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Leveraging IoT and Machine Learning for intelligent Water Quality Monitoring systems

Leveraging IoT and Machine Learning for Intelligent Water Qu...

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2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Baranwal, Gaurav Singh, Kaushtubh Gupta, Ayush Tiwari, Harsh N. Gupta, Tukur Ajay Kumar Garg Enginnering College Department of Electronics and communication Engineering Ghaziabad India

ISBN: (纸本)9798331527495

Water quality monitoring is a crucial aspect of public health, particularly in mitigating the risks associated with waterborne diseases and ensuring safe drinking water. Traditional monitoring methods are often labor-intensive, costly, and lack real-time capabilities, making them unsuitable for widespread and rural deployment. This paper examines the integration of advanced technologies, specifically the Internet of Things (IoT) and Machine Learning (ML), to design a sophisticated, low-cost water quality monitoring system. Leveraging an array of sensors, including pH, turbidity, temperature, total dissolved solids (TDS), and dissolved oxygen (DO), combined with the advanced embedded processor, the system ensures seamless data acquisition and processing. IoT enables continuous remote monitoring, while ML algorithms provide predictive analytics and intelligent anomaly detection to identify potential contaminants and trends. These value-driven solutions underscore the transformative potential of advanced technologies in revolutionizing water quality assessment. This review article carefully reviews the available literature on water quality assessment in order to lay out an assortment of conclusions on the concerns, problems, challenges, and research gaps that have emerged during the last few years. Research gaps are identified by thoroughly examining the previously suggested methodologies. The study presented in this article primarily identified the problems with the accuracy of the models, the generation of data collection mechanisms, and the types of data that were used in the suggested frameworks. Subsequently, research avenues towards smart water quality monitoring are offered by examining key concepts crucial to the expansion of this field of research. © 2025 ieee.

关键词： Damage detection

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Next-Generation Intrusion Detection systems with Machine Learning and Deep Learning-Based Firewalls 3

Next-Generation Intrusion Detection Systems with Machine Lea...

引用

3rd international conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Rani, Ritu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

The growing complexity of cyber-attacks has underlined the requirement of strong Intrusion Detection systems (IDS) able to instantly recognize a variety of hazards. This work offers a strategy for improving Next-Generation Intrusion Detection systems (NGIDs) combined with firewalls by use of machine learning (ML) and deep learning (DL) approaches. Using the NSL-KDD dataset - which includes both normal and attack network traffic - the work creates a system able to classify traffic patterns into "Normal"and "Attack"categories. The proposed method consists in four phases: data collecting, data preprocessing, feature selection and extraction, and classifier application. In the preprocessing stage, the dataset is cleaned and normalized;hence, suitable feature selection follows to increase classification accuracy. Trained and tested for classification using performance criteria including accuracy, precision, recall, and F1-score, naïve bayes and support vector machine (SVM) models are then compared in efficacy. The results reveal that the SVM classifier has low misclassification and great accuracy (97.29%), therefore proving its prospective use in advanced IDS. The article underlines how several models could be merged inside a firewall system to offer real-time network protection. Future studies will look at using deep learning techniques and ensemble methods to raise detection accuracy even more, therefore allowing NGIDS to be more adaptable and efficient in countering evolving cyber threats. © 2025 ieee.

关键词： Network intrusion

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Implementing RDMA over Soft-RoCE with Basic eBPF Monitoring: A Cost-Effective Approach for High-Performance data Communication

Implementing RDMA over Soft-RoCE with Basic eBPF Monitoring:...

引用

2025 international conference on computing for Sustainability and intelligent Future, COMP-SIF 2025

作者： Kagal, Suresh Narasimha Murthy, M.S. Mutalikdesai, Tejas Yugal Kishore, V. Tejaswini, T.V. Bms Institute of Technology and Management Dept. of Information Science & Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331538538

RDMA is a technology that enables extremely fast communication between different systems by allowing these systems to transfer data without using the CPU and operating system. This results in the rapid transfer of data and reduces latency in data transfer. RDMA, brings more efficiency in moving the data between systems, improving general performance and responsiveness in many applications as CPU is not involved in the data transfer. Traditional RDMA solutions usually require special and costly hardware, such as RoCE adapters. This can make them less accessible for companies with lesser budgets. Soft-RoCE is a software-based solution that brings RDMA capabilities to standard Ethernet hardware. In this way, we can replicate the benefits that RDMA offers without the expensive equipment, hence making it possible in environments where it would not have been feasible. Also, an innovative monitoring framework using eBPF is introduced, which offers comprehensive real-time intelligence to RDMA performance by decreasing the latency and throughput. © 2025 ieee.

关键词： Budget control

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Cultural Intelligence in AI Ethics: A Systematic Framework for Implementation Across Global Contexts 3

Cultural Intelligence in AI Ethics: A Systematic Framework f...

引用

3rd international conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Kumar, Devesh Kumar, Kamlesh Choudhary, Rahul Kumar Singh, Varnika NIT Hamirpur Dept. of Computer Science & Engineering India NIT Hamirpur Dept. of Electrical Engineering India Panjab University UIET Dept. of Electronics & Communication Engineering Chandigarh India

ISBN: (纸本)9798331523893

This research examines artificial intelligence ethics implementation across diverse cultural contexts through case studies in healthcare, finance, education, and corporate sectors. Using mixed-methods analysis across six cultural regions, the study reveals fundamental variations in approaches to AI ethics, particularly regarding data privacy and human-technology relationships. The integration of Western, Eastern, and African perspectives yields three frameworks: a cultural intelligence model for AI development, an adaptive governance mechanism, and a cross-cultural collaboration approach. Results demonstrate that successful AI implementation requires balancing diverse cultural perspectives with technical innovation through flexible governance structures, providing practical pathways for culturally sensitive AI development. © 2025 ieee.

关键词： intelligent computing

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Parallel computing Optimization of Tourism Big data based on Crawler Programs 1

Parallel Computing Optimization of Tourism Big Data based on...

引用

1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Zhou, Xiaoyu Wuhan Donghu University School of Management Hubei Wuhan China

ISBN: (纸本)9798331529246

With the rapid development of the tourism industry, traditional tourism methods are undergoing significant transformation, and online tourism is gradually becoming a new highlight in the market. However, faced with the challenges of big data, online tourism data experiences tremendous pressure in terms of storage, computation, and management, and the issue of information overload seriously affects the user experience. Therefore, there is an urgent need for innovative platforms and recommendation algorithms to optimize the screening and recommendation of tourism information. This paper proposes an optimization scheme based on the Spark cloud computing platform to improve the efficiency of tourism data processing. First, utilizing Spark's distributed computing and memory management capabilities, tourism data can be stored and processed in a distributed manner, thus accelerating the computation speed of recommendation algorithms. Secondly, a Python-written crawler program is used to obtain real tourism data, including user ratings and comments on attractions, to evaluate the effectiveness of the improved algorithm. Finally, a weighted algorithm combining the LDA topic model is proposed. This algorithm analyzes the topic distribution of comment texts and weights the comments based on user ratings, calculating the predicted score for unrated attractions for the target user, thereby achieving accurate attraction recommendations. The experimental results show that the algorithm proposed in this study demonstrates significant advantages in prediction accuracy. © 2025 ieee.

关键词： Cloud platforms

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Indian Gross Domestic Product Per Capita Prediction based on Economic Indicators 5

Indian Gross Domestic Product Per Capita Prediction based on...

引用

5th international conference on Emerging systems and intelligent computing, ESIC 2025

作者： Sarveswaran, M.G. Mahalakshmi, B. Vaishnav, Varun Mishra, Shruti Satapathy, Sandeep Kumar Mallick, Pradeep Kumar School of Computer Science & Engineering Vit University Tamilnadu Chennai India Centre for Advanced Data Science Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Engineeing Kiit University Odisha India

ISBN: (纸本)9798331522100

This paper is about identifying the contributing socio-economic indicators that affect the Gross Domestic Product Per Capita of India with historical data from 1960 to 2021 and build a methodology to predict the Gross Domestic Product per capita based on the contributing factors. To predict India's GDP per capita, an ensemble prediction was used, which included Support Vector Regression (SVR), the Extreme Gradient Boosting Model, and the ARIMA Model. It is critical to forecast GDP per capita and its influencing factors for better policy formulation, resource allocation, risk management, investment decisions, long-term planning, social welfare, and other purposes. This paper could contribute to encouraging long-term economic growth and improve citizens' quality of life. The findings of this paper provide an in-depth understanding of the intricate relationship between socioeconomic indicators and Gross Domestic Product in India. © 2025 ieee.

关键词： Risk management

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An intelligent SCADA System for Power Distribution Network Cable Fault Detection with Real-Time Monitoring and Autonomous Maintenance 4

An Intelligent SCADA System for Power Distribution Network C...

引用

4th international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Nahidul Islam, S.M. Ahmad, Shameem Prodhan, Md. Mahbub-E-Elahi Khan Chowdhury, Md. Shoaib Bin Mizan, Sakib Ahmed, Abir Jahan, Effat Hazari, Md. Rifat Hossain, Chowdhury Akram American International University-Bangladesh Faculty of Engineering Department of Electrical and Electronic Engineering Dhaka1229 Bangladesh American International University-Bangladesh Faculty of Engineering Department of Computer Engineering Dhaka1229 Bangladesh

ISBN: (纸本)9798331530938

Supervisory Control and data acquisition (SCADA) systems are significant for boosting industrial operations, but established systems often struggle with fault management and real-time monitoring. To address these issues, this paper proposes an advanced SCADA system that integrates IoT technologies, multi-agent systems, and predictive maintenance for seamless data collection, analysis, and decision-making. Machine learning enhances fault detection and cybersecurity, while a modular hardware design allows scalability and flexibility to various industrial environments. The system is highly sensitive to detecting faults, including open circuit faults, line-to-ground faults, line-to-line faults, double line to ground faults and three-phase faults. It improves operational safety and efficiency by accurately locating faults and notifying professionals in real time. Extensive simulations and hardware tests were conducted to validate its performance in different fault scenarios. The results show that the proposed SCADA system has successfully detected different types of faults, offering a unified approach for fault management, maintenance, and real-time monitoring. © 2025 ieee.

关键词： SCADA systems

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Boosting Software Quality: A Machine Learning Technique for Predicting Defects 6

Boosting Software Quality: A Machine Learning Technique for ...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Akilan Daniel, E. Alwin Infant, P. Jainish, G.R. Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (纸本)9798331522667

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. This work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. The objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. These indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. These algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. This approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges. © 2025 ieee.

关键词： data reliability

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Research on Mechanical Automation Control based on Bidirectional Long Short-Term Memory 1

Research on Mechanical Automation Control based on Bidirecti...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Song, Xiaolu Zaozhuang Vocational College of Science & Technology College of Mechanical and Electrical Engineering China

ISBN: (纸本)9798331529246

The rapid advancement of industrial automation has led to increased reliance on intelligent control systems for optimizing mechanical processes. Traditional automation methods often struggle with dynamic environmental conditions, system uncertainties, and real-time decision-making challenges. Many existing approaches, such as PID controllers and conventional neural networks, fail to effectively handle sequential dependencies in time-series data, resulting in suboptimal system performance. To address these limitations, this research presents an advanced mechanical automation control framework based on a Bidirectional Long Short-Term Memory (BiLSTM) algorithm. The BiLSTM model enhances automation efficiency by capturing long-range dependencies in sequential data, improving predictive accuracy and system adaptability. By leveraging bidirectional processing, the model effectively learns past and future context, leading to superior performance in control precision and response time. Experimental evaluations demonstrate that the proposed framework achieves an accuracy of 98.2%, significantly outperforming conventional automation control techniques in terms of robustness, adaptability, and computational efficiency. This study contributes to the development of intelligent, data-driven mechanical automation systems that align with Industry 4.0 objectives and enhance operational reliability. © 2025 ieee.

关键词： Three term control systems

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Weighted Graph Clustering with PaCCo 5

Weighted Graph Clustering with PaCCo

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5th international conference on Emerging systems and intelligent computing, ESIC 2025

作者： Soma, Arun Kumar Park University Department of Information Systems & Business Analytics 8700 NW River Park Dr ParkvilleMO64152 United States

ISBN: (纸本)9798331522100

Graph data analysis benefits from detecting substructures to infer meaning and interpret data correctly. However, many existing graph clustering algorithms are not applicable to weighted graphs, which contain edge weights representing interaction magnitudes. To address this, the PaCCo algorithm maximizes node similarity and cluster interconnectivity while accounting for inter-cluster links. This paper reviews related algorithms and evaluates PaCCo's performance. Results show that PaCCo offers benefits such as parameter independence, automatic clustering, and reduced runtime, making it suitable for various applications. Future enhancements could include incorporating fuzzy clustering and transitioning to a distributed computation paradigm for scalability. © 2025 ieee.

关键词： Cluster computing

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