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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Shrivas, Shaswat Rai, Aditya Lakshmi, M. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

Detecting deepfake content presents a formidable challenge, necessitating advanced methodologies. This paper proposes a holistic strategy employing Facenet-pytorch, MTCNN, and InceptionResnetV1 for robust deepfake detection. Facenet-pytorch serves as the cornerstone for facial feature recognition, while MTCNN efficiently identifies faces in images during preprocessing. InceptionResnetV1 scrutinizes visual details to detect subtle anomalies indicative of manipulation, such as unnatural facial expressions and incongruent lip synchronization. Our approach underscores the importance of maintaining a delicate balance between accurate detection and individual privacy rights. By leveraging these advanced models, we achieve significant strides in differentiating manipulated content from authentic media, contributing to the ethical deployment of deepfake detection technologies. © 2024 IEEE.

关键词： Biometrics

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EnhancedNet Deep Learning Model for Plant Disease Classification 1

EnhancedNet Deep Learning Model for Plant Disease Classifica...

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1st International Conference on Logistics, ICL 2024

作者： Umer, Muhammad Javed, Ali Dawood, Hussain University of Engineering and Technology Department of Software Engineering Taxila47050 Pakistan School of Computing Skyline University College Sharjah United Arab Emirates

ISBN: (纸本)9798331518844

Agricultural production vitally contribute to country's economic growth and food needs;however, it depends upon the production and quality of crops. Plants are affected easily due to climate changes and low-quality water. Plants health is examined manually by farmers or pathologists;however, it is not viable to examine each plant regularly due to larger areas of field and limited human resource. If the plant disease gets diagnosed at early stage, the precautionary measures can be taken on time and quality can be improved. There are several solutions available based on modern machine learning and deep learning techniques however, precise prediction and classification is challenging due to the limitations of low light or blurry images. We propose a deep learning-based approach which classifies the healthy and diseases plant using blurry images. Our method addresses the problem using improved layers activation, lesser parameters, using radiant pixels and active neurons. We improved the classification of smaller size and blur images by adding the pooling and dropout layers. Our solution addresses the vanishing gradient and high validation loss problems using a smaller number of parameters as compared to other convolutional neural networks. We evaluated the performance of the proposed method on plant village dataset consisting of a diverse collection of 20600 images. We achieved overall 98.3 % accuracy by minimizing the vanishing gradient, high validation loss and overfitting problem. © 2024 IEEE.

关键词： Plant diseases

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A Fertilizer Recommendation System for Rice Leaf Plant 9

A Fertilizer Recommendation System for Rice Leaf Plant

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Murugan, Subhiksha Manikandan, A. Hariprasad, S. College of Engineering and Technology Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur Chennai603203 India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Chennai603203 India

ISBN: (纸本)9798350377972

The secret to increasing crop productivity and sustainability is precision farming. Optimizing fertilizer use is essential in rice cultivation to satisfy the unique nutrient requirements of crops while reducing environmental effect. Conventional techniques focus on general principles, which frequently result in reduced yields, deteriorated soil, and inefficient nutrient usage. The AI-based fertilizer recommendation system RiceFert, created for rice leaf plants, is presented in this work. The system examines crop-specific information, soil characteristics (such as type and nutrient levels), and environmental data (temperature, humidity, and wetness) using a Random Forest (RF) model and deep learning techniques. Data preparation, feature extraction, and model training are all part of the process, which compares classifiers such as Random Forest, SVM, Gradient Boosting, and Naive Bayes. The Random Forest model is superior at recommending fertilizer based on performance criteria like accuracy, precision, recall, and F1-score. This focused strategy reduces excessive or inadequate fertilizer use, increasing rice output and promoting sustainable farming. RiceFert demonstrates the potential of AI in precision agriculture by offering farmers personalized guidance that improves crop output, lowers costs, encourages environmental conservation, and helps farmers make better decisions. © 2024 IEEE.

关键词： Deep Learning Fertilizer Recommendation Precision Agriculture Rice Crop

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An Algorithm for Enhancing Privacy-Utility Tradeoff in the Privacy Funnel and Other Lift-based Measures 17

An Algorithm for Enhancing Privacy-Utility Tradeoff in the P...

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17th International Conference on Signal Processing and Communication Systems, ICSPCS 2024

作者： Zarrabian, Mohammad A. Sadeghi, Parastoo Australian National University College of Engineering Computing and Cybernetics Canberra Australia The University of New South Wales School of Engineering and Technology Canberra Australia

ISBN: (纸本)9798350389630

This paper investigates the privacy funnel, a privacy-utility tradeoff problem in which mutual information quantifies both privacy and utility. The objective is to maximize utility while adhering to a specified privacy budget. However, the privacy funnel represents a non-convex optimization problem, making it challenging to achieve an optimal solution. An existing proposed approach to this problem involves substituting the mutual information with the lift (the exponent of information density) and then solving the optimization. Since mutual information is the expectation of the information density, this substitution overestimates the privacy loss and results in a final smaller bound on the privacy of mutual information than what is allowed in the budget. This significantly compromises the *** overcome this limitation, we propose using a privacy measure that is more relaxed than the lift but stricter than mutual information while still allowing the optimization to be efficiently solved. Instead of directly using information density, our proposed measure is the average of information density over the sensitive data distribution for each observed data realization. We then introduce a heuristic algorithm capable of achieving solutions that produce extreme privacy values, which enhances utility. The numerical results confirm improved utility at the same privacy budget compared to existing solutions in the ***, we explore two other privacy measures, 1-norm and strong χ2-divergence, demonstrating the applicability of our algorithm to these lift-based measures. We evaluate the performance of our method by comparing its output with previous works. Finally, we validate our heuristic approach with a theoretical framework that estimates the optimal utility for strong χ2-divergence, numerically showing a perfect match. © 2024 IEEE.

关键词： Differential privacy

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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Computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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Gauze:enabling communication-friendly block synchronization with cuckoo filter

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Frontiers of Computer Science 2023年第3期17卷 113-123页

作者： Xiaoqiang DING Liushun ZHAO Lailong LUO Junjie XIE Deke GUO Jinxi LI College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Computer Science and Technology Xidian UniversityXi’an 710071China Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China Institute of Systems Engineering AMSPLABeijing 100141China

Block synchronization is an essential component of blockchain ***,blockchain systems tend to send all the transactions from one node to another for ***,such a method may lead to an extremely high network bandwidth overhead and significant transmission *** is crucial to speed up such a block synchronization process and save bandwidth consumption.A feasible solution is to reduce the amount of data transmission in the block synchronization process between any pair of ***,existing methods based on the Bloom filter or its variants still suffer from multiple roundtrips of communications and significant synchronization *** this paper,we propose a novel protocol named Gauze for fast block *** utilizes the Cuckoo filter(CF)to discern the transactions in the receiver’s mempool and the block to verify,providing an efficient solution to the problem of set reconciliation in the P2P(Peer-to-Peer Network)*** up to two rounds of exchanging and querying the CFs,the sending node can acknowledge whether the transactions in a block are contained by the receiver’s mempool or *** on this message,the sender only needs to transfer the missed transactions to the receiver,which speeds up the block synchronization and saves precious bandwidth *** evaluation results show that Gauze outperforms existing methods in terms of the average processing latency(about lower than Graphene)and the total synchronization space cost(about lower than Compact Blocks)in different scenarios.

关键词： block synchronization cuckoo filter probabilistic data structure

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Enhancing Network Design through Statistical Evaluation of MANET Routing Protocols

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Computers, Materials & Continua 2024年第7期80卷 319-339页

作者： Ibrahim Alameri Tawfik Al-Hadhrami Anjum Nazir Abdulsamad Ebrahim Yahya Atef Gharbi Faculty of Medical Sciences Jabir ibn Hayyan Medical UniversityAlkufa QuNajaf54001Iraq Computer Science Department School of Science and TechnologyNottingham Trent UniversityNottinghamNG118NSUK Department of Computing Faculty of Engineering Sciences&Technology(FEST)Hamdard UniversityKarachi74600Pakistan Department of Information Technology College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia Department of Information System College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and *** articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility *** study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network *** findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network *** paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.

关键词： Routing protocols statistical approach friedman MANET

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Classification of Speech Signal Using CNN-LSTM 3rd

Classification of Speech Signal Using CNN-LSTM

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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Gayathri, R. Sheela Sobana Rani, K. Aravindhan, K. Department of Electronics and Communication Engineering Sri Ramakrishna Institute of Technology Coimbatore India Karpagam College of Engineering Coimbatore India School of Computing and Information and Technology Reva University Bangalore India

ISBN: (纸本)9789819726707

Speech categorisation is a broad study that has attracted a lot of interest recently. Understanding other people's emotions and responding appropriately is the largest distinction between robots and people. Speech signals are categorised in this article using both conventional machine learning methods and deep learning algorithms. Speech emotion is categorised using the interactive emotional binary motion capture (IEMOCAP) and Ryerson affective voice and audio-visual database of songs (RAVDESS) databases. The expanded dataset is mixed with the voice signals once they have been transformed into spectrograms. This model incorporates CNN + LSTM, attention, and a novel type of spectrogram frequency distribution based on the spectrogram’s properties. Mel-frequency cepstral coefficients guarantee improved speech feature performance in tasks requiring emotion categorisation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spectrographs

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CPIE: A Spatiotemporal Visual Analytic Tool to Explore the Impact of Coal Pollution

CPIE: A Spatiotemporal Visual Analytic Tool to Explore the I...

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2024 IEEE Workshop on Energy Data Visualization, EnergyVis 2024

作者： Jin, Sichen Henneman, Lucas Roberts, Jessica Georgia Institute of Technology School of Interactive Computing United States George Mason University College of Engineering and Computing Sid and Reba Dewberry Department of Civil Environmental and Infrastructure Engineering United States

ISBN: (纸本)9798350379235

This paper introduces CPIE (Coal Pollution Impact Explorer), a spatiotemporal visual analytic tool developed for interactive visualization of coal pollution impacts. CPIE visualizes electricitygenerating units (EGUs) and their contributions to statewide Medicare deaths related to coal P M2.5 emissions. The tool is designed to make scientific findings on the impacts of coal pollution more accessible to the general public and to raise awareness of the associated health risks. We present three use cases for CPIE: 1) the overall spatial distribution of all 480 facilities in the United States, their statewide impact on excess deaths, and the overall decreasing trend in deaths associated with coal pollution from 1999 to 2020;2) the influence of pollution transport, where most deaths associated with the facilities located within the same state and neighboring states but some deaths occur far away;and 3) the effectiveness of intervention regulations, such as installing emissions control devices and shutting down coal facilities, in significantly reducing the number of deaths associated with coal pollution. © 2024 IEEE.

关键词： Coal transportation

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham Science Publishers.

关键词： Convolutional neural networks

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