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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Nasir, Imtisall Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Marjan, Shah Balochistan University of Information Technology Engineering and Management Science Department of Computer Science Quetta Pakistan Balochistan University of Information Technology Engineering and Management Science Department of Computer Engineering Quetta Pakistan Pakistan Television Corporation Department of Engineering Lahore Pakistan Balochistan University of Information Technology Engineering and Management Science Department of Software Engineering Quetta Pakistan

ISBN: (纸本)9798331508692

Sentiment Analysis is a deep text analysis to determine the expressions from the written text, The Urdu language holds significant importance due to the usage of these social media platforms and content generated at these platform in Urdu. Effective sentiment analysis in Urdu can provide valuable information about social trends, customer feedback, and public opinion on different sets of topics in the Urdu context. This study uses an deep learning (ANN) Artificial Neural Network to predict and classify the sentiments in the Urdu language, labeling them into positive, negative, and neutral sentiments. To achieve the research goals, advanced preprocessing techniques were utilized such as removing stop words, emojis, emails, and characters, removing symbols, performing lemmatization to expand the meaning of text, tokenized each word by using Lexicon-based approach and ngram. The dataset are tweets dependent and have 1,140,021 tweets. These steps ensure that data is standardized and cleaned to be split into training and testing. The datasets were converted into sequence and categorized to be input into the ANN model. The ANN model was trained and achieved an accuracy of 90%, precision of 98%, and f1-score of 97%. These high values indicate that the model accurately predicted the sentiments in the Urdu text. However, during experimentation limitations were discovered due to the complexities of Urdu text, difficult script patterns, and linguistic problems that were not solved by the preprocessing technique. During analysis, the limitation was discovered due to the limited number of preprocessing techniques and tools for Urdu text. However, the study highlights the potential of neural networks, especially ANN in Urdu sentiment analysis. This study contributed significantly to Urdu natural language processing by providing robust techniques and identifying limitations to work on it as a research problem. © 2024 IEEE.

关键词： Natural language processing systems

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Landslide Monitoring Using IoT System with Cloud Platform 10

Landslide Monitoring Using IoT System with Cloud Platform

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Rawat, Pradeep Singh Soni, Prateek Kumar Goel, Aryan Kaur, Gurmeet Dit University Computer Science & Engg. Department Dehradun India Gla University Computer Engineering & Application Mathura India

ISBN: (纸本)9798350382471

The natural disasters poses the losses in the disaster prone area. Landslides pose great socio-economic threats to regions which geography of the location favors it. The hill states of the India are more prone to the landslides due to the heavy rain fall other environmental challenges. The researchers found that ninety-nine percent of landslides induced due to heavy rain falls. The severity of disasters especially landslides is gradually increasing day by day in scale and frequency, resulting in widespread loss of human lives and infrastructure of the nation. The detection, monitoring and mitigation of the landslides in hill states is the major concern. The mitigation and monitoring of the effects of landslides can be performed using technology integration. This article presented an innovative landslide monitoring system. It is based on the IoT protocols based monitoring system consisting of cloud platform which provides instant alerts of upcoming landslides on the basis of real time data set collected inside the IoT cloud platform. As part of this monitoring system, landslide data is sent directly to the civil protection department and notified to the local population for the awareness and future precautionary safety measures. The results and discussions section shows the real time data visualization of the landslide inducting parameters, including metrological, geophysical and hydrological parameters. © 2023 IEEE.

关键词： Accelerometer sensor Arduino IDE Arduino uno Cloud Internet of Things Soil moisture Sensor

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Review on Enhancing Predictive Accuracy in Alzheimer Detection: A Hybrid Approach 3

Review on Enhancing Predictive Accuracy in Alzheimer Detecti...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Ambooj Khan, Ihtiram Raza Agarwal, Parul Kaur, Harleen School of Engineering Sciences & Technology Jamia Hamdard New Delhi India Bvicam Department of Computer Science and Application New Delhi India

ISBN: (纸本)9798331518592

Effective early detection techniques are essential because to the tripled global prevalence of dementia, which is mostly caused by Alzheimer's disease (AD) and is predicted to reach 152 million by 2050. AD pathology, which has a preclinical phase lasting 10-20 years before severe cognitive impairment, is associated to about 70% of dementia cases. Early detection of AD is essential since medication trial success rates may be increased and risk factor management may avoid 40% of cases. Currently, lengthy cognitive tests that are insensitive to the pre-clinical stage are used to detect dementia. Initiatives to make open data available have gathered so much data on AD that it is now too much for humans to analyze. In order to diagnose AD accurately, machine learning (ML) has become essential, especially when it comes to early-stage AD prediction. AI and ML-driven healthcare research places a strong emphasis on automated disease prediction systems, including dementia detection. Finding those who is susceptible of 'Alzheimer's disease' (AD) progression is still difficult, especially in light of the disease's severity and fast advancement. Preventing the progression of the disease and planning the course of treatment depend on early detection. Reviewing AD detection research conducted since 2016 has highlighted the contribution of machine learning (ML) to improving prediction accuracy. The reviews provide information on participant demographics, data modalities, feature extraction strategies, and ML models used. By combining earlier studies, promoting critical analysis, and directing future investigations for early AD diagnosis, this thorough review helps researchers. © 2024 IEEE.

关键词： Adversarial machine learning

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Streamlining Classroom and Laboratory Management in University: Enhancing Problem Reporting Processes 4

Streamlining Classroom and Laboratory Management in Universi...

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4th International Conference on Computing and Information Technology, ICCIT 2025

作者： Naith, Qamar H. College of Computer Science and Engineering Department of Software Engineering University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9798350353839

Managing the classroom and lab activities in the university and reporting process for the problems that occur during lectures is a hard process that needs more consideration. This study addresses challenges faced by technicians and academic staff in university classrooms, emphasizing the need for a streamlined automated issue reporting system. This paper proposes a comprehensive system aimed at improving communication, efficiency, and effectiveness in addressing classroom issues, benefiting technicians, students, and lecturers. Quantitative Analysis and evaluation of the system's user experience from instructors and technicians showed highly positive outcomes, indicating its high effectiveness in managing classroom issues and improving overall classroom management. Moreover, the statistical test revealed no statistically significant differences (α= 0.05) in the willingness to use the classroom issue management system between technicians and academic staff, indicating similar user perceptions and readiness for both groups to utilize the system. © 2025 IEEE.

关键词： Academic staff Classroom issue management system Issue reporting system Quantitative analysis reporting process Technicians User experience

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Structure learning of Bayesian networks using sparrow optimization algorithm

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2025年第2期9卷 14-25页

作者： Kareem, Shahab Wahhab Awla, Hoshang Qasim Mohammed, Amin Salih Department of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Computer Technical Engineering Al-Qalam University College Kirkuk Iraq Department of Computer Science Faculty of Science Soran University Soran Iraq Department of Software and Informatics Salahaddin University Erbil Iraq

Bayesian networks are powerful analytical models in machine learning, used to represent probabilistic relationships among variables and create learning structures. These networks are made up of parameters that show conditional probabilities and a structure that shows how random variables interact with each other. The structure is shown by a directed acyclic graph. Despite the NP-hard nature of learning Bayesian network structures, there has been significant progress in improving the accuracy of approximation solutions. The main focus is on score-based search strategies, which make use of functions to evaluate network models and identify structures with high scores. This study is significantly focused on structure learning Bayesian networks using the Bayesian Dirichlet equivalent uniform scoring function and metaheuristic search strategies. To this end, this paper presents the sparrow optimization algorithm (SOA), a new metaheuristic algorithm derived from the foraging behavior of sparrows. SOA performs a concurrent optimization in the solution space by simultaneously performing a local and global search that leads to the discovery of near-optimal structures. The results from our experiments on several benchmark datasets show that SOA yields overall better performance than SA and greedy search algorithms. In particular, it is claimed that by applying the proposed approach of SOA, the convergence speed is significantly higher compared with the existing ones;F1 score is 0.35 and 0.05 for the Hamming distance with better results. Given these results, signed operators prove to be very efficient in SOA’s Bayesian network structure learning as a concept, especially for real-world use. © (2024), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Bayesian Network Global and Local Search Search and Score Sparrow Search Optimization Algorithm Structure Learning

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A High-capacity Information Hiding Scheme Based on Blakley Secret Sharing 4

A High-capacity Information Hiding Scheme Based on Blakley S...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Zhou, Jincheng Li, Yue Shen, Yujie Tang, Dan Cai, Hongliang Gong, Jing Bai, Ningchao School of Software Engineering Chengdu University of Information Technology Chengdu China Sichuan Province Engineering Technology Research Center of Support Software of lnformatization Application Chengdu China School of Computer Science and Engineering Sichuan University of Science & Engineering Yibin China Sichuan Provincial Big Data Technical Service Center Chengdu China

ISBN: (纸本)9798331541729

A high-capacity reversible information hiding scheme based on Blakley secret sharing is proposed to address the issues of low embedding rate and weak disaster recovery performance of images when using reversible information hiding to protect data in scenarios with large data transmission. The scheme employs Euclidean division and a two-dimensional modulus function to establish a robust mapping relationship between the secret information and the pixels of the cover image. Subsequently, the values derived from the preprocessing of the secret information are incorporated into the secret sharing method proposed by Blakley as the elements of the secret vector. The proposed scheme exploits the disaster-tolerance property of the secret sharing method and enhances the redundancy of the secret-carrying encrypted image, thereby improving the embedding rate and security of the algorithm while maintaining the security of the secret sharing. © 2024 IEEE.

关键词： Euclidean division Information hiding Modulus function Secret sharing

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Optimizing Cloud Security with CNN and XGBoost Models for Intrusion Detection Systems 3

Optimizing Cloud Security with CNN and XGBoost Models for In...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Mogal, Amit Karbhari Anitha, V. Bhatt, Preeti Nitin Srivasatava, Kriti Devi, S. Rukmani Perada, Anvesh Mvp Samaj's Cmcs College Department of Computer Science and Application Nashik India School of Engineering and Technology Dhanalakshmi Srinivasan University Department of Computer Science and Engineering Trichy India Iimt College of Engineering Department of Electronics & Communication Engineering Greater Noida India Vile Parle India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Chennai India Drexel University Department of Electrical and Computer Engineering Philadelphia United States

ISBN: (纸本)9798331508456

Cloud computing and Internet growth have simplified certain formerly difficult activities. This advancement has also revealed many security weaknesses. Due to cyberattacks, organizations need Intrusion Detection Systems (IDS) to protect their data and networks. Preprocessing, feature selection, and model training comprise the suggested strategy. Preprocessing includes data cleansing, standardization, and labeling. Information gain, chi-square, and PSO are used to choose features. CNN-XGBoost trains the model. The CNN-XGBoost model outperforms solo CNN and XGBoost with an average accuracy of 92.08%. This accuracy proves the hybrid method's cloud computing breach detection usefulness. The study underlines the importance of enhanced IDS models in cyber risk reduction. CNN's feature extraction and XGBoost's classification power provide a powerful, efficient, and reliable cloud intrusion detection model. Modern organizations benefit from this network security method. © 2025 IEEE.

关键词： Network intrusion

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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Comparative Evaluation of Machine Learning Algorithms for Detecting Credit Card Fraud 8

Comparative Evaluation of Machine Learning Algorithms for De...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Prashanth, Banagani Gangadhar, Suddapalli Bhardwaj, Vivek Reddy, Kovvuru Diwakar Reddy, Puli Shiva Mehra, Manik Lovely Professional University Department of Computer Science and Engineering Phagwara India Manipal University Jaipur Department of Computer Science and Engineering Jaipur India Lovely Professional University School of Computer Application Phagwara India

ISBN: (纸本)9798350396638

Detecting credit card fraud is an extremely important issue in the financial sector, as it results in significant financial losses and negatively impacts customer trust. The main motive of CCFD is to develop methods to identify fraudulent transactions accurately and efficiently. This research typically involves the use of statistical and machine-learning algorithms to analyze large amounts of transaction data. Utilizing machine learning is a potential solution for addressing credit card fraud. Studies suggest that machine learning techniques can help overcome the challenges of identifying fraudulent transactions with high detection rates, both directly and indirectly. While supervised ML algorithm and unsupervised ML algorithms have been proposed, the limitations of each approach underscore the need for hybrid methods. From the results, it is evident that Logistic Regression has the highest accuracy of 94.86%, highest recall value of 98.97% and highest F1-score of 95.09%. © 2023 IEEE.

关键词： Feature extraction

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Performance Evaluation of Blockchain Platforms

Performance Evaluation of Blockchain Platforms

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2023 International Conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Gupta, Manish Kumar Dwivedi, Rajendra Kumar Sharma, Annu Farooq, Mansoor Britto Raj, S. Madan Mohan Malaviya University of Technology Department of Information Technology & Computer Application Gida Gorakhpur India RajaRajeshwari College of Engineering Department of Computer Science & Application Bengaluru India University of Kashmir Department of Management Studies Jammu & Kashmir India Rrase College of Engineering Department of Computer Science & Engineering Chennai601301 India

ISBN: (纸本)9798350302820

Blockchain (BC) is a cryptographic technology that enables the decentralized storage of an immutable record of transactions. As a result, many industries are interested in integrating BC into their IT systems. Although BC-based platforms are used in enterprise solutions, concerns regarding accessibility, privacy, performance, and scalability are still legitimate. Permissioned Blockchain (PBC) frameworks provide a solution to securely store private records. The main objective of this paper is to focuses on the performance and scalability of popular private BC platforms. To evaluate each platform, we varied the workloads and assessed performance metrics. Our comparative analysis provides valuable insights into the strengths and weaknesses of each platform, enabling enterprises to make informed decisions when choosing a private BC solution. © 2023 IEEE.

关键词： Blockchain

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