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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Bhushan Pawar Lalit Garg Vijay Prakash Ryan Stevenson Matthew Amaira Daria Soloducha Adam Kraus Computer Information Systems Faculty of Information & Communication Technology University of Malta Msida Malta Communications & Computer Engineering Faculty of Information & Communication Technology University of Malta Msida Malta

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

Rising Levels of air pollution and changing environmental conditions are the global concern. There is substantial evidence linking short-term and long-term exposure to NO 2 (nitrogen dioxide) with multiple disorders such as asthma, Bronchitis, Chronic Obstructive Pulmonary Disease (COPD), and Respiratory Infections in humans. However, multiple researchers have mainly depended on time-based exposure data, possibly overlooking fluctuations in NO 2 levels across different locations. We aim to analyze the relationship between NO 2 fluctuations and emergency department hospital admissions. We adopted machine learning (ML) based regression approaches to predict hospital admissions, we obtained emergency department hospital admissions from 2018–2019, while NO 2 tropospheric concentration was collected from the Sentinel 5P satellite. The results are evaluated using various evaluation matrices such as MSE (Mean Square Error), RMSE (Root Mean Square Error), R2 score, etc. Our study demonstrated that Random Forest and ExtraTreesRegressor models gained substantial predictive accuracy compared to other applied regression models. ExtraTreesRegressor proves its significance with R2 score of 67.11. These results deliver significant insights for hospitals and healthcare authorities by offering resource management through anticipating fluctuating NO 2 concentrations. Moreover, the findings of our study highlight the potential of extending this research to include several air pollutants and meteorological factors.

关键词： Technological innovation Hospitals Atmospheric modeling Predictive models Air pollution Resource management Nitrogen Forecasting Regression tree analysis Random forests

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Proposed Privacy Preservation Technique for Color Medical Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 719-732页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Noha A.El-Hag Ashraf A.M.Khalaf Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Nowadays,the security of images or information is very *** paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image ***,the secret medical image is encrypted using Advanced Encryption Standard(AES)***,the secret report of the patient is embedded into the encrypted secret medical image with the Least Signiﬁcant Bit(LSB)watermarking *** that,the encrypted secret medical image with the secret report is concealed in a cover medical image,using Kekre’s Median Codebook Generation(KMCG)***,the stego-image obtained is split into 16 ***,it is sent to the *** adopt this strategy to send the secret medical image and report over a network *** proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio(PSNR),Correlation Coefﬁcient(Cr),Fea-ture Similarity Index Metric(FSIM),and Structural Similarity Index Metric(SSIM).Histogram estimation is used to conﬁrm the matching between the secret medical image before and after *** results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

关键词： LSB steganography AES algorithm KMCG algorithm

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Image Captioning with Reinforcement Learning 2

Image Captioning with Reinforcement Learning

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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Verma, Anand Agarwal, Saurabh Arya, K.V. Petrlik, Ivan Esparza, Roberto Rodriguez, Ciro ABV-IIITM Gwalior Department of Information Technology Madhya Pradesh 474015 India ABV-Indian Institute of Information Technology and Management Multimedia and Information Security Research Group Department of Computer Science and Engineering Gwalior474015 India National University Federico Villarreal Faculty of Industrial and Systems Engineering Lima Peru National University Mayor de San Marcos Faculty of Systems Engineering and Informatics Peru

ISBN: (纸本)9798350305142

Image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO. © 2023 IEEE.

关键词： Reinforcement learning

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A Trend Analysis of Test Smells in Python Test Code Over Commit History 49

A Trend Analysis of Test Smells in Python Test Code Over Com...

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49th Euromicro Conference on Software engineering and Advanced Applications, SEAA 2023

作者： Fushihara, Yuki Aman, Hirohisa Amasaki, Sousuke Yokogawa, Tomoyuki Kawahara, Minoru Ehime University Faculty of Engineering Department of Computer Science Ehime790-8577 Japan Ehime University Center for Information Technology Ehime790-8577 Japan Okayama Prefectural University Faculty of Computer Science and Systems Engineering Okayama719-1197 Japan

ISBN: (纸本)9798350342352

Software testing is an essential activity for developing and maintaining high-quality software. Unit testing with test code (test cases) is a fundamental testing activity, and developers can test their production code whenever they create or modify the code. However, such quality assurance relies on the correctness of the test code. If a test code had a flaw, it would mislead the developers about the hidden faults and prevent early detection of the faults. This paper focuses on "test smells,"which may cause test code flaws in Python programs, and analyzes their changing trends over commit history (code changes) toward better Python test code management. Through an empirical data analysis of 100 open-source projects, the paper reports the following findings: (1) a few kinds of test smells constitute the majority of smells detected in the studied projects, and (2) most kinds of smells tend to increase over commits, i.e., many test smells are likely to have remained in test code as technical debt. © 2023 IEEE.

关键词： Python

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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security information and Event Management systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

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computer systems Science & engineering 2022年第10期43卷 397-412页

作者： Sunil Dhankhar Mukesh Kumar Gupta Fida Hussain Memon Surbhi Bhatia Pankaj Dadheech Arwa Mashat Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipurRajasthan302017India Department of Electrical Engineering Sukkur IBA UniversityPakistan Department of Mechatronics Engineering Jeju National UniversityJeju63243Korea Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversitySaudi Arabia Faculty of Computing&Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia

In today’s digital era,the text may be in form of *** research aims to deal with the problem by recognizing such text and utilizing the support vector machine(SVM).A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language.A method is developed for identifying Hindi language characters that use morphology,edge detection,histograms of oriented gradients(HOG),and SVM classes for summary *** rank employs the summary to extract essential phrases based on paragraph position,phrase position,numerical data,inverted comma,sentence length,and keywords *** primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant *** second goal is to maintain or improve the classification system’s *** experiment included news articles from various genres,such as Bollywood,politics,and *** proposed method’s accuracy for Hindi character recognition is 96.97%,which is good compared with baseline approaches,and system-generated summaries are compared to human *** evaluated results show a precision of 72%at a compression ratio of 50%and a precision of 60%at a compression ratio of 25%,in comparison to state-of-the-art methods,this is a decent result.

关键词： Support vector machine(SVM) optimization precision Hindi character recognition optical character recognition(OCR) automatic summarization and compression ratio

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Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

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computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human *** the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital *** current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 *** suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image ***,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based *** proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover *** secret images,encrypted by signcryption technique,are embedded into cover ***,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax *** integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV *** proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct *** simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

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Enhancing Data Aggregation in Cluster-Based Wireless Sensor Networks with LCSS: Longest Common Subsequence Empowerment 8th

Enhancing Data Aggregation in Cluster-Based Wireless Sensor ...

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8th World Conference on Smart Trends in systems Security and Sustainability, WorldS4 2024

作者： Jamil, Muhammad Farooq, Umer Tariq, Noshina Alwakid, Ghadah Humayun, Mamoona Khan, Nadia Department of Computer Science Abdul Wali Khan University Mardan Pakistan Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Department of Avionics Engineering Air University Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Institute of Computer Sciences and Information Technology Faculty of Management and Computer Sciences The University of Agriculture Peshawar Pakistan

ISBN: (纸本)9789819793266

A wireless sensor network (WSN) is a system of interconnected sensors that can gather environmental information. On the other hand, data redundancy is a common source of problems with WSNs. The literature presents a plethora of approaches. Despite these mechanisms’ best efforts, most current systems still need help with difficulties like noise, redundancy, or application specificity. Thus, we proposed an algorithm to overcome all the problems with redundant and highly correlated data. The longest common subsequence (LCSS)-enabled sliding window algorithm can resolve issues in constrained networks related to energy conservation and congestion control. The proposed method exhibits remarkable performance on multiple benchmark datasets. According to the result analysis, the suggested technique can outperform its equivalents for a range of data sets by about 32%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Congestion control (communication)

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Experimental evaluation of MU-MIMO in TDD environment for 5G NR using exploiting channel reciprocity

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International Journal of information technology (Singapore) 2024年第7期16卷 4651-4666页

作者： Kumar, Rajesh Sinwar, Deepak Singh, Vijander Department of Computer and Communication Engineering Manipal University Jaipur Rajasthan Jaipur 303007 India Department of IoT and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur 303007 India Department of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering NTNU - Norwegian University of Science and Technology Ålesund Norway

5G New Radio (NR) operates in two frequency ranges viz. Frequency Range 1 (sub-6 GHz band) and Frequency Range 2 (millimeter wave communication). It utilizes Time Division Duplex (TDD) communication, allowing the same frequency band for uplink and downlink transmission using sounding reference signals. These signals help estimate channel conditions for scheduling downlink traffic. Multi-User Multiple Input Multiple Output (MU-MIMO) technology enables simultaneous data transmission to multiple users using multiple antennas. This study examines MU-MIMO performance in TDD by analyzing variables like radio conditions, modulation schemes, code rates, precoding algorithms, and sub-carrier spacing. Simulation results show that Zero Forcing (ZF) precoding outperforms Block Diagonalization (BD) and Regularized Zero Forcing (RZF) in good radio conditions. However, in bad radio conditions, RZF performs better than ZF. Choosing higher code rates in 16 and 64-QAM and lower code rates in 256-QAM from the MCS table improves throughput. This indicates that MCS and user selection are crucial for higher throughput in TDD-based MU-MIMO for 5G NR. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： 5G new radio Beamforming MU-MIMO Precoding Sounding reference signal Time division duplex

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Foliar Chlorophyll Monitoring to Enhance Palm Oil Plant Productivity 9th

Foliar Chlorophyll Monitoring to Enhance Palm Oil Plant Prod...

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9th Future Technologies Conference, FTC 2024

作者： Adnan, Nur Damia Kassim, Mohd Sallehin Mohd Dao, Hassan Mohamed, Maizatul Suriza Sajak, Aznida Abu Bakar Computer Engineering Section Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur50250 Malaysia Faculty of Engineering Princess of Naradhiwas University Narathiwat96000 Thailand Malaysian Palm Oil Board Selangor Bandar Baru Bangi43650 Malaysia

ISBN: (纸本)9783031731211

In the modern agricultural landscape, enhancing productivity stands as a central objective. The monitoring system of palm oil plant health development based on Foliar Chlorophyll Leaves with IoT, presents an innovative approach to improve palm oil plantation management. Utilizing the ESP32 microcontroller, data from the color sensor is processed, with integrated Wi-Fi capabilities enabling seamless connectivity to the platform. These readings are subject to storage, analysis, and comparison against predefined ranges to ascertain the distinct health status of the plants. Collaboration with the Malaysian Palm Oil Board ensures alignment with user requirements and demands, strengthening the system’s efficacy and reliability. The paper provides a detailed overview of the plant health monitoring process, outlining various treatments and their corresponding RGB color sensor readings. Each treatment, representing a specific plant type, is accompanied by the date of leaf testing, offering a comprehensive insight into the monitoring procedure. Results indicate the system’s effectiveness in detecting stress-induced physiological changes and early signs of diseases, equipping farmers and plantation managers with actionable data for informed decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision making

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