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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Kumar, Rahul Gupta, Aniket Rajeswari, D. School of Computing College of Engineering and Technology Department of Data Science and Business Systems India

ISBN: (纸本)9798350327533

Traffic signals and other signs like parking., stop signs., etc. have become very crucial in autonomous and s elf-driving cars as it helps the smart system to comply with the basic traffic rules along with that it helps navigate routes based on the signs thus enabling a more secure driving experience for the drivers. There have been a lot of new algorithms that have emerged in the past recent years regarding this. In this paper., this research has used the new YOLOv8 object detection system to help us detect traffic signs as it is much fas ter and more precis e than its previous iterations. To improve the algorithm., this paper has used a dataset comprising photos of traffic signs taken at different angles and different light intensities. This system can predict the traffic signs with 93% accuracy. © 2024 IEEE.

关键词： Autonomous vehicle Deep Learning Traffic sign detection yolov8

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Ontology Evaluation Tools: Current and Future Research

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Recent Advances in Computer Science and Communications 2023年第6期16卷 2-15页

作者： Debnath, Narayan C. Patel, Archana Department of Software Engineering School of Computing and Information Technology Eastern International University Thu Dau Mot Viet Nam

In recent years, the increasing interest in ontologies resulted in the developing and publishing of many ontologies in the same or different domains. When users try to reuse the existing ontologies in their applications, they may encounter problems determining the most appropriate ontology based on their needs. Ontology evaluation is a process that helps users to examine the quality of the ontology based on different attributes. Many accessible and usable tools for ontology evaluation have been studied in the literature. However, finding an efficient ontology evaluation tool, following ontology specifications and their requirements (advantages/disadvantages), is still missing, limiting the researchers from determining possible future research. This paper aims to help new researchers and practitioners identify appropriate ontology evaluation tools based on their requirements and provide guidelines for future research directions on the same topic. This paper provides a detailed description of the different types of ontologies and classifies the available ontology evaluation tools into two categories, namely domain dependant ontology evaluation tools and domain-independent ontology evaluation tools. © 2023 Bentham Science Publishers.

关键词： Ontology

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Machine Learning-Driven Sri Lankan Stock Market Prediction: Harnessing Economic Indicators and Sentiment Analysis 4

Machine Learning-Driven Sri Lankan Stock Market Prediction: ...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Dinushan, S. Wijegunasekara, M. Carmel University of Kelaniya Faculty of Computing and Technology Department of Software Engineering Kelaniya Sri Lanka

ISBN: (纸本)9798350384864

The stock market plays a pivotal role in a nation's economy. People tend to invest in the stock market due to its high rate of return. However, predicting the stock market remains a formidable challenge. This research uses machine learning algorithms to predict the ASPI and S&P 20 monthly stock market indexes by considering global economic conditions, macroeconomic variables, and sentiment data. Correlation tests, forward stepwise regression, and p-value tests were carried out to identify the influential variables. Machine learning algorithms including deep learning were utilized for model building, using the influential variables dataset. Shuffling and non-shuffling methods were deployed during dataset splitting. Additionally, included the previous month's stock value to the influential variables dataset and re-conducted the model building to assess the performance. The best-performing model (with the lowest MAE rate) was selected separately for ASPI and S&P 20 prediction. © 2024 IEEE.

关键词： Forecasting

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Edge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyML 2nd

Edge Intelligence for Wildlife Conservation: Real-Time Hornb...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Hing, Kong Ka Behjati, Mehran Saleh, Vala Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Electrical Engineering Shiraz University of Technology Shiraz Iran Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Hornbills, an iconic species of Malaysia's biodiversity, face threats from habitat loss, poaching, and environmental changes, necessitating accurate and real-time population monitoring that is traditionally challenging and resource intensive. The emergence of Tiny Machine Learning (TinyML) offers a chance to transform wildlife monitoring by enabling efficient, real-time data analysis directly on edge devices. Addressing the challenge of wildlife conservation, this research paper explores the pivotal role of machine learning, specifically TinyML, in the classification and monitoring of hornbill calls in Malaysia. Leveraging audio data from the Xeno-canto database, the study aims to develop a speech recognition system capable of identifying and classifying hornbill vocalizations. The proposed methodology involves preprocessing the audio data, extracting features using Mel-Frequency Energy (MFE), and deploying the model on an Arduino Nano 33 BLE, which is adept at edge computing. The research encompasses foundational work, including a comprehensive introduction, literature review, and methodology. The model is trained using Edge Impulse and validated through real-world tests, achieving high accuracy in hornbill species identification. The project underscores the potential of TinyML for environmental monitoring and its broader application in ecological conservation efforts, contributing to both the field of TinyML and wildlife conservation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental monitoring

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Analysis of Encryption Techniques for Secure Data in Cloud Environment 1

Analysis of Encryption Techniques for Secure Data in Cloud E...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kumar, M. Ravi Jeya, I. Jasmine Selvakumari Athira, S. Hindusthan College of Engineering and Technology Department of Computer Science And Engineering Coimbatore India Vit Bhopal University School of Computing Science and Engineering Sehore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331529246

Over the years, Cloud computing is becoming increasingly popular due to the continually changing technology. The primary goal of the cloud computing network is to offer consumers pay-per-use usage of on-demand processing and storage resources, enabling low-cost, efficient infrastructure utilization for smaller businesses. However, because of the feature of cloud resource sharing, data security and privacy create issues, and prevent smart systems from continuously transferring produced data to the cloud computing resources. Many encryption methods were presented to address data security concerns. This article presents the processes of several data protection and encryption systems currently in use and critically analyzes them based on common factors. The purpose of this survey is to compile data encryption methods now in use, often discussed in the survey of smart systems for data security when applied to the cloud platforms. © 2025 IEEE.

关键词： Cloud security

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Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud computing Environment

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Computer Systems Science & engineering 2023年第3期44卷 1881-1895页

作者： S.Stewart Kirubakaran V.P.Arunachalam S.Karthik S.K annan Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia School of Computing Science and Engineering Vellore Institute of TechnologyBhopalMadhya PradheshIndia

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud ***,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data *** addressing and handling the security-related issues on Cloud,several models were *** that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud *** preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data ***,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the ***,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

关键词： Third-party auditor(TPA) efficient auditing technique(EAT) cloud service provider(CSP) data user(DU) data security privacy-preserving cloud computing cloud security

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Network Intrusion Detection System Using Optuna 5

Network Intrusion Detection System Using Optuna

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Parekh, Nishank Sen, Arzob Rajasekaran, P. Jayaseeli, J. D. Dorathi Robert, P. School of Computing Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Chengalpattu India School of Computing Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331518097

The rapid growth of the digital industry has created a higher demand for robust Network Intrusion Detection Systems (NIDS) to protect valuable information and the integrity of network infrastructures as the digital industry grows rapidly. One of the most important challenges in the current intrusion detection landscape is the growing sophistication of cyber threats, including zero-day attacks, polymorphic malware, and advanced persistent threats, which are difficult to detect using traditional methods. Furthermore, systems often suffer from high false positive rates and struggle to scale effectively in real-time applications. Traditionally, intrusion detection methods were quite effective, but performance is still lacking due to the inability to adapt to evolving threats. Recent breakthroughs include deep learning approaches, ensemble methods, and hybrid detection models. However, these are still plagued by high computational overhead and a lack of transparency in their decision-making processes. The work exploits Optuna for the optimization of hyperparameters, specifically in the performance improvement of various ML models. Among the best-ranked frameworks for the optimization of hyperparameters, Optuna provides a principled method for tuning hyperparameters, resulting in significantly enhanced accuracy and efficiency of the intrusion detection model. The implication of this research work is that it searches for the best configuration of parameters for each algorithm with balanced false positives and detection rates. The study includes an overall scenario of recent development in NIDS. More precisely, this paper shows how Hyperparameter tuning attains very superior model performance compared to other models. The comparative results presented have shown that models which are optimized using Optuna surpass the non-optimized ones by a huge margin with respect to accuracy, recall, precision, and F1-score. The paper also discusses ensemble techniques by integrating the

关键词： Network intrusion

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Sales Analysis- Review and Recommendations on Business Intelligence 24

Sales Analysis- Review and Recommendations on Business Intel...

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24th International Arab Conference on Information technology, ACIT 2023

作者： Al Rumhi, Saif Sivakumar, Janaki Global College of Engineering and Technology Department of Computing Muscat Oman

ISBN: (纸本)9798350384307

A retail company used Power BI to analyse sales data for three months. The team developed 1 00 %. stacked bar charts, donut charts, ribbon charts, line charts, and tree maps from the 'Supermarket Sales' dataset. Data visualisations indicated product category sales performance across three grocery branches, consumer behaviours and satisfaction, peak sales hours, and payment methods. Based on current trends. The team advised retail company adjustments to boost sales and consumer satisfaction. 23.1% of Customers had a terrible experience, and branch peak sales hours varied. The team advised sales and customer satisfaction enhancements. Based on their findings, the team proposed focusing on fashion and technology accessories to boost sales, offering incentives to use credit cards, and addressing unhappy clients. These adjustments may help the retail company boost sales and customer satisfaction. © 2023 IEEE.

关键词： power bi Retail

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CNN-LSTM: A Novel Hybrid Deep Neural Network Model for Brain Tumor Classification

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Intelligent Automation & Soft computing 2023年第7期37卷 1129-1143页

作者： R.D.Dhaniya K.M.Umamaheswari Department of Computer Science and Engineering SRM Institute of Science and TechnologyKattankulathur603202India Department of Computing technologies SRM Institute of Science and TechnologyKattankulathur603202India

Current revelations in medical imaging have seen a slew of computer-aided diagnostic(CAD)tools for radiologists *** tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging(MRI).In this work,we reported on new observations based on binary brain tumor categorization using HYBRID ***,the collected image is pre-processed and augmented using the following steps such as rotation,cropping,zooming,CLAHE(Contrast Limited Adaptive Histogram Equalization),and Random Rotation with panoramic stitching(RRPS).Then,a method called particle swarm optimization(PSO)is used to segment tumor regions in an MR *** that,a hybrid CNN-LSTM classifier is applied to classify an image as a tumor or *** this proposed hybrid model,the CNN classifier is used for generating the feature map and the LSTM classifier is used for the classification *** effectiveness of the proposed approach is analyzed based on the different metrics and outcomes compared to different methods.

关键词： Brain tumor segmentation particle swarm optimization CNN-LSTM convolution neural network

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Setting Standards for Personal Health Data in the Age of 5G and 6G Networks

Journal of ICT Standardization

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Journal of ICT Standardization 2024年第1期12卷 47-70页

作者： Koren, Ana Prasad, Ramjee University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia CTIF GLOBAL CAPSULE Department of Business Development and Technology Aarhus University Herning Denmark

Electronic health records (EHRs) play a vital role in simplifying thorough and effective patient treatment, promoting smooth exchange of information between medical professionals, and enhancing the process of making clinical decisions. With the increasing adoption of sensor-embedded smart wearables and home automation devices, new opportunities arise for innovative solutions in various sectors, such as eHealth. In the age of 5G and 6G, the potential of utilizing user-collected health data becomes vast, promising significant improvements in people’s health and well-being. Realizing continuous healthcare access takes a step closer to reality by equipping EHRs to effectively store and interpret data collected by these sensors. This would result in personalized medical services that adhere to standardized practices. This paper presents a comprehensive review of contemporary advancements in the realm of standardization methods aimed at managing personal health data. The study delves into an extensive analysis of state-of-the-art solutions that have emerged to address the intricate challenges associated with the harmonization and uniformity of personal health information. By systematically examining these cutting-edge approaches, the review elucidates the diverse strategies employed to establish a cohesive framework for organizing, storing, and exchanging personal health data. Furthermore, the review critically evaluates the effectiveness and limitations of each solution in terms of promoting interoperability, safeguarding data privacy, and facilitating seamless data sharing among healthcare stakeholders. Furthermore, this paper then presents an approach to standardize the data by establishing semantic constraints for healthcare data types and proposing a validation procedure to ensure compliance with relevant standards and regulations. © 2024 River Publishers.

关键词： eHealth

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