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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Ayyasamy, Ramesh Kumar Shaikh, Farhan Bashir Lah, Nur Syadhila Binti Che Kalhoro, Shadab Chinnasamy, P. Krisnan, Saraswathy Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Kampar Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Kampar Kampar Malaysia

ISBN: (纸本)9798350348217

Cloud computing, big data analytics, and the internet of things are just a few of the recent technological marvels that have surprised organisations, particularly when it comes to managing innovation. The current study investigates the impact of the internet of things, big data analytics and cloud computing as digital capabilities on information systems and firms' innovation performance via a mediating role of dynamic and innovation capabilities. International entrepreneurial orientation has been taken as a moderator in this study. The study uses a quantitative method research approach conducting survey questionnaires from manufacturing SMEs in Malaysia. The conclusions of this study could aid the industry in identifying, assessing, and strengthening its skills in the ever-evolving technology trends, allowing them to produce new business models and innovate through information systems. People will have more awareness about necessary technological advancements and economic and job options as a result of their growth, and their social status will improve © 2023 IEEE.

关键词： Data Analytics

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Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

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Intelligent Automation & Soft Computing 2023年第8期37卷 2117-2132页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Amel Ali Alhussan Marwa M.Eid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansouraEgypt

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and ***,the accurate prediction can be realized using the recent advances in machine learning and predictive *** research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)*** proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting *** optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic *** prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed *** results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other *** addition,statistical experiments are conducted to prove the statistical difference of the proposed *** results of these tests confirmed the expected outcomes.

关键词： Stochastic fractal search dipper throated optimization energy consumption long short-term memory prediction models

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Domain Modelling For A Lightweight Convolutional Network Focused On Automated Exudate Detection in Retinal Fundus Images 9

Domain Modelling For A Lightweight Convolutional Network Foc...

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9th International Conference on information technology Trends, ITT 2023

作者： Aydin, Burcu Ataer Hussain, Muhammad Hill, Richard Al-Aqrabi, Hussain University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom Higher Colleges of Technology Faculty of Computer Information Science Sharjah United Arab Emirates

ISBN: (纸本)9798350327502

Retinal Fundus images contain important signs implying various stages of diabetic retinopathy. One of the early signs of diabetic retinopathy is exudates. The timely detection of this sign from fundus images can prevent or at least suppress the advancement of this condition. To this effect, this research presents a CNN-based automated exudate detection architecture for the timely detection of this early sign. In order to overcome the challenges of a limited dataset several domain specific augmentations are proposed for improving the generalization capacity of the developed architecture. The lightweight architecture consists of only 6.42 million parameters, compared to Resnet-18 (11.69 million) whilst achieving an overall F1 score of 89% © 2023 IEEE.

关键词： computer vision

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Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

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computers, Materials & Continua 2023年第3期74卷 5607-5623页

作者： Abdallah Elhigazi Abdallah Mosab Hamdan Shukor Abd Razak Fuad A.Ghalib Muzaffar Hamzah Suleman Khan Siddiq Ahmed Babikir Ali Mutaz H.H.Khairi Sayeed Salih Faculty of Engineering Future UniversityKhartoum 10553Sudan Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Information Assurance and Security Research School of ComputingUniversiti Teknologi MalaysiaJohor81310Malaysia School of Psychology and Computer Science University of Central LancashirePreston PR12HEUK Deaptement of Management Information Systems College of Administrative ScienceApplied Science UniversityAl Eker623Bahrain Deaptement of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh11461Saudi Arabia

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 *** to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate ***,the security design of the wireless structure is vulnerable to versatile *** example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false *** security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world *** paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks *** proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the *** scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in *** proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related *** experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

关键词： 802.11 media access control(MAC) wireless local area network(WLAN) artificial neural network denial-of-service(DoS)

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Toward Anomaly Detection in the Blockchain Using Numerical Association Rule Mining 21

Toward Anomaly Detection in the Blockchain Using Numerical A...

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21st IEEE International Symposium on Intelligent Systems and Informatics, SISY 2023

作者： Kramberger, Renata Fister, Iztok Kamisalic, Aida Zagreb University of Applied Sciences Department of Information Technology and Computing Zagreb Croatia University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia

ISBN: (纸本)9798350343366

With the global increase in the popularity of cryptocurrencies, the need for anomaly detection and fraudulent behavior is reaching an all-Time high. In our paper, we propose a novel method of anomaly detection with the use of Numerical Association Rule Mining with Differential Evolution. The experiment was conducted by using the Dogecoin blockhain, and the dataset contained all of the transactions from one month. Our results contained 303 rules, with the best fitness function value of 0.8. © 2023 IEEE.

关键词： Association rules

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Advanced Cryptocurrency Price Prediction: Incorporating Time-Reversed ARIMA and BVIC 19

Advanced Cryptocurrency Price Prediction: Incorporating Time...

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19th International Conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Luc, Minh Tuan Senkerik, Roman Dang, Tran Khanh Dept. of Computer Science Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava-Poruba Czech Republic Center for Applied Information Technology Ton Duc Thang University Ho Chi Minh city Viet Nam Institute for Experiential Technology Van Lang University Ho Chi Minh city Viet Nam Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh city Viet Nam

ISBN: (纸本)9798331507817

The highly volatile nature of cryptocurrencies, particularly Bitcoin, exhibits extreme price volatility, making accurate price prediction a challenging task for traditional forecasting models. This paper proposes an enhanced ARIMA model, called ARIMA_T, which incorporates time-reversed data and optimizes model selection using the Backward Validated information Criterion (BVIC). The ARIMA_T model aims to improve forecasting accuracy by capturing both linear and non-linear patterns in the data that standard ARIMA models often overlook. We apply this model to predict Bitcoin prices over short and medium-term horizons, specifically 1-day, 7-days, and 30-days ahead predictions. The dataset spans from April 2013 to February 2024. Experimental results demonstrate that ARIMA_T consistently outperforms the traditional ARIMA model across all forecast horizons, as evidenced by lower Root Mean Square Error (RMSE) values. The ARIMA_T model provides more accurate and robust predictions, particularly for short-term forecasting, making it a valuable tool for navigating the volatile cryptocurrency market. This study also discusses the limitations of both models in handling longer-term predictions and suggests future work in integrating ARIMA_T with machine learning techniques for further improvements. © 2025 IEEE.

关键词： Prediction models

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Self-Configured Workflow Platform for MapReduce Job Execution in Cloud Environment

Self-Configured Workflow Platform for MapReduce Job Executio...

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2022 Applied Informatics International Conference, AiIC 2022

作者： Saadoon, Muntadher Admodisastro, Novia University of Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Universiti Putra Malaysia Faculty of Computer Science and Information Technology Selangor Serdang43400 Malaysia

ISBN: (数字)9781728184289

ISBN: (纸本)9781728184289

Hadoop MapReduce becomes the essential framework to store, process and analyse big data in large-scale computing environments. The framework provides numerous opportunities to handle data-intensive applications like web crawling and indexing, data mining, anomaly prediction and machine learning. However, manual configurations of the framework parameters expose to human errors and lead to performance overhead and resource consumption penalties. Existing deployment automation solutions intended to mitigate the limitations by only facilitating the application execution workflow without considering crucial conditions that violates the overall performance after setting up the infrastructure parameters. This paper proposes a self-configured workflow platform with a user-friendly collaborating environment that provides automated setups of the framework paraments to obtain ideal performance compared to the native deployment of Hadoop MapReduce. The self-configured workflow platform has been evaluated in experiments on a real Hadoop cloud-based cluster and the results shows that the execution times are comparable to the native application. © 2022 IEEE.

关键词： MapReduce

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Landslide susceptibility assessment along the major transport corridor using decision tree model: a case study of Kullu-Rohtang Pass

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International Journal of Business Intelligence and Data Mining 2023年第1期24卷 1-24页

作者： Nirbhav Malik, Anand Maheshwar Prasad, Mukesh Department of Geography Delhi School of Economics University of Delhi Delhi India Swami Shraddhanand College University of Delhi Delhi India TGT Computer Science Directorate of Education Delhi Government Delhi India Faculty of Engineering and Information Technology School of Computer Science Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia

To lessen damages from landslides, the key challenge is to predict the events precisely and accurately. The objective of this study is to assess landslide susceptibility in the study area. To achieve this objective, a detailed landslide inventory has been prepared based on imagery data and frequent field visits of 153 rock slides and 44 debris slides. Nine landslide factors were prepared initially and their relationships with each other and with the type of landslide was analysed. information gain ratio measure is used to eliminate triggering factors with least score. Train_test_split method was used to classify the dataset into training and testing groups. Decision tree classification model of machine learning was applied for landslide susceptibility model (LSM). The performance was evaluated using classification report and receiver operating characteristic (ROC) curve. Results obtained have proven that the decision tree classification model performed well with good accuracy in forecasting landslide susceptibility. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Landslides

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Artificial Intelligence for Improved Maternal Healthcare: A Systematic Literature Review 16

Artificial Intelligence for Improved Maternal Healthcare: A ...

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16th IEEE AFRICON, AFRICON 2023

作者： Chemisto, Musa Gutu, Tar J.L. Kalinaki, Kassim Mwebesa Bosco, Darlius Egau, Percival Fred, Kirya Tim Oloya, Ivan Rashid, Kisitu Department of Electronics and Computer Engineering Soroti Uganda Department of Computer Science Mbale Uganda Isbat University Faculty of Information and Communications Technology Kampala Uganda Soroti Uganda Soroti Uganda

ISBN: (纸本)9798350336214

The integration of artificial intelligence (AI) in maternal health is a promising avenue for improving pregnancy, early childhood, and postnatal care. This systematic review analyzed 31 articles retrieved from Web of Science, PubMed, and Scopus, which were classified using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and Mendeley referencing tool. Our interpretive study found that AI applications in maternal health can predict 48% of maternal complications using electronic medical records (EMR), 29% using medical images, 19% using genetic markers, and 4% using other medical features such as fetal heart rates and sensors. The accuracy of prematurity prediction using AI was 95.7%, while the XGBoost technique predicted neonatal mortality with 99.7% accuracy. The study underscores the potential benefits of AI in maternal healthcare and highlights the need for further research to improve maternal and child health outcomes, especially in resource-constrained sub-Saharan African regions where maternal mortality rates are significantly high. © 2023 IEEE.

关键词： Artificial intelligence

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Optimal Resource Allocation Method for Device-to-Device Communication in 5G Networks

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computers, Materials & Continua 2022年第4期71卷 1-15页

作者： Fahd N.Al-Wesabi Imran Khan Saleem Latteef Mohammed Huda Farooq Jameel Mohammad Alamgeer Ali M.Al-Sharafi Byung Seo Kim Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan Department of Medical Instrumentation Techniques Engineering Electrical Engineering Technical CollegeMiddle Technical UniversityBaghdad10013Iraq Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA Department of Software and Communications Engineering Hongik UniversityKorea

With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing ***,how to obtain a higher data rate,wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of ***-to-Device(D2D)communication technology and other frontier communication technologies have ***-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular *** has become one of the key technologies of the fifth-generation mobile communications system(5G).D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization,enhance network coverage,reduce transmission delay and improve system throughput,but it would also bring complicated and various interferences due to reusing cellular resources at the same *** resource management is one of the most challenging and importing issues to give full play to the advantages of D2D *** resource allocation is an important factor that needs to be addressed in D2D ***,this paper proposes an optimization method based on the game-matching *** main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching *** results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms.

关键词： D2D communication resource allocation optimization networks

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