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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Subramanya, S.G. Parkavi, A. Ramaiah Institute of Technology affiliated to Visvesvaraya Technological University Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350388602

Viticulture or grape cultivation is a significant horticulture method all over the globe. Often, grapevines suffer from many viral, fungal, and bacterial infections that affect production quantity and quality. Farmers worldwide use traditional methods to detect and treat these diseases. However, they are time-consuming, labor intensive, and require significant expertise. The EfficientNetB0 model is implemented for automating the diagnosis of grape leaf diseases. The transfer learning (TL) approach is leveraged to train and fine-tune the EfficientNetB0 model using Kaggle grapevine disease dataset. The leaf image dataset represents four classes - black rot, esca, leaf blight, and healthy samples. The model's performance is evaluated based on metrics like accuracy and loss. The result shows that EfficientNetB0 gave accuracies of 92.45% and 96.73% on training and validation datasets, respectively. The model achieves better results in disease classification while maintaining computational efficiency. © 2024 IEEE.

关键词： Transfer learning

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A pressure therapy method: Monaural beats sonic resonance

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IEEE Potentials 2024年第3期43卷 17-22页

作者： Lin, Tsen-Fang Chen, Liang-Bi Southern Taiwan University of Science and Technology Department of Popular Music Industry Tainan710301 Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan

This article introduces a non-invasive pressure therapy method using monaural beats (MB) sonic resonance to promote relaxation and reduce stress. Unlike binaural beats (BB), MB requires no headphones, enhancing accessibility. By targeting ultralow brainwave frequencies, MB effectively relieves anxiety and stress. Compared to BB and other infrasound methods, MB offers advantages in terms of cost, comfort, and flexibility. Potential applications in IoT and AIoT systems for adaptive therapies are also discussed, with further research needed to realize their therapeutic potential fully. © 2024 IEEE.

关键词： Noninvasive medical procedures

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Automated Underwater Fish Species Recognition using Deep Learning-based Techniques 11th

Automated Underwater Fish Species Recognition using Deep Lea...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Anantha Prabha, P. Sachin, S. Srinithish, U. Deva Priya, M. Karthick, S. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819717231

Knowledge about fish species with continuous monitoring play a dominant role in determining short and long-term effects on ecosystems and generating ways to manage the problem through specific treatment. Categorization and identification of fish species from underwater film are critical for maintaining resources of fisheries and environmental balance. It is difficult to get an accurate result from actual underwater films owing to poor resolution and object motion. Deep Learning (DL) technology is effectively used in a variety of sectors including aquaculture. In order to identify and recognize fishes in the presence of marine background noise, this research offers architecture for fish detection system that includes an updated training model and a multi-scale fish detection module using enhanced Convolutional Neural Network (CNN). The training technique entails gradual training of enhanced CNN by initially focusing on learning tough species followed by gradual learning of new species by incrementally employing knowledge distillation loss while maintaining high performance of previously learned species. This detection model employs a DL strategy based on CNN to improve detection performance on small-sized objects. As a consequence, the issues prevalent in identifying objects underwater are reduced, and performance of the framework is improved. In contrast to current identification methods, the proposed techniques offer better accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Ensuring Trust in Blockchain-Based E-commerce Using Smart Contracts 5th

Ensuring Trust in Blockchain-Based E-commerce Using Smart Co...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Surendar, P.D. Kumar, Adhithi Satish Pavani, Venkata Azhagiri, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai600089 India

ISBN: (纸本)9789819773701

In the ever-changing realm of e-commerce, building trust is crucial for both customers and sellers. Reputation systems play a vital role in enabling informed decision-making by providing vendor ratings and reviews. Conventional reputation systems are typically inadequate in dealing with contemporary issues including privacy, identity theft, and feedback fraud. In order to address these challenges, we suggest implementing a decentralized reputation system that places a high value on privacy and effectively counters existing risks. Our architecture utilizes two permissioned blockchains, Hyperledger Indy for decentralized identity management through verifiable credentials and Zero Knowledge Proof (ZKP) technology, guaranteeing anonymity and authenticity, and Hyperledger Fabric for executing smart contracts, facilitating intricate business logic. The system incorporates verified vendor identities, transaction feedback tokens, and discount tokens to encourage genuine evaluations. Smart contracts implemented on the Hyperledger Fabric platform provide the security and transparency of the feedback process, hence reducing the occurrence of biased feedback and identity assaults. This technique provides a strong and reliable answer to the issues faced in e-commerce. It ensures the protection of private information and enables the verification of digital identities while maintaining privacy. The implementation of feedback and discount tokens promotes the provision of precise input, mitigating unjust practices and bolstering the dependability and confidence in the system. We demonstrate the effectiveness of our methodology using Zero Knowledge Proofs (ZKP), highlighting its importance in establishing confidence within the context of blockchain. Our system represents a notable advancement in creating a safe, transparent, and reliable e-commerce environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Automated Waste Sorting: A Comprehensive Approach Using Deep Learning for Detection and Classification 32

Automated Waste Sorting: A Comprehensive Approach Using Deep...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Ogrezeanu, Iulian Alexandru Suciu, Constantin Itu, Lucian Mihai 'Transilvania' University of Brasov Faculty of Electrical Engineering and Computer Science Brasov Romania

ISBN: (纸本)9798350395440

The increasing challenges associated with waste management necessitate advanced technologies to enhance the efficiency of recycling processes. In this work, we present a comprehensive study utilizing the WaRP (Waste Recycling Plant) dataset for waste detection and classification within an industrial waste sorting plant. The dataset is available on Kaggle, and it encompasses 28 recyclable waste categories, including plastic and glass bottles, cardboard, detergents, canisters, and cans. Notably, objects in the dataset exhibit complexities such as overlap, deformation, and varying lighting conditions. For the classification task, we leverage the WaRP-C dataset, consisting of cut-out image areas from the WaRP-D set with class labels. Employing diverse deep learning architectures, including CNN, Le-Net5, AlexNet, VGG16, Mobile-Net-v2, Inception, and DenseNet, we explore the efficacy of each of them in accurately classifying the waste. For the task of waste detection, we focus on the WaRP-D dataset, comprising images with full HD resolution. The YOLO v8 architecture is employed for its efficiency in detecting waste items in cluttered and complex scenes. The dataset includes 2452 training images and 522 test images, each annotated with bounding boxes for precise object localization. The integration of both classification and detection tasks contributes to a holistic approach in automating waste sorting processes, fostering advancements in sustainable waste management practices. © 2024 IEEE.

关键词： Deep learning

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Intelligent predictions of Covid disease based on lung CT images using machine learning strategy

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Materials Today: Proceedings 2023年 80卷 3744-3750页

作者： Prabha, B. Kaur, Sandeep Singh, Jaspreet Nandankar, Praful Kumar Jain, Sanjiv Pallathadka, Harikumar Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram Guntur India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Department of Computer Science and Engineering Adesh Institute of Technology Chandigarh Campus India Department of Electrical Engineering Government College of Engineering Nagpur India Medi-Caps University India Manipur International University Manipur India

Covid or Corona Virus, a term ruling the world from past two years and causes a huge destruction in all countries. One of the most important Covid disease identification method is Lung based Computed Tomography (CT) image scanning, in which it provides an effective disease identification means in clear manner. However, this Lung CT image based disease detection principles are complex to health care representatives and doctors to predict the Covid disease accurately. Several manual errors and medical flaws are raised day-by-day, so that a new systematic methodology is required to identify the Covid disease effectively with respect to machine learning principles. The machine learning principles are most popular to identify the respective disease efficiently as well as classify the disease in accurate manner without any time consumption. The infected portions of the chest are identified accurately and report to the respective person without any delay. In this paper, a new machine learning strategy is introduced called Hybrid Disease Detection Principle (HDDP), in which it is derived from the two classical machine learning algorithms called Convolutional Neural Network (CNN) and the AdaBoost Classifier. Both these algorithms are integrated together to produce a new strategy called HDDP, in which it process the lung CT image based on the machine learning factors such as pre-processing, feature extraction and classification. Based on these effective image processing strategies the proposed algorithm handles the CT images to predict the Covid disease and report to the respective user with proper accuracy ratio. This paper intends to provide effcient disease predictions as well as provide a sufficient support to medical people and patients in fine manner to assist them with modern classification algorithms. © 2021

关键词： Machine learning

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A Cascade Reaction Triggered by H-H Steric Hindrance:Dimeric Covalent Organic Frameworks on Au(111)and Dimeric Nanoribbons on Ag(111)

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Chinese Journal of Chemistry 2025年第2期43卷 161-168页

作者： Boyu Fu Jianchen Lu Jianqun Geng Yong Zhang Wei Xiong Gefei Niu Yi Zhang Lei Gao Jinming Cai Faculty of Materials Science and Engineering Kunming University of Science and TechnologyNo.68 Wenchang RoadKunmingYunnan650093 China Faculty of Science Kunming University of Science and TechnologyNo.727 Jingming South RoadKunmingYunnan650500 China Southwest United Graduate School KunmingYunnan650093 China

In on-surface synthesis,dimers are typically utilized to explore reaction mechanisms or as intermediates in the formation of final ***,constructing the innovative nanostructures with dimers as building blocks remains ***,using non-planar 2,2′,7,7′-tetrabromo-9,9′-biflurenyliden molecules,we have successfully synthesized dimeric covalent organic frameworks(COFs)on the Au(111)surface through a temperature-controlled cascade ***,the H-H steric hindrance within precursors caused by double bonds leads to selective stepwise debromination during the thermal annealing,which promotes the dimerization through intermolecular Ullmann coupling and cyclodehydrogenation reaction to form COFs primarily constituted by dimer building *** scanning tunneling microscopy/spectroscopy and density functional theory calculations,we have precisely confirmed the structural evolution and reaction ***,by introducing Ag adatoms to form C−Ag−C intermediates,we have successfully regulated the reaction path and synthesized one-dimensional nanoribbons with dimers as building *** work not only validates the strategy of synthesizing dimeric nanostructures on different surfaces through cascade reactions induced by precursor design,but also enriches the research field of surface synthesis of COFs and nanoribbons.

关键词： H-H steric hindrance Dimeric CoFs Dimeric nanoribbons Scanning tunneling microscopy Cascade reaction C-Ag-Cintermediate

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Particle Swarm Optimization versus Simulated Annealing for 5G and 6G Wireless Networks: A Comparative Study 27

Particle Swarm Optimization versus Simulated Annealing for 5...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Agrawal, Atul Baniya, Pashupati Pandey, Shraiyash Bhushan, Bharat Rakesh, Nitin ITS Engineering College Department of Computer Science and Engineering Greater Noida India IEC College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Polytechnic Institute Purdue University Department of Computer Science and Engineering West LafayetteIN United States Sharda University Department of Computer Science and Engineering Greater Noida India Pune India

ISBN: (纸本)9798350392319

The evolution from 5G to 6G represents a significant leap in wireless communication technology, moving beyond enhanced mobile broadband, low latency, and increased connectivity offered by 5G. The evolution of 5G is expected to create a whole new way to connect and communicate around the world. In this paper, we compare the performance of Particle Swarm Optimization PSO and Simulated Annealing SA in optimizing network density and network interference in 5G and6G wireless networks, focusing on the technological advancements and challenges associated with the transition. PSO is a more effective optimization algorithm than SA across multiple dimensions of network performance, particularly in the challenging context of 6G networks, where the demands for optimization are greater. These results underscore PSO's potential to significantly enhance network performance in the complex, dense environments of next-generation wireless systems. It is crucial to focus on sustainable and energy-efficient solutions to support the anticipated massive increase in connected devices and data traffic, ensuring that the technological advances are matched with responsible development practices. © 2024 IEEE.

关键词： 5G mobile communication systems

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Provisioning a Pre-Trained Network Model for Extracting Text from Images 7

Provisioning a Pre-Trained Network Model for Extracting Text...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Abisheck, V. Abiram, S. Abinesh, G. Dhanapal, R. Karpagam Academy of Higher Education Faculty of Engineering Department of Computer Science And Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350359299

Accurate text recognition and detection results for complex action-filled video images are very hard to achieve. To simplify the problem and enhance text detection and recognition capabilities, this work suggests Deep Convolutional Encoder Model (DCEM) model for categorizing text from images with an action focus. This work builds the VGG16 network and ResNet50 to discover the properties of maximally stable extremal regions and acquires the general pixel-distribution level knowledge required to categorize action-oriented video. A multitask convolutional encoder network is utilized to acquire text components which are subsequently introduced to another network model. The method defines text-based classes by combining the outputs of the proposed DCEM model. To demonstrate the efficacy of the proposed strategy, experimentation trials were conducted on text dataset (10 classes of text information). Compared to previous work, the proposed method significantly improves specific text recognition. © 2024 IEEE.

关键词： Convolution

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Deep Reinforcement Learning-Based AoI Minimization in UAV-Assisted Multi-Platoon Vehicular Networks 17

Deep Reinforcement Learning-Based AoI Minimization in UAV-As...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Du, Bochun Qu, Long Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350351637

In the current landscape of intelligent transportation, vehicle platooning has become a key strategy for improving traffic efficiency and safety. However, as multiple platoons move at high speeds, the platoon encounters an unpredictable communication environment, which poses a major challenge for coordinated control of multi-platoon vehicles. Meanwhile, the freshness of information plays a pivotal role in ensuring road traffic safety, quantified by the Age of Information (AoI). This study focuses on a UAV-assisted platooning system in Manhattan scenarios, aiming to minimize vehicle AoI while improving the communication success rate between vehicles and UAV nodes. To tackle this challenge, we model this problem as a Partially Observable Markov Decision Process (POMDP). We propose a trajectory and scheduling strategy based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) approach. Additionally, to better adapt to the dynamic nature of multi-platoon vehicular networks, we integrate the Prioritized Experience Replay (PER) technique to prioritize learning and processing of critical experience samples. Through simulation experiments and comparisons with baseline methods, the superiority of the proposed algorithm is verified. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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