahassee32306 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Rapid advancements in technologies such as autonomous driving, humanoid robotics, and vision augmentation have led to a growing need for accurate and quick object identification algorithms that operate in real-time on edge devices. For the purpose of detecting and classifying the different kinds of vehicles (such as trucks and cars) crossing the main road, the system was constructed utilizing the YOLOv8 and Faster RCNN object detection model. This made it possible to develop a small, effective system that can process aerial imagery in real time and is ready for deployment in a variety of situations requiring minimal resources. The main goal is to create a reliable and effective vehicle detection system that works with the Nvidia Jetson Nano card. Within NVIDIA's Jetson range of specialized SOCs for AI systems, the Nano is the smallest model available. It has a computing capacity of 472 GFLOPs with a power consumption of 5 Watts. It offers a CPU and GPU integrated into the SOC. These characteristics are within the range of what one may logically anticipate from a widely accessible edge device. The task was accomplished by making use of a sizable aerial dataset that was gathered with UAVs while paying attention to various circumstances and scenarios. As a result, an effective detection model was trained having low latency and high accuracy for vehicle detection on images captured by UAVs. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Aerial photography

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QR Code with Block Chain Technology for Medical Device Ownership 9

QR Code with Block Chain Technology for Medical Device Owner...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Makanyadevi, K. Rithika, S. Biratheep, S. Subanki, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur India

ISBN: (纸本)9798350397376

Medical device quality and safety are important because they affect people's lives and health, and society has placed a high value on these factors. Healthcare supply chain has been majorly intruded for fake medical devices and technologies. Products that are counterfeited are frequently created to reach profit from the original product's lower price. Because of its high risk, a traceability system must be set up. Most businesses are making greater efforts to prevent counterfeiting. The QR code system presented in this project is application-, security-and privacy-focused. The existing system uses non-fungible token (NFT) based solution that enhances the tokenization protocols. In our paper we propose the usage of QR technology. It is easy and affordable to create a QR code for a medical device's identification. Because QR codes are simple to use. This system stores the information in decentralized storage of the Blockchain technology system to verify the legitimacy of medical equipment, ensuring that users are not dependent on outside apps. Blockchain which is defined as the distributed ledger system that is based upon apowerful cryptographic technique and is used to capture and preserve information about medical devices, making it difficult for anybody to tamper with them. Along with Blockchain, the usage of decentralized system makes the privacy of the data more effective. The paper also allows the customer end to view and verify the medical device ownership. © 2023 IEEE.

关键词： Blockchain

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