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QoS-Based Beamforming and Compression Design for Cooperative Cellular Networks via Lagrangian Duality

IEEE Transactions on Signal Processing 2025年

作者： Fan, Xilai Liu, Ya-Feng Liu, Liang Chang, Tsung-Hui Chinese Academy of Sciences State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong The Chinese University of HongKong School of Science and Engineering Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China

This paper considers the quality-of-service (QoS)-based joint beamforming and compression design problem in the downlink cooperative cellular network, where multiple relay-like base stations (BSs), connected to the central processor via ratelimited fronthaul links, cooperatively transmit messages to the users. The problem of interest is formulated as the minimization of the total transmit power of the BSs, subject to all users' signal-to-interference-plus-noise ratio (SINR) constraints and all BSs' fronthaul rate constraints. In this paper, we first show that there is no duality gap between the considered joint optimization problem and its Lagrangian dual by showing the tightness of its semidefinite relaxation (SDR). Then, we propose an efficient algorithm based on the above duality result for solving the considered problem. The proposed algorithm judiciously exploits the special structure of an enhanced Karush-Kuhn-Tucker (KKT) conditions of the considered problem and approaches the solution that satisfies the enhanced KKT conditions via two fixed point iterations. Two key features of the proposed algorithm are: (1) it is able to detect whether the considered problem is feasible or not and find its globally optimal solution when it is feasible;(2) it is highly efficient because both of the fixed point iterations in the proposed algorithm are linearly convergent and function evaluations in the fixed point iterations are computationally cheap. Numerical results show the global optimality and efficiency of the proposed algorithm. © 1991-2012 IEEE.

关键词： Lagrange multipliers

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Deep Learning for Plant Disease Detection and Crop Yield Prediction based on NPP-WPF Analysis in Smart Agriculture 7

Deep Learning for Plant Disease Detection and Crop Yield Pre...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Amudha, S. Bindu, G. Sankar, Sasi Rekha Poongothai, E. VijayaLakshmi, K. Sarveshwaran, Velliangiri Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai602203 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350341485

Despite a sharp rise in population, agriculture still provides food for all people and acts as a backbone of the country. Nowadays, no proper guidance given to farmers and they are not able to analyze the nature of soil, disease of plant and its requirements. This leads to the depletion of farming in future. Crop diseases must be identified and prevented in order to increase the productivity. To overcome this, the nature of the soil is analyzed through various parameters like Nitrogen-Potassium-Phosphorus Content in soil, Humidity, Temperature, pH value of the soil, Rainfall using sensors, plants disease using cameras in real-time environment. These parameters are obtained using various IoT Sensors, cameras and existing Data, are analyzed through ML Algorithms. The proposed system consists of two modules in crop yield prediction system using the features NPP-WPF whereas NPP stands for Nitrogen-Potassium-Phosphorus and WPF stands for Water Content-Pesticide-Fertilizer, the Farmers can be guided with the details of the crop that is suitable for the soil and about Fertilizer, Pesticide, Water requirements. The second module called as automatic disease detection system uses CNN, ResNet, VGGNet, and Mobile net models to recognize and diagnose plant diseases from their leaves at the early stage. The proposed model is implemented with the dataset of 14 different species of plants and with the categorical classes of 38 classes. The result analysis shows that the suggested model provides accuracy of 98.2% when compared to the existing state-of-the-art methodologies. © 2023 IEEE.

关键词： Phosphorus

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Enhancement in Quality of Routing Service Using Metaheuristic PSO Algorithm in VANET Networks

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Soft computing 2023年第5期27卷 2739-2750页

作者： Javadpour, Amir Rezaei, Samira Sangaiah, Arun Kumar Slowik, Adam Mahmoodi Khaniabadi, Shadi School of Computer Science and Technology Guangzhou University Guangzhou510006 China Bernoulli Institute for Mathematics and Computer Science University of Groningen Groningen Netherlands School of Computing Science and Engineering VIT University Vellore India Department of Industrial Engineering and Management National Yunlin University of Science and Technology Douliu Taiwan Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland Department of Computer Science and Engineering USM Engineering Campus Pulau Pinang George Town14300 Malaysia

Vehicular ad hoc Networks (VANETs) can be designed in a way to organize road protection with no specific need for any fixed infrastructure. Accordingly, the movement of all vehicles can be planned according to perceived information, and Quality of Services Routing (QoSR) algorithms can be pressured according to its available options, paths, and links, and according to the criteria and reliability of the QoSR. Ensure that QoSR is aware of the environment of the network of vehicles, including location of vehicles, direction, and speed. This study is to reduce the effects of unpredictable problems on the best pathway to replace the broken path/link. A QoSR with Particle Swarm Optimization (QoSR-PSO) is used in this article for improving QoSs in vehicular ad hoc networks. By modeling the behavior of a group of particles, particle swarm optimization algorithms find the best possible solution to the problem. In order to perform simulation experiments, NS2 simulator and VanetMobisim have been used. The comparison results with benchmark studies show the improvement in packet delivery rate (PDR), delay, Packet Drop, and overload. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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A DEEP LEARNING BASED DISCONTINUOUS GALERKIN METHOD FOR HYPERBOLIC EQUATIONS WITH DISCONTINUOUS SOLUTIONS AND RANDOM UNCERTAINTIES

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Journal of computational mathematics 2023年第6期41卷 1281-1304页

作者： Jingrun Chen Shi Jin Liyao Lyu School of Mathematical Sciences University of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China School of Mathematical Sciences Institute of Natural Sciencesand MOE-LSCShanghai Jiao Tong UniversityShanghai200240China Department of Computational Mathematics Scienceand EngineeringMichigan State UniversityEast LansingMI48824USA

We propose a deep learning based discontinuous Galerkin method(D2GM)to solve hyperbolic equations with discontinuous solutions and random *** main computational challenges for such problems include discontinuities of the solutions and the curse of dimensionality due to *** learning techniques have been favored for high-dimensional problems but face difficulties when the solution is not smooth,thus have so far been mainly used for viscous hyperbolic system that admits only smooth *** alleviate this difficulty by setting up the loss function using discrete shock capturing schemes–the discontinous Galerkin method as an example–since the solutions are smooth in the discrete *** convergence of D2GM is established via the Lax equivalence theorem kind of *** high-dimensional random space is handled by the Monte-Carlo *** a setup makes the D2GM approximate high-dimensional functions over the random space with satisfactory accuracy at reasonable *** D2GM is found numerically to be first-order and second-order accurate for(stochastic)linear conservation law with smooth solutions using piecewise constant and piecewise linear basis functions,*** examples are given to verify the efficiency and the robustness of D2GM with the dimensionality of random variables up to 200 for(stochastic)linear conservation law and(stochastic)Burgers’equation.

关键词： Discontinuous Galerkin method Loss function Convergence analysis Deep learning Hyperbolic equation

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Two-Stage Submodular Maximization Under Knapsack Problem

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Tsinghua science and Technology 2024年第6期29卷 1703-1708页

作者： Zhicheng Liu Jing Jin Donglei Du Xiaoyan Zhang Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China College of Taizhou Nanjing Normal UniversityTaizhou 225300China Faculty of Management University of New BrunswickFrederictonE3B 5A3Canada School of Mathematical Science Institute of Mathematicsand Ministry of Education Key Laboratory of NSLSCSNanjing Normal UniversityNanjing 210023China

Two-stage submodular maximization problem under cardinality constraint has been widely studied in machine learning and combinatorial *** this paper,we consider knapsack *** this problem,we give n articles and m categories,and the goal is to select a subset of articles that can maximize the function F(S).Function F(S)consists of m monotone submodular functions fj,j=1,2,…,m,and each fj measures the similarity of each article in category *** present a constant-approximation algorithm for this problem.

关键词： submodular function knapsack constraint matroid

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Secure E-Voting System using Deep Learning Techniques 2

Secure E-Voting System using Deep Learning Techniques

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2nd International Conference on Innovative Sustainable computational Technologies, CISCT 2022

作者： Kanchana, M. Gopirajan, P.V. Ramar, Kaladevi Hariharan, Shanmugasundaram School of Computing Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chennai India Department of Computer Science and Engineering Chennai India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665474160

Democracy is vital in many emerging economies because it places power in control of the general public, who choose government via the electoral procedure. Elections rely heavily on polling places and people. Numerous changes have happened in electoral processes since the year of the republic till now. Subsequently, Ballot sheets were implemented as a voting medium, but this was later replaced by electronic voting machines, in which voters must select from a list of buttons, each symbolizing a symbol. This paper proposes techniques for ensuring voter identification and privacy through fingerprint scanners and other biometric identification techniques-based machine learning algorithms. As a result, we offered a new approach to speed up the voting process and eliminate similar issues. For face identification, we employed Convolutional Neural Network (CNN) architectures such as Alex Net, Visual Geometry Group (VGG-16), and Random Forest Algorithms in this study. The accuracy provided by Alex Net, VGG-16, and Random Forest (R.F.) Algorithms was 94.16%, 98.45%, and 92%, respectively. VGG-16 architecture outperformed all other algorithms with a 98.45% accuracy. We utilized an ATmega328-based Arduino for finger detection. © 2022 IEEE.

关键词： Biometrics

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Twitter sentiment analysis using conditional generative adversarial network

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International Journal of Cognitive computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters 1

Cloud Based Automated Control System Workshops and Rooms for...

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1st International Conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kumar, Dinesh Kuntal, Ravinder Singh Deep, Pragya Chamoli, Anup Shekhar Singh, Pinki Mandal, Rishabh SRM Institute of Science & Technology Department of Computer Science and Engineering UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India IIMT College of Engineering Department of AI & DS Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun248001 India Accurate College of Engineering & Management Department of Computer Science and Engineering Greator Noida India Tula's Institute Department of Computer Science and Engineering UK Dehradun India

ISBN: (纸本)9798331518578

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing. © 2025 IEEE.

关键词： Smart manufacturing

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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Computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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Secure Goods Storage and Anti-Theft Approach using Ethereum Blockchain

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Procedia Computer science 2024年 233卷 1-11页

作者： Likhitha Amasala Mahesh Ponnuru Srideviponmalar P School of Computer Science and Engineering Vellore Institute of Technology Amaravathi 522237 India School of Computing SRM Institute of Science and Technology Potheri 603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri 603203 India

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors.

关键词： Ethereum Blockchain Secure Goods Storage Anti-theft Interplanetary File System Smart Contracts Ownership verification

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