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Developing Robust Cybersecurity Policies and Governance Frameworks in Response to Evolving Legal and Regulatory Landscapes

Developing Robust Cybersecurity Policies and Governance Fram...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Pandey, Radhika Anjimoon, Shaik Asha, V. Singla, Atul Khan, Irfan Abed, Zain Alabdeen Hasan GyanGanga Institute of Technology and Science DoCSE M.P Jabalpur India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Applications Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This research introduces a comprehensive cybersecurity framework consisting of five interlinked algorithms: Threat Intelligence Integration, Risk Assessment and Management, Compliance Mapping, Incident Response Planning, and Employee Training and Awareness. Through comparative performance evaluation and ablation study, the framework demonstrates consistent superiority across key parameters. Visual representations, including bar charts, line charts, and pie charts, provide intuitive insights into effectiveness, adaptability, compliance, and overall performance. The ablation study underscores the synergistic importance of each algorithm, emphasizing the framework's holistic nature. This research contributes to advancing cybersecurity methodologies, offering a dynamic and adaptive approach to safeguarding against evolving cyber threats. © 2024 IEEE.

关键词： Cybersecurity

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Provably Efficient Multi-Cancer Image Segmentation Based on Multi-Class Fuzzy Entropy

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第8期47卷 77-88页

作者： Jasim, Hend Muslim Ghrabat, Mudhafar Jalil Jassim Abdulrahman, Luqman Qader Nyangaresi, Vincent Omollo Ma, Junchao Abduljabbar, Zaid Ameen Abduljaleel, Iman Qays Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Iraqi Commission for Computers and Informatics The Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Science Department Al-Turath University College Baghdad10013 Iraq College of Health Science Hawler Medical University Erbil Iraq Jaramogi Oginga Odinga University of Science and Technology Bondo40601 Kenya College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Technical Computer Engineering Department AL-Kunooze University College Basrah6100 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518000 China Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah61004 Iraq

One of the segmentation techniques with the greatest degree of success used in numerous recent applications is multi-level thresholding. The selection of appropriate threshold values presents difficulties for traditional methods, however, and, as a result, techniques have been developed to address these difficulties multidimensionally. Such approaches have been shown to be an efficient way of identifying the areas affected in multi-cancer cases in order to define the treatment area. Multi-cancer methods that facilitate a certain degree of competence are thus required. This study tested storing MRI brain scans in a multidimensional image database, which is a significant departure from past studies, as a way to improve the efficacy, efficiency, and sensitivity of cancer detection. The evaluation findings offered success rates for cancer diagnoses of 99.08%, 99.87%, 94%;97.08%, 98.3%, and 93.38% sensitivity;the success rates of in particular were 99.99%;98.23%, 99.53%, and 99.98%. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Image segmentation

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Smart Diagnostic System for Early Detection and Prediction of Polycystic Ovary Syndrome

Smart Diagnostic System for Early Detection and Prediction o...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Harish, K.P. Dhivyanchali, M.N. Devi, K.Nirmala Krishnamoorthy, N. Dhana Sree, R. Dharanidharan, R. Kongu Engineering College Department of Computer Science & Engineering Erode India Swamy Vivekanandha Medical College Hospital and Research Institute Elayampalayam Tamilnadu Tiruchengode637 205 India Vellore Institute of Technology Vellore Campus School of Information Technology and Engineering Department of Software Systems and Engineering Tamilnadu 632014 India

ISBN: (纸本)9798350348217

PCOS are the serious condition which affects female ovaries during their reproductive age of 15 to 45. This disease affects 5 to 10% of reproductive-age females. Although it is difficult to fully resolve this issue,the PCOS affected women can be mitigated through proper exercise, by taking proper nutritious diet and maintaining the healthy BMI. Until they take a pregnancy test, the majority of women are unaware of the disease. The clinical dataset has the 541 instances and 45 attributes of unbalanced classes of 0 and 1 (no and yes) which has 364 instances of 0 (no) class and 177 instances of 1 (yes) class. Preprocessing is done for the unbalanced dataset by filling the null values and changing the datatype of all attributes to numeric datatypes. The unbalanced dataset is balanced by the balancing techniques of SMOTE and Random Over Sampling. Comparing the both balanced techniques through the accuracy the random oversampling gives the *** supervised learning algorithms are Decision tree, KNN,Random Forest,AdaBoost, Logistic regression,Gradient boosting, cat boosting, XGBoosting, Linear SVM, Radial SVM, Linear discriminant analysis and Quadratic discriminant analysis are *** supervised learning algorithms are trained and tested by splitting the dataset to 70% for training and 30% for testing. The ensemble stacking techniques are used by implementing the all models at the cross validation of *** xgboost gives the accuracy of 96% for the balanced dataset. © 2023 IEEE.

关键词： Adaptive boosting

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Coordinated Control of Three-Level T-Type Inverter for Renewable Energy Applications: Integrating Predictive Control with External RLC Estimation 7

Coordinated Control of Three-Level T-Type Inverter for Renew...

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7th International Conference on Green technology and Sustainable Development, GTSD 2024

作者： Trung, Tin Lu Nguyen Tran, Khoa Dang Ly, Trinh Thi Quang Pham, Tran Dang Le, NhuanAn Pham, Minh Duc Ho Chi Minh City University of Technology VNU-HCM Faculty of Electrical and Electronics Engineering Ho Chi Minh City Viet Nam Department of Physics and Computer Science Faculty of Physics and Engineering Physics University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Ho Chi Minh City University of Technology VNU-HCM Faculty of Transportation Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350386073

Three-phase inverters, particularly three-level converters like the three-level T-type inverter (3LT2I), play a pivotal role in renewable energy applications, offering advantages such as improved efficiency and reduced costs due to their unique design. However, the operation of the 3LT2I may lead to a capacitor voltage imbalance, resulting in increased total harmonic distortion (THD) of output currents and overvoltage across switches. Various control methods, including proportional-integral (PI) controllers and predictive control schemes, have been proposed to address this issue. Despite the fast dynamic response of predictive control schemes, their operational dependency on prior knowledge of system load parameters poses a significant challenge. This paper proposes a coordinated control approach that integrates a model predictive control (MPC) scheme with an external RLC online estimator to address the challenges associated with the 3LT2I. The proposed strategy aims to achieve precise current tracking, balanced capacitor voltage, and continuous updating of load impedance parameters for optimal current control performance. Simulations are conducted under various load scenarios to demonstrate the feasibility and effectiveness of the proposed control method. © 2024 IEEE.

关键词： Predictive control systems

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Study on Host-based Security System Advancement in ICT-Enabled Smart Cities 1

Study on Host-based Security System Advancement in ICT-Enabl...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Fouad, Laith Hussien, Naseer Ali Hussian, Ahmed Alwan, Adil Abbas Qasim Abbas, Saad Abdulaziz, Ismail Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Scientific Research Center Al-Ayen University Information and Communication Technology Research Group Thi-Qar Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

Modern ICTs (Information and Communication technology) are used in Smart Cities (SC) to boost productivity, share data with other users, deliver better federal programs, and improve customer satisfaction. Most technological schemas and operational information are readily available and can be used to expand the reach of civic intelligence. Security and privacy concerns, however, remain a major obstacle when faced with the difficulties of meeting security standards. 5G-enabled technologies, self-driving cars, and the proliferation of communication equipment increase the opportunities for malicious actors to launch attacks. Host-level security tools are known as Host Security (HS). Modern technology used in smart cities meets residents' requirements and solves urban problems. An intelligent city achieves many things and gives its residents a voice. With host-based protection, it is feasible to escape the battle of security upgrades, allowing IT executives to focus on the most important changes. For the smart city against cyber-attacks protection, this article employs ICT enabled Smart City based Host security (ICT-SC-HS). A single infected machine could destroy the enterprise's systems without host-based protection. A network security application installed on a computer or networked device is a host-based barrier. Using these firewalls can control the transmission of dangerous infestations across the network. Individual hosts may be safeguarded from viruses and malware. A host-based firewall, which is installed on each server, decides its accessibility to a certain device and also monitors and regulates inbound and outbound traffic. Hence, compared with existing research, the proposed article presents malware detection in different attacks as 72.1%, evaluation of host networks as 69.7%, absolute errors of security analysis as 82.8%, probability of network as 75.4%, and accuracy as 67.2%. © 2023 IEEE.

关键词： Smart city

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Fostering Air Pollution using Cloud Computing and IoT-based Methods 1

Fostering Air Pollution using Cloud Computing and IoT-based ...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Majeed, Maryam Ghassan Nazar Dawood, Mustafa Taha, Ahmed Hussien, Naseer Ali Lafta, Alaa M. Noori, Mohammed Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Ayen University Information and Communication Technology Research Group Scientific Research Center Thi-Qar Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

Each year, the number of people dying from pollution rises, and air pollution is the main factor in these deaths. Car pollution is a key contributor to air pollution (AP) caused by various sources. Most cities have poor air quality that does not meet health standards. The major issue in the contemporary civilised world is AP, which hurts human health and the environment. With numerous emission sources, motor vehicles and industrial processes are the principal causes of air pollution. Both short-term and long-term exposure to airborne toxicants can have various toxicological consequences on people, including long-term chronic disorders including cancer, neuropsychiatric issues, skin diseases, eye irritation, and respiratory and cardiovascular illnesses. Using the Internet of Things (IoT), track air pollutants in a given area and analyse and forecast air quality. This article will combine cloud computing with the AP-IoT strategy to deliver these services more swiftly and in real time. The area of town where the intended system will be installed is very polluted. The concentrations of each hazardous contaminant are monitored regularly. A mechanism is in place for monitoring and reducing traffic in areas with high pollution levels. The answer in this situation is a sensor-based hardware module that can be positioned beside roads. These gadgets mounted on lampposts may wirelessly send information about air quality to a remote server. For traffic control, this information may be used. This technology will be developed further so consumers can get weekly or monthly air quality reports via an easier-to-use mobile app. Commuters can choose routes where the air quality is good thanks to a smartphone application that provides information about it. The experimental results show that, in comparison to other approaches already in use, the suggested AP-IoT model improves prediction ratios of 98.9%, accuracy ratios of 95.6%, stability ratios of 97.85, efficiency ratios of 96.7%, an

关键词： Diseases

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Anomaly Detection in Univariate Time Series: HOT SAX vesus LSTM-Based Method 12th

Anomaly Detection in Univariate Time Series: HOT SAX vesus L...

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12th EAI International Conference on Context-Aware Systems and Applications, ICCASA 2023

作者： Anh, Duong Tuan Hoai, Tran Long Department of Information Technology HCMC University of Foreign Languages and Information Technology Ho Chi Minh City Viet Nam Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031588778

Anomaly detection in time series has been an important and challenging research topic. There have been several methods proposed for detecting anomaly subsequences in a time series. The majority of these methods is classified into the window-based category, which applies a sliding window with a fixed length to extract subsequences before finding out anomaly subsequences. A well-known algorithm in this category is HOT SAX algorithm. Recently, deep neural network models, especially Long Short Term Memory (LSTM) network, are also applied for time series anomaly discovery. LSTM-based methods for time series anomaly detection belong to prediction-based category. So far, there has been no research work to compare the performance of LSTM-based method to that of any traditional window-based method in time series anomaly detection. The research question investigated in this paper is that whether the newly developed LSTM-based method for time series anomaly detection is superior to the traditional algorithms, such as HOT SAX or not. In this study, we give an empirical comparison between LSTM-based method and HOT SAX in time series anomaly detection. Extensive experiments on seven benchmark time series indicate that LSTM-based method is not superior to HOT SAX since it brings out the same detection accuracy as HOT SAX while it incurs much higher computational overhead. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Deep neural networks

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Cardiovascular Health Risk Prediction Using Bayesian Predictive Analytics Approach 5th

Cardiovascular Health Risk Prediction Using Bayesian Predict...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Shantanu, Kumar Singhal, Priya Mishra, Sushruta Sharma, Ruchi Obaid, Ahmed J. Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Department of ECE Bharati Vidyapeeth’s College of Engineering Delhi India Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq

ISBN: (纸本)9789819763177

A correct CVD diagnosis and outcome, on the other hand, result in expedited patient care and highly accurate treatment, as well as good results. Medicine has turned to machine learning since it is capable of discerning intricate patterns from vast data set insights. This study seeks to reduce the risk of miscategorizing CVD events and subsequently decrease CVD death rates by using machine learning methods. As a result, a more sophisticated approach outlined: RF, Decision Tree, MP, and XGB as the best current computer program in the proposed system. To refine the sorting capability of the model, a revolutionary method produce, including a K-modes grouping and a Huang starting point technique to increase sorting the stuff more effectively. Using an actual data from Kaggle dataset which had 4240 examples, our models were trained and tested. After rigorous evaluation, the models demonstrated noteworthy accuracy rates: Decision Tree achieved 72.81%, XGBoost exhibited 83.33%, Random Forest attained 83.61%, Naive Bayes achieved 81.01%, and Multilayer Perceptron outperformed others with an accuracy of 77.32%. Moreover, effectiveness of the model was calculated by using the Area Under the Curve (AUC), which resulted in outstanding predictive capabilities. Specifically, XGBoost and Random Forest both achieved an AUC of 0.58, with support vector machine (SVM) model outperforming all, boasting an accuracy level of 83.33%, an AUC score of 0.58, highlighting their robustness in predicting CVD occurrences. An observation from this study tells the superiority of the brain-like network model (MPM), particularly at the time matched with cross-checking. It resulted in achieving an exceptional success rate of 87.28%. The research developed not only focuses on the area of predicting heart diseases but also lays emphasis on the potential of machine learning methods to transform medical diagnostics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Patient treatment

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Digital-Bracers: A Design Process for an Interactive Monitoring Generative System That Helps People with Hand Movements

Digital-Bracers: A Design Process for an Interactive Monitor...

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2023 International Symposium on World Ecological Design, ISWED 2023

作者： Yao, Yousheng Xie, Yuan Ying, Fangtian Wang, Haoran Huang, Zhihao Zheng, Junpeng Faculty of Humanities and Arts Macau University of Science and Technology China Hexiangning College of Art and Design Zhongkai University of Agriculture and Engineering China College of Computer and Cyber Security Fujian Normal University China

ISBN: (纸本)9781643685069

In the post-New Crown epidemic era, people need to face a period of physical recovery after being infected with New Crown, especially when there are frequent cases of deaths due to over-aggressive or excessive physical exercise after healing, or due to people's concerns about safety hazards, or loss of interest and adherence to fitness due to exercising alone, among other problems. In this paper, we design a complete interactive monitoring generation system for wrist brace components, Digital-Bracers, which consists of both hardware and software. The hardware has integrated textile heart rate monitoring and integrated sensors in a three-lead configuration for a textile-based digital wrist brace that conforms to human comfort. The software is a visualized app with pulse rate, speed, and frequency, oscillation rhythm, content generation social connectivity, etc. The software and hardware are connected via WI-FI or Bluetooth. Through exploration of materials, ergonomics, and textile wrist guard construction, we designed an experimental process and tested Digital-Bracers to determine the system's performance through peak heart rate detection and noise interference. The contribution of the Digital-Bracers Interactive Monitoring and Generation System is the ability to monitor one's own health in real-time during hand exercise to avoid potential risks, while at the same time, The Digital-Bracers Interactive Monitoring System contributes to the real-time monitoring of one's health during hand movements to avoid potential risks, and to the generation of a variety of different sounds to bring a sense of fulfillment and enjoyment to the hand movements. © 2024 The Authors.

关键词： Textiles

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Heart Disease Prediction Using Machine Learning 2

Heart Disease Prediction Using Machine Learning

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Kamal, Haram Bakhtawar Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Mustafa, Muzzamil Yaqub, Muhammad Atif Umar, Hooria Fatima, Hoor Nasir, Usama Information Technology University Computer Engineering Department Lahore Pakistan Bahria University Department of Computer Science Lahore Campus Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan University of Management & Technology Department of Artificial Intelligence Lahore Pakistan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Pakistan Bahria University Pakistan

ISBN: (纸本)9798331528713

The heart plays a pivotal role in the functioning of living organisms, making its diagnosis and prediction of related diseases a matter of utmost importance. Approximately 17.9 million individuals succumb to cardiovascular disease, accounting for 32% of worldwide fatalities. This global concern highlights the importance of early detection, as timely treatment can significantly reduce heart disease-related mortality. Errors in diagnosis can lead to severe consequences, including fatigue or even death. The increasing prevalence of heart-related diseases necessitates the development of precise prediction systems to enhance awareness. Machine learning, a subset of Artificial Intelligence (AI), offers robust tools for predicting various events based on patterns learned from natural occurrences. This research paper evaluates the accuracy of machine learning algorithms, specifically k- nearest neighbor, decision tree, linear regression, and support vector machine (SVM), in predicting heart disease. We utilize the UCI repository dataset for training and testing and employ the Python programming language through the Anaconda Jupyter notebook for implementation and the experimental findings indicate an accuracy rate of 95.7% using the heart disease prediction model, leveraging its extensive libraries and header files to ensure accuracy and precision in our analysis. © 2024 IEEE.

关键词： Random forests

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