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Enhancing sustainable 5G powered agriculture 4.0 : Summary of low power connectivity, internet of UAV things, AI solutions and research trends

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Multimedia Tools and Applications 2025年第17期84卷 17389-17433页

作者： Majumdar, Parijata Mitra, Sanjoy Bhattacharya, Diptendu Bhushan, Bharat Department of Computer Science and Engineering Techno College of Engineering Agartala Maheshkhola Madhuban799004 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh Agartala799009 India Department of Computer Science and Engineering National Institute of Technology Agartala Jirania Agartala799046 India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida201310 India

Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud Computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-related concerns and to fulfill the increasing food demand of the expanding demographics. Adoption of 5G offers numerous benefits including Ultra-Reliable Low Latency Communication (URLLC) required to reduce communication delays in time-sensitive applications, non-public networks to allocate part of the frequency spectrum on demand, and network slicing to fulfill the diverse requirements requested by a specific application etc. for realizing the full potential of Agriculture 4.0 operations. In this paper, a comprehensive review of 5G radio enhancements, enabling technologies, and multi-tier heterogeneous 5G network architecture are presented. The main focus is to concisely cover AI integration with Unmanned Aerial Vehicles (UAVs) leveraging 5G technologies and Low Power Wide Area Networks (LPWANs) for supporting intelligent, long-range, cost-effective and energy-saving data transmission across the network and seamless Agriculture 4.0 operations. Cloud computing architectures that permit centralized management of Agriculture 4.0 operations through sharing of computations, configurable networks, and servers are also studied in detail to reveal the need for edge computation as increasing number of IoT devices allows only to perform complex computations on-site. Extensive survey reveals the recent advances of 5G-enabled Agriculture 4.0 operations, future research opportunities of 5G networks including Energy Harvesting techniques for the deployment of massive to critical IoT applications, and the socio-demographic impacts of 5G networks in Agriculture 4.0 to support ubiquitous connectivity of exponentially increasing IoT devices. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Energy efficiency

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Deep Learning Model for Identification and Classification of Web based Toxic Comments

Deep Learning Model for Identification and Classification of...

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2023 International Conference in Advances in Power, Signal, and Information technology, APSIT 2023

作者： Shukla, Anubhav Arora, Deepak Amity School of Engineering and Technology Lucknow Amity University Department of Computer Science & Engineering Uttar Pradesh India

ISBN: (纸本)9798350339369

Everyday, many individuals face online trolling and receive hate on different social media platforms like Twitter, Instagram to name a few. Often these comments involving racial abuse, hate based on religion, caste are made by anonymous people over the internet, and it is quite a task to keep these comments under control. So, the objective was to develop a Machine Learning Model to help identify these comments. A Deep Learning Model (a sequential model) was made and it was trained to identify and classify a comment based on whether it is an apt comment or not. LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) that is particularly well-suited for modeling sequential data, such as text. LSTMs are capable of modeling long-term dependencies in sequential data. In the case of text classification, this means that LSTMs can take into account the context of a word or phrase within a sentence, paragraph, or even an entire document. LSTMs can learn to selectively forget or remember information from the past, which is useful for filtering out noise or irrelevant information in text. LSTMs are well-established in the field of natural language processing (NLP) and have been shown to be effective for various NLP tasks, including sentiment analysis and text classification. Binary cross-entropy is a commonly used loss function in deep learning models for binary classification problems, such as predicting whether a comment is toxic or not. Binary cross-entropy is designed to optimize the model's predictions based on the binary nature of the classification task. It penalizes the model for assigning a low probability to the correct class and rewards it for assigning a high probability to the correct class. The loss function is differentiable, which allows gradient-based optimization methods to be used during training to minimize the loss and improve the model's performance. Binary cross-entropy is a well-established loss function that has been extensively used

关键词： Sentiment analysis

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SEEMA: An Automation Framework for Vulnerability Assessement and Penetration Testing 2

SEEMA: An Automation Framework for Vulnerability Assessement...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Tyagi, Yash Shekhar, Shubham Abhishek, P. Bhardwaj, Shweta Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350347982

The world has witnessed an exponential increase in the number of Internet Connected computers due to various causes. This exponential increase and the resulting dependence have had far reaching cybersecurity implications. The primary implication being exponential rise in cybercrime which necessitates the vulnerability assessment and penetration testing (VAPT). The vulnerability assessment and penetration testing aids in offsetting any liability associated with a successful attack on an organization's asset. The current frameworks and tool available for VAPT require very high expert knowledge and are predominantly manual. Moreover, some tools available automate only certain phases of VAPT but not the whole process. This mismatch provides the opportunity to create a solution by automating the whole process while lowering the knowledge threshold. Thus, this paper provides a revamped automated framework for a novice individual and a cybersecurity professional's use. It also provides a framework for commercial use which leverages multi-agent system and certain aspects of full stack monitoring. © 2023 IEEE.

关键词： Multi agent systems

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A Dangerous Driving Behavior Detection Method Based on Improved YOLOv8s

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engineering Letters 2025年第3期33卷 721-731页

作者： Zhou, Tong Zhang, Xiaoxia Chen, Huilong School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China

Detecting dangerous driving behavior is a critical research area focused on identifying and preventing actions that could lead to traffic accidents, such as smoking, drinking, yawning, and drowsiness, through technical methods. Advanced computer vision and machine learning technologies enable efficient detection models to monitor and analyze driver behavior, improving road safety. Due to challenges posed by complex environments, this paper introduces an enhanced detection algorithm, YOLOv8s-CDS, to improve the identification of dangerous driving behaviors. First, the ConvNeXt V2 module is integrated with the C2f module to form C2fNeb2, optimizing feature extraction for behaviors like smoking or phone use. Second, the DASI (Dimension-Aware Selective Integration) module enhances detection accuracy through multi-scale fusion and dimension perception. Additionally, the SCConv module replaces the Conv module in the Bottleneck, forming C2fSCConv, which reduces spatial redundancy and improves detection efficiency.A comprehensive experimental analysis of dangerous driving image datasets demonstrates that the mean average precision (mAP) of the YOLOv8s-CDS algorithm is 91.20%, which is 2.4% higher than that of the YOLOv8s algorithm. Compared to other object detection algorithms, such as Faster R-CNN, YOLOv5s, YOLOv7s, YOLOX and YOLOv8, YOLOv8s-CDS demonstrates greater practicality in detecting dangerous driving behaviors, contributing to a reduction in traffic accidents and enhancing the safety of life and property. © 2025, International Association of Engineers. All rights reserved.

关键词： Highway accidents

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The Improved Unet Semantic Segmentation Network for Remote Sensing Images

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 1187-1195页

作者： Zhu, Hang Zhao, Ji School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

With the development of artificial intelligence, deep learning has been increasingly used to achieve automatic detection of geographic information, replacing manual interpretation and improving efficiency. However, remote sensing images themselves have the issue of slight inter-class variance and significant intra-class variance, making it challenging to extract valuable information. Additionally, the increasing resolution and size of remote sensing images in recent years have introduced more complexity in the types of information, further increasing the difficulty of extracting valuable data. This paper proposes an improved Unet semantic segmentation network (referred to as RAUnet). First, in the encoder, continuous convolutional blocks are enhanced to extract features. At the same time, the EMAM multi-scale attention module is employed for cross-channel learning, capturing information from different feature channels of the target and using the surrounding feature information to assist in distinguishing target information. To capture multi-directional long-range dependencies, the Lo2 module is used for long-range modeling, which captures not only local contextual information but also long-range dependencies. In the decoder, a Dysample upsampling module is used to restore feature details, and in the skip connection layer, features are added for feature fusion. Experimental results show that compared to mainstream models, the proposed method achieves superior segmentation results on the Potsdam and Vihingen datasets. © (2025), (International Association of Engineers). All rights reserved.

关键词： Semantic Segmentation

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Movie Recommendation System Using Machine Learning and MERN Stack

Movie Recommendation System Using Machine Learning and MERN ...

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International Conference on Data Analytics and Management, ICDAM 2022

作者： Gupta, Shikhar Rawat, Dhruv Gupta, Kanishk Yadav, Ashok Kumar Gandhi, Rashmi Gupta, Aakanshi Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789811976148

The entertainment industry is booming, and machine learning is playing a vital role in the technical world. Content consumption habits are growing more complicated and evolving at a faster rate than ever before. Machine learning-based recommendation systems forms self-sufficient system which learn from their experiences and improve without having to be explicitly coded. It is a mechanism that allows a user to find information that is relevant to him or her from huge amounts of data. Every entertainment company uses a complex recommendation algorithm to display meaningful content to a user based on his preferences. It helps them to increase their sales and retain their user base. Movie recommendations systems have various approaches such as collaborative filtering (CF) which compares users for similarity of content consumption or content-based filtering which uses the movie’s features such as year of release, genre, and actors. A hybrid approach incorporates two or more different approaches of movie recommendation. We present a solution in this paper of movie recommendation system architecture that uses MERN stack and ML and handles the cold-start problem. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Collaborative filtering

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Transformer-Based Person Re-Identification: A Comprehensive Review

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 1-19页

作者： Sarker, Prodip Kumar Zhao, Qingjie Uddin, Md. Kamal School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Bangladesh

In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE

关键词： Cameras

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Detection and Classification of Bone Fracture Using Digital Image Processing 10

Detection and Classification of Bone Fracture Using Digital ...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Makwane, Simran Kiran, Anjali Bhardwaj, Shweta Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350382471

This research paper presents an innovative approach for automated bone fracture discovery and bracket using digital image processing ways and the Scale Invariant point Transform (SIFT) algorithm. The proposed system excerpts distinctive original features from radiographic images, enabling robust identification of fracture patterns. Through a multi-step process, including pre-processing, point birth, and bracket, the system achieves high delicacy in differencing between colourful fracture types. Experimental results demonstrate the effectiveness of the system on a different dataset, showcasing its eventuality for enhancing clinical diagnostics and expediting treatment planning. This exploration contributes to the advancement of computer- backed medical imaging systems, offering a promising tool for accurate and effective fracture opinion. © 2023 IEEE.

关键词： classification Digital Image processing fracture Machine learning SIFT X-ray

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Secure vehicular data communication in Named Data Networking

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Digital Communications and Networks 2023年第1期9卷 203-210页

作者： Xiaonan Wang Xilan Chen Xingwei Wang School of Computer Science and Engineering Changshu Institute of TechnologyChangshu215500China School of Computer Science and Technology Northeastern UniversityShenyang110169China

Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data *** paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure *** paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure *** constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse *** the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are *** is evaluated,and the data results demonstrate that SCD achieves the above objectives.

关键词： Named data networking Reverse path Secure data communication

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A comprehensive review and open issues on energy aware resource allocation in cloud

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International Journal of Web engineering and technology 2023年第4期17卷 353-371页

作者： Singh, Shubham Singh, Pawan Tanwar, Sudeep Department of Computer Science and Engineering Amity University Uttar Pradesh Lucknow Campus India Department of Computer Science and Engineering Institute of Technology Nirma University India

Cloud computing is an on-demand service resource that includes applications to data centres on a pay-per-use basis. While allocating resources, the node failure causes the cloud service failures. This reduces the quality of service along with ‘extra’ energy consumption. This survey makes an evaluation on 65 papers regarding energy aware resource allocation (RA) techniques in cloud. The performances and associated highest attainments of each contribution are described in this review. In addition, the tools used for implementing the RA techniques in each paper along with type of workloads were considered. We examine a number of current or developed energy-aware RA approaches for cloud settings in this study. We also provide a thorough analysis of the various energy-conscious RA and selection strategies for cloud-based virtual machines. Lastly, the clear sketch of issues and weakness are determined that may be supportive to resolve the existing problems in this aspect. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Energy utilization

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