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11th International Conference on computer, Control, informatics and its Applications, IC3INA 2024

作者： Ilmi, Muhammad Adam Fahmil Doewes, Afrizal Wiharto Wardani, Dewi Wisnu Cahyono, Hasan Dwi Department of Informatics Faculty of Information Technology Data Science Universitas Sebelas Maret Surakarta Indonesia

ISBN: (纸本)9798331542313

Unpredictable weather changes, such as sudden rain, can disrupt daily activities, particularly tasks like drying clothes. To address this challenge, this study presents a smart home system that forecasts rain using a trained Artificial Neural Network (ANN) algorithm. The system employs IoT sensors to collect five critical weather parameters—temperature, humidity, air pressure, wind speed, and light intensity. Data from these sensors are used to train the Backpropagation ANN model. The practical application of this model is demonstrated through a prototype that integrates a smart house roof and a servo-driven clothesline system. When rain is predicted, the servo moves the clothesline under the roof;otherwise, it positions the clothesline outside. The system achieved an accuracy of 0.86334, validating its effectiveness in mitigating the impact of sudden weather changes on clothes drying. © 2024 IEEE.

关键词： Rain

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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Towards Blockchain-Based Secure BGP Routing,Challenges and Future Research Directions

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computers, Materials & Continua 2024年第5期79卷 2035-2062页

作者： Qiong Yang Li Ma Shanshan Tu Sami Ullah MuhammadWaqas Hisham Alasmary Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan School of Computing and Mathematical Science Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerth6027Australia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Border Gateway Protocol(BGP)is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different *** BGP protocol exhibits security design defects,such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes,easily triggering prefix hijacking,path forgery,route leakage,and other BGP security ***,the traditional BGP security mechanism,relying on a public key infrastructure,faces issues like a single point of failure and a single point of *** decentralization,anti-tampering,and traceability advantages of blockchain offer new solution ideas for constructing secure and trusted inter-domain routing *** this paper,we summarize the characteristics of BGP protocol in detail,sort out the BGP security threats and their ***,we analyze the shortcomings of the traditional BGP security mechanism and comprehensively evaluate existing blockchain-based solutions to address the above problems and validate the reliability and effectiveness of blockchain-based BGP security methods in mitigating BGP security ***,we discuss the challenges posed by BGP security problems and outline prospects for future research.

关键词： BGP security blockchain prefix hijacking trust model secure routing

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Optimizing Speech Feature Extraction: A Comparative Study of MFCC, MFDWC, and MFWPC for Integration with Xception Block-Based CNN in Speech Emotion 7

Optimizing Speech Feature Extraction: A Comparative Study of...

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7th International Conference on Data science and Its Applications, ICoDSA 2024

作者： Ismail, Kevin Sihwi, Sari Widya Cahvono, Hasan Dwi Universitas Sebelas Maret Faculty of Information Technology and Science Data Department of Informatics Surakarta Indonesia

ISBN: (纸本)9798350365351

The experiments conducted in this research examines the performance of various mel-frequency based feature extraction modules such as Mel-Frequency Cepstral Coefficient (MFCC), Mel-Frequency Discrete Wavelet Coefficient (MFDWC), and Mel-Frequency Wavelet Packet Coefficient (MFWPC) on the Speech Emotion Recognition task. Empirical studies proved that MFDWC performs better than MFCC and MFWPC. Besides that, in the emotion classification module, this study tested the performance of Xception block as the convolutional building blocks that requires lower number of learnable parameters. The model with Xception Block has better efficiency against Residual Block as the compared building block. The final stage of the research resulted in the output of an ideal Convolutional Network architecture and settings that had been trained in a single-corpus Speech Emotion Recognition scenario on each of the Ravdess, EMOVO, and Emo-DB datasets. This study evaluates the performance of MFCC, MFDWC, and MFWPC as feature extraction modules for Speech Emotion Recognition, demonstrating that MFDWC outperforms the others © 2024 IEEE.

关键词： Emotion Recognition

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning(MCR)

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computer Systems science & Engineering 2023年第5期45卷 1167-1186页

作者： Kazim Ali Adnan N.Qureshi Muhammad Shahid Bhatti Abid Sohail Muhammad Hijji Atif Saeed Department of Computer Science Faculty of Information TechnologyUniversity of Central Punjab LahoreLahore54000Pakistan Department of Computer Science COMSAT University IslamabadLahore CampusLahore54000Pakistan Faculty of Computers and Information Technology Computer Science DepartmentUniversity of TabukTabuk47711Saudi Arabia

Brain magnetic resonance images(MRI)are used to diagnose the different diseases of the brain,such as swelling and tumor *** quality of the brain MR images is degraded by different noises,usually salt&pepper and Gaussian noises,which are added to the MR images during the acquisition *** the presence of these noises,medical experts are facing problems in diagnosing diseases from noisy brain MR ***,we have proposed a de-noising method by mixing concatenation,and residual deep learning techniques called the MCR de-noising *** proposed MCR method is to eliminate salt&pepper and gaussian noises as much as possible from the brain MRI *** MCR method has been trained and tested on the noise quantity levels 2%to 20%for both salt&pepper and gaussian *** experiments have been done on publically available brain MRI image datasets,which can easily be accessible in the experiments and result *** Structure Similarity Index Measure(SSIM)and Peak Signal-to-Noise Ratio(PSNR)calculate the similarity score between the denoised images by the proposed MCR method and the original clean ***,the Mean Squared Error(MSE)measures the error or difference between generated denoised and the original *** proposed MCR denoising method has a 0.9763 SSIM score,84.3182 PSNR,and 0.0004 MSE for salt&pepper noise;similarly,0.7402 SSIM score,72.7601 PSNR,and 0.0041 MSE for Gaussian noise at the highest level of 20%*** the end,we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.

关键词： MR brain images median filter wiener filter concatenation learning residual learning MCR de-noising method

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Examining the impact of link failures and network performance on a 6to4, 6rd, CHANC and D4across6 tunneling-based networks using various routing protocols

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International Journal of information technology (Singapore) 2024年 1-19页

作者： Hamarsheh, Ala Faculty of Information Technology/Department of Computer Science Arab American University Jenin 00970 Palestine

The article discusses the importance of failure recovery in communication networks to ensure smooth and dependable service. It states that the performance of real-time applications has been negatively affected for years due to failed links, and thus it is crucial to address the issue to prevent collapse and cascading failures for both users and service providers. The paper then goes on to analyze the performance of a network by simulating the failure and recovery of network links for different time periods using the 6rd, CHANC, D4across6 and 6to4 tunneling network. Different routing protocols and tunneling techniques are employed to evaluate the performance of the network for the specific type of applications (i.e. video streaming and real-time voice). It also looks at different metrics, including data transfer rate, dropped traffic, and network convergence. Furthermore, the study delves into the examination of the route table properties of various routers and the IPv4 backbone when links between them experience repeated failures and recoveries. The purpose of this analysis is to anticipate network performance and to ensure network availability and accessibility of traffic through forecasting the network's ability to survive and recover from link failures. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： IPv4 tunneling IPv6 IPV6 transition mechanisms IPv6 tunneling OPNET network performance

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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