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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Wankhede, Disha Athanikar, Abhishek Borude, Yash Chougule, Raviraj Diware, Om Department of Computer Engineering Vishwakarma Institute of Information Technology India

ISBN: (纸本)9798331300579

Software programmers may get gradually precise at expecting consequences without being clearly coded using machine learning techniques. Machine learning is based on the idea that models and algorithms may collect input data then utilize statistical investigation to determine an output, while updating results as information become obtainable, as underlying principle. For example, models may be adapted and trained to meet management expectations so that correct measures are followed to reach a certain goal. Wall Mart, a one-stop shopping mall, has been used in this system to estimate sales of various products and to study impact that various variables have on sales of products. Predictive models may be built using different features of a Wall Mart dataset and methods used to construct them, and these findings can be used to make better business choices. This Predictive Algorithm is used in many fields over the market world for their profitability. Recently, Lot of companies are going to used this Machine Learning for better AI and new technologies in market. This report Includes combination of machine learning algorithm and Features engineering to predict Walmart sales. The research topic of this paper primarily solves the problems of Walmart sales forecasting that is provided by the Kaggle competition. © Grenze Scientific Society, 2024.

关键词： Input output programs

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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Illicit Events Evaluation on the Dark Web Using NSGA-2 Algorithms Based on Energy Consumption

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Informatica (Slovenia) 2024年第18期48卷 77-96页

作者： Rawat, Romil Rajavat, Anand Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India

The proposed work objective is to adopt the non-dominated sorting genetic algorithm II (NSGA-II), a type of MOEA (multi-objective evolutionary algorithms), to reduce the dimensionality and identify the most relevant features. The objectives and hypotheses tested using NSGA-II and MOEA are to develop predictive models that best fit the problem of traffic analysis, analyse the set of features associated with the dataset, and apply NSGA techniques to reduce the data dimensionality. The work used the datasets and considered the Transnational Terrorist Hostage Event (TTHE) and Counter Trafficking Data Collaborative (CTDC) datasets, which relate to global human trafficking (HTr). The method used is focused on analysing the energy consumption (Econ) with the carbon footprint (CFP) of the proposed algorithm for different values of the main parameters on different hardware platforms, where the quality of each individual is computed based on ML algorithms. The result represents the "F-measure" metric obtained from support vector machines (SVM) and the number of features in order to determine the dimensionality, the execution time, the Econ, and the CFP during the feature engineering process. To estimate performance, the parameter values are computed using *** proposed work concludes that cybercrime employs and represents susceptible behaviour on the OSN Channels network. We identified the threat patterns by using NSGA-II and Econ analysis to establish connections between the variables in the dataset. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Support vector machines

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Reversible data hiding in encrypted images with block-based bit-plane reallocation

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Multimedia Tools and Applications 2024年第37期83卷 84911-84932页

作者： Liu, Li Chen, Chaofan Wu, Yingchun Chang, Chin-Chen Wang, Anhong College of Electronic Information and Engineering Taiyuan University of Science and Technology Taiyuan030024 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

As cloud storage and multimedia communication continue to evolve, the preservation of image privacy is becoming increasingly important. Reversible data hiding in encrypted images (RDHEI) is an effective method for enhancing the protection of personal privacy. To improve the embedding capacity, this paper introduces a novel scheme for RDHEI using block-based bit-plane reallocation. First, an effective optimized prediction method is utilized to predict the value of each pixel, followed by the calculation of prediction errors (PEs). Then, block-based bit-planes of PEs are extracted, and ten different bit-plane types are categorized to facilitate bit-plane reallocation, thereby freeing up redundant space. Finally, an adaptive encryption procedure is employed to encrypt the reallocated bit-plane, excluding the flag-bits. Experimental results demonstrate that the proposed scheme achieves average embedding rates (ER) of 3.584 bpp, 3.501 bpp, and 2.985 bpp on the three experimental datasets, all of which are higher than those of other state-of-the-art RDHEI schemes while ensuring both reversibility and security. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Energy Efficient Unequal Fault Tolerance Clustering Approach

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computer Systems Science & engineering 2023年第5期45卷 1971-1983页

作者： Sowjanya Ramisetty Divya Anand Kavita Sahil Verma NZ Jhanjhi Mehedi Masud Mohammed Baz Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadIndia School of Computer Science and Engineering Lovely Professional UniversityPhagwaraIndia Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia Department of Computer Science and Engineering Tailor’s UniversityMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia

For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network *** saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor *** of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to *** increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor *** Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster *** data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the ***,the MCH overhead *** the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.

关键词： Energy-efficiency unequal fault tolerant clustering approach particle swarm optimization master data aggregator energy efficient time division multiple access optimal nodes

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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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Effective IoT-based crop disease prediction using localise search traversing coupled with deep convolutional neural network classifier

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International Journal of Wireless and Mobile Computing 2024年第2期26卷 168-181页

作者： Vani, B.V. Guruprakash, C.D. Information Science and Engineering Sri Siddhartha Institute of Technology Karnataka Tumkur India Department Computer Science and Engineering Sri Siddhartha Institute of Technology Karnataka Tumkur India

Predicting crop disease on the image obtained from the affected crop has been a potential research topic. In this research, the Localise Search Optimisation Algorithm (LSOA) enabled deep Convolutional Neural Network (deep CNN) is used to predict the crop disease for which the dominant statistical and texture features are utilised and LSOA as a training algorithm. The experiments were done on an apple data set and a corn data set, and the results show that the LSOA-deep CNN model attains 98.474% of accuracy, 92.837% of sensitivity and 99.00% of specificity in k-fold training data and 94.683% of accuracy, 95.489% of specificity and 99.00% of specificity with 80% training data for the corn data set. With the apple data set, the developed method achieves 94.587% of accuracy, 99.00% sensitivity and 99.00% specificity under k-fold training, while for the 80% of training, 97.959% accuracy, 96.233% sensitivity and 99.005% specificity are attained. © 2024 Inderscience Enterprises Ltd.

关键词： Wireless sensor networks

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Red deer algorithm to detect the secret key of the monoalphabetic cryptosystem

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Soft Computing 2024年第17-18期28卷 10569-10582页

作者： Jain, Ashish Bansal, Sulabh Das, Nripendra Narayan Gupta, Shyam Sunder Department of Information Technology School of Information Security and Data Science Manipal University Jaipur Jaipur303007 India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Madhya Pradesh Gwalior474005 India

Encryption of a plaintext involves a secret key. The secret key of classical cryptosystems can be successfully determined by utilizing metaheuristic techniques. Monoalphabetic cryptosystem is one of the famous classical cryptosystems. In this paper, we determine the secret key of the monoalphabetic cryptosystem using a recently proposed metaheuristic technique, namely, red deer algorithm. The existing red deer algorithm framework has been tailored to solve the above considered problem. Performance of the developed red deer algorithm is compared with the following metaheuristic techniques: tabu search, genetic algorithm, and cuckoo search using three criteria, namely, effectiveness, efficiency, and accuracy. The results obtained show that the proposed red deer algorithm can compete with all the above three algorithms with respect to all the criteria. This signifies the importance of the proposed red deer algorithm is that it can be utilized to solve the similar problems effectively, efficiently, and with more accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Tabu search

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A hyperledger fabric-based EMR sharing mechanisms with proxy re-encryption and IPFS

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Cluster Computing 2024年第5期27卷 6953-6983页

作者： Huang, Der-Chen Liu, Ling-Chun Deng, Yong-Yuan Chen, Chin-Ling Zeng, Kuang-Wei Department of Computer Science and Engineering National Chung-Hsing University Taichung402 Taiwan School of Information Engineering Changchun Sci-Tech University Jilin Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

With the advent of the information age, data storage has not only developed from paper information systems to electronic information system storage, but has also extended to cloud database storage methods. To date, we can see the application of big data and cloud in various fields and levels, making induction, arrangement, transmission, and exchange of data easier and faster. The electronic and systematisation of patient medical records in the medical environment is an example. The use of electronic medical records can help improve the quality of medical care, reduce duplication of treatment, and reduce the waste of examination resources. It has many advantages, but also many risks. Medical institutions may abuse electronic medical data without the consent of patients, leading to the leakage of patient privacy data and causing serious social impact. Therefore, this study proposes a medical record exchange and sharing mechanism based on the Hyperledger blockchain architecture combined with proxy reencryption. When pharmacists need to obtain medical records for related research, patients can decide whether to share medical records, and hospitals can also revoke pharmacist access rights at any time to achieve more private, secure, and convenient sharing of medical records. The proposed scheme meets various blockchain security requirements and the BAN (Burrows–Abadi–Needham) logic proof model (Burrows–Abadi–Needham) is applied to assess the correctness of the proposed scheme. The proposed scheme performs well in terms of computational and communication costs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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