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Hybrid Arithmetic-Rider Optimization algorithm as new intelligent model for travelling salesman problem

International Journal of Information technology (Singapore) 2024年 1-18页

作者： Krishna, Madugula Murali Majhi, Santosh Kumar Panda, Nibedan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Odisha Burla 768018 India Department of Computer Science and Information Technology Guru Ghasidas Viswavidyalaya C.G. Bilaspur India School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar 751024 India

Travelling Salesman Problem (TSP) is one of the significant NP-hard benchmark problems for performing discrete optimization. In recent times, determining the optimal route mechanism has been implemented and ensured as an important mechanism for solving practical applications. In real-time applications, energy-saving, reaching the destination at the assigned time, and identifying the shortest route is very essential. Route determination is researched by different scientists and engineers. Numerous research is done on this problem, and it is still challenging. Therefore, meta-heuristics are involved in this problem as it is inspired by the biological species. This article constitutes the continuation of the work on adapting the TSP using the hybridized algorithm named Arithmetic-ROA (A-ROA). The ultimate aim of the proposed TSP is to reduce the distance travelled by the salesman while focusing on the entire city. To attain this optimal solution, the novel hybrid A-ROA optimizes the number of the city to be travelled. The results acquired by the new hybrid heuristic are compared with other heuristic algorithms. The computational results confirm that the developed algorithm obtained better results when compared with existing algorithms. The developed optimization algorithm also provides enhanced performance within a realistic amount of computational time. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Arithmetic Optimization Algorithm Arithmetic-Rider Optimization Algorithm Benchmark Datasets Meta-heuristic Algorithms Rider Optimization Algorithm Travelling Salesman Problem

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The Future of Online Shopping: Blockchain- Powered E-Commerce Platforms

The Future of Online Shopping: Blockchain- Powered E-Commerc...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Kiran, Ajmeera Sri, Nemani Divya Ayyasamy, Ramesh Kumar Chinnasamy, P. Vellanki, Sreelakshmi Vujwala, Maddula MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Faculty of Information and Communication Technology Kampar Malaysia School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798331540685

A cutting-edge online marketplace that uses blockchain technology to transform how we purchase and sell goods and services is known as a blockchain- powered e-commerce platform. This platform uses a decentralized network of computers, also known as nodes, to maintain a transparent and impenetrable record of all transactions instead of depending on a central authority like a bank or a company. A blockchain-based e-commerce platform is a secure, transparent, and trusted online marketplace. It does away with the need for intermediaries, improves security, and provides a world market where users can confidently conduct peer-to-peer transactions. This novel strategy could change how online commerce and shopping are performed. © 2024 IEEE.

关键词： Marketplaces

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Optical Flow Estimation in Dense Foggy Scenes with Domain-Adaptive Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1777-1788页

作者： Yan, Wending Sharma, Aashish Tan, Robby T. National University of Singapore Electrical and Computer Engineering Department Singapore Singapore Agency for Science Technology and Research Singapore Singapore

Estimating optical flow in dense foggy scenes is a challenging task. The basic assumptions for computing flow such as brightness and gradient constancy become invalid. To address the problem, we introduce a semisupervised deep learning method that can learn from real fog images without requiring the corresponding optical flow ground-truths. Our method is a multitask network, integrating the domain transformation and optical flow networks in one framework. The domain transformation is performed between foggy and clean domains. Under our semisupervised training strategy, we train our method in a supervised manner with a pair of synthetic fog images, their corresponding clean images and optical flow ground-truths. Subsequently, given a pair of real fog images and a pair of clean images that are not corresponding to each other (unpaired), in the next training batch, we train our network in an unsupervised manner. The supervised and unsupervised training processes are alternated iteratively. Since our method relies on unsupervised learning for real data, we show that it can be used for test-time training. We show in our experiments that performing test-time training improves the results further on our test data. Extensive experiments show that our method outperforms the state-of-the-art methods in estimating optical flow in dense foggy scenes. © 2020 IEEE.

关键词： Optical flows

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Severity and Disease Affected Area Measurement in Paddy and Maize Crop Leaves using Image Processing Techniques 14

Severity and Disease Affected Area Measurement in Paddy and ...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Kumar, Sunil H.R. Poornima, K.M. PES Institute of technology and Management Department of Computer Science and Engineering Shivamogga India JNN College of Engineering Department of Computer Science and Engineering Shivamogga India

Large population of India is dependent on agriculture directly or indirectly for their livelihood. To achieve better yield and quality in agricultural products, it is good to diagnose crop diseases at very initial stages. Pathogens like fungi, bacteria, viruses and adverse environment are the main reasons for agricultural crop diseases. For sustainable agriculture, continuous observation of crops from early stage may play prominent role. Early stage crop disease detection and severity measurement is very significant as far as profit in good forming is concerned. Paddy and Maize are two major crops grown in the country which not only provide feeding but also give major contribution to GDP. In the proposed work, K-means clustering with fuzzy logic is adopted to measure the disease affected area of the leaf and in turn to determine the severity of the diseases. For this experiment numbers of Paddy and Maize diseased leaves are taken. Later the results are validated with ImageJ tool to check accuracy from the proposed method. © Grenze Scientific Society, 2023.

关键词： Fuzzy logic

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Security on mobile cloud computing using cipher text policy and attribute based encryption scheme

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Materials Today: Proceedings 2023年 80卷 3059-3063页

作者： Swetha, Madireddy Latha, M. Department Of Computer Science & Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu India Vels Institute of Science Technology & Advance Studies Tamil Nadu India

Mobile Cloud Computing (MCC) is a new paradigm that has been emerged by the advances in the Cloud Computing for Mobile devices to access Cloud services. The data security challenges against the data thefting, deleting, corrupting, or exploiting are existed as the data storage and access to/from the cloud has been most popular. In order to handle these data security issues in the cloud and to provide protection for the data, some innovative encryption methods have been developed. One of such encryption methods is Attribute-Based Encryption (ABE) that has a more control on the data in cloud as which is accessed by whom. Since from the recent years, many of academic researchers have paying a widespread of attention on using advanced cryptographic techniques to securely store, process, also share data over the untrusted cloud environment. One of the promising cryptographic techniques which can solve the open challenge of regulating the fine-grained access control of important data over the distributed cloud is the Cipher text Policy – Attribute Based Encryption (CP-ABE). Therefore, a survey of work is presented in this paper on different CP-ABE methods for the secure mobile cloud computing architecture. This paper presents some works that are focused on secure data storing, accessing and sharing using CP-ABE methods over the untrusted cloud environments. All the works that are studied are analyzed and compared with each other in the results to find the best out of it. © 2021

关键词： Mobile cloud computing

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

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computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： Software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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A Deep Reinforcement Learning Framework for Reducing Energy Consumption of Server Cooling System 6th

A Deep Reinforcement Learning Framework for Reducing Energy ...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Al Munem, Abdullah Hossain, Md. Shafayat Prito, Rizvee Hassan Tuhin, Rashedul Amin Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9783031503269

Data Centers consume a tremendous amount of energy for cooling the servers. The cooling system of a data center consumes around 40–55% of the total energy consumption. Thus, it is required to reduce the energy consumption of the server cooling system to minimize the electricity cost. In this experiment, a general framework for reinforcement learning agent integrated cooling systems has been proposed to reduce the cooling cost. The proposed model has been trained and evaluated in a simulated environment. A traditional threshold-based cooling system was also evaluated in the same environment to determine the efficiency of the proposed framework. The proposed framework was able to reduce energy consumption by 21% for 36 months compared to the threshold-based cooling system. To develop the proposed model, the Deep Q-Learning algorithm has been used in this experiment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

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Speak to your Database: Streamlining Human-to-Database Communication with NLP 1

Speak to your Database: Streamlining Human-to-Database Commu...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Sthuthi, B. Sivani, J.C. Sathyalakshmi, S. Kumar Yadav, Kamleshwar Global Academy of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350339727

The paper demonstrates the successful integration of recent enhancements in Natural Language Processing (NLP) with the goal of generating accurate and meaningful Structured Query Language (SQL) queries from human language. The focus is on the methods that we have implemented in the project. The methodology we propose in this paper relies entirely on NLP techniques. Our system accepts user queries in English, which are then transformed into SQL statements using our NLP-based approach, before being executed against the database to retrieve the desired results. The student database was used in our project, as it provides a real-world example of how our NLP-based approach can be applied to retrieve data from educational institutions. The paper also outlines the various challenges and limitations faced by our system when converting human language into SQL. The paper concludes by discussing the broader implications of our approach for the field of database querying, and how it could lead to the development of more intuitive and user-friendly interfaces for interacting with databases and outlines potential directions for future improvements. © 2023 IEEE.

关键词： Query languages

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Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare

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Multimedia Tools and Applications 2024年 1-22页

作者： Almujally, Nouf Abdullah Alzahrani, Abdulrahman Hakeem, Abeer M. Attiah, Afraa Umer, Muhammad Alsubai, Shtwai Polsinelli, Matteo Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Ovarian cancer is a global health concern due to the unavailability of an effective screening strategy and is often diagnosed at a late stage with approximately 70% of the case which reduces the survival chances of patients. Initial diagnosis is challenging due to inconspicuous symptoms in its initial stages which complicate its timely diagnosis. Regular screenings, such as pelvic exams, ultrasounds, and blood tests targeting specific biomarkers can be helpful for early diagnosis. In addition, the use of machine learning models can help automate this process thereby assisting medical experts for accurate diagnosis. This study aims at timely and accurate detection of ovarian cancer using a transfer learning approach that uses the MobileNet model. Moreover, the Chi-square technique is used to extract the most impactful features for better accuracy. For experiments, the Soochow University ovarian cancer dataset is employed. Extensive experiments are performed using all features from the dataset, as well as, using Chi-square-based selective features. The best accuracy of 90.88% is achieved with the Xception model when all features are used. Experimental results show a substantial increase when selective features are used indicating a 98.49% accuracy using the MobileNet model with 20 most important features. In addition, precision, recall, and F1 scores of 99.13%, 99.27%, and 99.20% are obtained showing the model’s robustness and generalization. This study also employs Shapley additive explanations to explain the importance of various features toward the model’s output thereby providing transparency for the model’s decision-making process. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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A Novel Work on Analyzing STRESS and Depression level of Indian Population During COVID-19

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Recent Advances in computer science and Communications 2022年第6期15卷 822-831页

作者： Gupta, Amit Kumar Mathur, Priya Bijawat, Shruti Dadhich, Abhishek Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Rajasthan Jaipur India Department of Mathematics Poornima Institute of Engineering & Technology Rajasthan Jaipur India Department of Computer Science & Engineering Poornima Institute of Engineering & Technology Rajasthan Jaipur India

Objective: The world is facing the pandemic of COVID-19, which has led to a considerable level of stress and depression in mankind as well as in society. Statistical measurements can be made for early identification of the stress and depression level and prevention of the pre-vailing stressful conditions. Several studies have been carried out in this regard. The Machine learning model is the best way to predict the level of stress and depression in humankind by statistically analyzing the behavior of humans which helps in the early detection of stress and de-pression. This helps to prevent society from psychological pressures from any disaster like COVID-19. COVID-19 pandemic is one of the public health emergencies that are of great international concern. It imposes a great physiological burden and challenges on the population of the country facing the calamity caused by this disease. Methods: In this paper, the authors conducted a survey based on some questionnaires related to depression and stress and used the machine learning approach to predict the stress and depression level of humankind in the pandemic of COVID-19. The data sets were analyzed using the Multiple Linear Regression Model. The predicted score of stress and depression was mapped into DASS-21. The predictions have been made over different age groups, gender, and categories. The machine learning model is the best way to predict the level of stress and depression in humans by statistically analyzing their behavior which helps in the early detection of stress and depression. Results: Women, in general, were more stressed and depressed than men. Moreover, the people who are 45+ years of age were found to be more stressed and depressed, including male and fe-male students. The overall analysis showed that the people of India were stressed and depressed at "Serve" level due to COVID-19. It may be because students were more depressed about their study and career, women were stressed about their business as

关键词： Forecasting

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