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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sree, K. Navya Reddy, Mancharla Rakesh Saraswathi, S. Department of Computer Science and Engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. This research focuses on the development of an advanced air quality forecasting system using machine learning techniques. The system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. The model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. The system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. This research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies. © 2025 IEEE.

关键词： air pollution Air quality forecasts cardiovascular diseases data-driven strategies environmental policy global climate international collaboration machine learning NO2 O3 PM2.5 policy-making pollution mitigation proactive alerts public health real-time monitoring respiratory diseases spatial variability temporal variability transboundary pollution

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BD-Trust: behavioural and data trust management scheme for internet of things

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16195-16207页

作者： Sharma, Avani Pilli, Emmanuel S. Mazumdar, Arka P. Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

The emergence of Internet of Things (IoT) has paved the way to realize ubiquitous access to manifold services and applications. Effective utilization of these services vitally demands secure deployment of IoT systems where data or information can be transmitted without worrying about the uncertainty of different devices in the system. Trust Management systems (TMS) have proved themself as impressive solutions to attain this security requirement where entities are judged based on their behavior. For IoT, influential deployment of TMS requires coping with characteristics of IoT systems such as device heterogeneity, context-awareness, dynamicity, etc. Paying attention to the quality of received data for reliable service composition and accurate assessment is also a matter of concern. This paper presents a direct observation-based distributed trust management scheme, called BD-Trust, in which trust is measured by integrating behavioral trust of devices with the data trust. For evaluation of behavioral trust, BD-Trust reckons on three trust parameters, namely success rate, satisfaction level, and credibility. Data trust is measured considering consistency in received data values for the successful interaction responses. BD-Trust employs a trust update mechanism to alleviate the effect of malicious devices that try to disrupt the process of trust management. An adaptive decay factor has been designed to reflect previously observed trust value in recent computation while updating trust scores. The effectiveness of BD-Trust has been validated through simulation experiments and results have been compared with two state-of-art schemes for trust convergence, assessment accuracy, and resilience against attacks, considering a dynamic environment. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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DD-SPP: Dynamic and Distributed Service Placement Policy for Optimal Scheduling in Fog-Edge Computing

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SN computer science 2024年第7期5卷 801页

作者： Kaur, Navjeet Bhardwaj, Vivek Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University School of Computer Science and Engineering Manipal University Jaipur

The existing cloud model unable to handle abundant amount of Internet of Things (IoT) services placed by the end users due to its far distant location from end user and centralized nature. The edge and fog computing are the latest technologies for developing smart cities that are becoming popular to efficiently support latency-sensitive IoT application services. But these advanced technologies suffer from limited size and computation capabilities;therefore, optimal placement of services among available resources is an open issue. Therefore, the proposed work target the stated issue and presents a Dynamic and Distributed Service Placement Policy (DD-SPP) considering the edge and fog devices. The model is divided into three main phases, that is, the Service Type Estimator (STE), the Service Dependency Estimator & Resolution (SDER), and Resource Demand Estimator & Scheduling (RDES). STE and RDES modules depend on each other to perform optimal service placement. The results of the implementation showed satisfactory improvement with respect to present state-of-the-art policy where delay is improved by around 41%, energy is improved by 27%, response time by 28% and overall cost by 28%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud computing DD-SPP Edge computing Fog computing IoT Scheduling Service placement policy

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A Novel Fuzzy-Based Modified GAN and Faster RCNN for Classification of Banana Leaf Disease

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Journal of The Institution of Engineers (India): Series A 2023年第3期104卷 529-540页

作者： Raja, N. Bharathi Rajendran, P. Selvi Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Kelambakkam India

In the global marketplace, agriculture plays an important role. However, diseases produced in plants mostly affect the financial system. Pest occurrence and climatic changes are the leading trouble that the banana plant leaf faces, which influences the quality of fruit and crop yields. To predict the leaf disease symptoms in an early stage, the automatic banana leaf disease detection technique is more significant. The traditional deep neural network methods used in banana plant leaf detection need more parameters and utilize complex architectures. To address such problems, modified generative adversarial networks–modified faster region-based convolutional neural networks with fuzzy (MGAN–MFRCNN with Fuzzy) is proposed for identifying banana leaf disease. The most effective framework in deep learning is GAN. The autoencoder is incorporated within the fundamental GAN design for developing a modified GAN model;it is used for data augmentation. To perform feature extraction from high-resolution networks, an MFRCNN is used in our proposed method. The improved and more accurate classification scores are obtained by combining fuzzy with the ensemble model. The experimentation is carried out using a banana plant leaf dataset in which the healthy leaf, xanthomonas-affected leaf, and sigatoka-affected leaf diseases are detected and classified. The proposed model reached 98% accuracy, 97% precision, 96% F1-score, 98% specificity, and 98% sensitivity, which is better than other existing methods. © 2023, The Institution of Engineers (India).

关键词： Generative adversarial networks

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A Study on Transfer Learning TinyML-Based Intrusion Detection Framework on IoT Devices

A Study on Transfer Learning TinyML-Based Intrusion Detectio...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Mwaura, Jedidah Araki, Shunsuke Musthafa, Muhammad Bisri Huda, Samsul Nogami, Yasuyuki Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Graduate School of Environmental Life Natural Science and Technology Okayama University Okayama Japan Green Innovation Center Okayama University Okayama Japan

ISBN: (纸本)9798331521165

Currently, there are many ongoing attempts to incorporate transfer learning into Intrusion Detection systems. Transfer learning leverages on the knowledge of previous models thus reducing training time, improving accuracy and making it possible to train new models with limited data. However, it is difficult to incorporate such a technique into IoT devices with limited computational power. In this paper, we propose a Transfer Learning TinyML-based IDS framework with feature selection to be implemented on a microcontroller for detecting attacks on IoT devices. To implement the proposal, we leveraged knowledge achieved from our previous DDoS attack detection model to create a new transfer learning framework that could detect various IoT attacks such as malware attacks and zero-day attacks. To test the transfer learning framework, we trained it using two separate sets of datasets each containing different attacks. After each training, we evaluated the original model, converted it into a format compatible with a microcontroller and evaluated it further. The TL TinyML-based framework was evaluated based on its accuracy, model sizes and average inference times. As the results confirmed, the framework performed well and can successfully be implemented on a microcontroller without depleting its resources. © 2025 IEEE.

关键词： computer viruses

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Sensitive information leakage analysis of database code by abstract interpretation

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International Journal of Security and Networks 2023年第2期18卷 91-105页

作者： Jana, Angshuman Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

An information system stores outside data in the backend database to process them efficiently and protects sensitive data from illegitimate flow or unauthorised users. However, most information systems are made in such a way that the sensitive information stored in a database may be leaked explicitly or implicitly during data processing along with the control structure of the program to the output channels. Therefore, sensitive data leakage is one of the crucial security threat. In this paper, the main objective is to detect the illegitimate flow of confidential information in an information system. We propose a framework to detect sensitive information leakage through the data-flow paths of an information system. In particular, to compute the precise set of data-flow paths, we use the non-relational abstract property of the interval domain and the relational abstract property of the polyhedra domain that enables the framework to produce efficient security analysis results. © 2023 Inderscience Enterprises Ltd.

关键词： Formal methods

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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Fuzzy modelling techniques for improving multi-label classification of software bugs

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International Journal of Innovative Computing and Applications 2023年第3期14卷 141-154页

作者： Panda, Rama Ranjan Nagwani, Naresh Kumar Department of Computer Science and Engineering National Institute of Technology Chhattisgarh Raipur India

Software bug repositories stores a wealth of information related to the problems that occurred during the software development. Today’s software development is a modular approach, with multiple developers working in different locations all around the world. A software bug may belong to multiple categories and can be resolved by more than one developer. For understanding the multiple causes of software bugs and proper bug information management at large bug repositories, better classification of software bugs is needed. In the proposed work, a multi-label fuzzy system-based classification (ML-FBC) is proposed. A fuzzy system is used to compute the membership of software bugs into multiple categories. Then a fuzzy c-means clustering algorithm is used to create various clusters. Once the clusters are created, the cluster-category mapping is done for various software bugs. For a new bug, the fuzzy similarity values are computed, and the created cluster-category mappings are utilised to categorise it. Using a user-defined threshold value, a new bug is classified into multi-label categories. Experiments are carried out on available benchmark datasets to compare the performance measures F1 score, BEP score, Hloss, accuracy, training time, and testing time of various multi-label classifiers. The proposed ML-FBC outperforms existing multi-label classifiers. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Information management

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Deep Learning System and It’s Automatic Testing: An Approach

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Annals of Data science 2023年第4期10卷 1019-1033页

作者： Khan, Rijwan Department of Computer Science and Engineering ABES Institute of Technology UP Ghaziabad India

The process of testing conventional programs is quite easy as compared to the programs using Deep Learning approach. The term Deep learning (DL) is used for a novel programming approach that is highly data centric and where the governing rules and logic are primarily dependent on the data used for training. Conventionally, Deep Learning models are evaluated by using a test dataset to evaluate their performance against set parameters. The difference in data and logic handling between programs using conventional methods and programs using the DL approach makes it difficult to apply the traditional approaches of testing directly to DL based programs. The accuracy of test data is currently the best measure of the adequacy of testing in the DL based systems. This poses a problem because of the difficulty in availability of test data that is of sufficient quality. This in turn restricts the level of confidence that can be established on the adequacy of testing of DL based systems. Unlike conventional applications, using the conventional programming approaches the lack of quality test data and the lack of interpretability makes the system analysis and detection of defects a difficult task in DL based systems. So testing of DL based models can be done automatically with a different approach compared to normal software. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Automatic testing Deep learning system Automatic test case generation Software testing Unit testing

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Knowledge As Not Only Justified True Beliefs in Vision

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 297-299页

作者： Wenbo Zheng Fei-Yue Wang IEEE School of Computer Science and Artificial Intelligence Wuhan University of Technology Sanya Science and Education Innovation Park of Wuhan University of Technology the State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences

COMPUTATIONAL knowledge vision [1] is emphasized as a novel perspective or field in this paper. It first proposes the visual hierarchy and its connection to knowledge, stating that knowledge is a justified true belief. To further the previous research, we concisely summarize our recent works and suggest a new direction that knowledge is also a thought framework in vision.

关键词： Computational modeling Visualization Artificial intelligence Data models Visual perception Visual databases Training data

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