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International Journal of Interactive Mobile Technologies 2022年第21期16卷 49-70页

作者： Islam, Gazi Zahirul Hossain, Md. Mobarak Faruk, Md. Nur, Fernaz Narin Hasan, Nayeem Khan, Khalid Mahbub Tumpa, Zerin Nasrin Department of Computer Science and Engineering Southeast University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka Bangladesh School of Computer Science Western Illinois University IL United States Department of Computer Science and Engineering University of Information Technology & Sciences Dhaka Bangladesh

Gas Leakage and its fatal effects are a great concern throughout the world, especially in developing countries like Bangladesh. Every year lots of people died and countless damages to assets occur due to the fire caused by the gas leakage. Not only that but gas leakage and explosion are also very harmful to the climate. Thus, a system to detect gas leakage and preventive measures is of utmost importance. In this project, we design and implement an intelligent IoT prototype to detect gas leakage, and the fire caught by gas leakage. Our goal is to minimize the effect of gas leakage by taking some protective measures. When the gas sensor, detects the gas leakage, the solenoid valve shuts off the gas line, and the exhaust fan starts to run. Again, when the flame sensor detects a fire, the sucker throws the fire extinguisher balls at the fire. The GSM SIM module notifies the user by sending a message to his smartphone. The buzzer sounds when a mishap occurs and the LCD monitor always shows the status of the system. In this way, we have efficiently designed and implemented a low-cost and intelligent gas leak detection and fire suppression system. © 2022, International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Internet of things

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Laryngeal Cancer Detection and Classification Using Deep Learning on Histopathological Images 29

Laryngeal Cancer Detection and Classification Using Deep Lea...

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29th International Conference on Information Technology, IT 2025

作者： Tonay, Mohammad Asif Khan Dihan, Shahriar Sadman Zarif, Farid Ishraqe Adnan, Omar Rafat Ali, Md. Nawab Yousuf East West University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798331517649

Laryngeal cancer has complex and subtle symptoms, which pose challenges in its early diagnosis and detection. Our research applies deep-learning-based image classification models to classify and detect the early stages of laryngeal cancer using histopathological images. Through our study, a dataset contains a collection of 1320 tissue images augmented to 5280 images which are divided into Healthy (He), Hypertrophic Blood Vessels (Hbv), IPCL-like Vessel (IPCL), and Leukoplakia (Le), preprocessed and augmented to enhance and optimize the model performance. Several deep-learning and CNN-based pre-trained models like ResNet50, DenseNet121, DenseNet201, VGG16, VGG19, MobileNetV2, and InceptionV3, were tested. We have taken our dataset from an Italian researcher. Our fine-tuned Resnet50 model has achieved a high accuracy of 99.62%, which surpasses all the previous works. Along with the accuracy our goal in this research was to improve the precision and recall of the models as it is sensitive to disease classification. © 2025 IEEE.

关键词： Lung cancer

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Navigating Data Scarcity in Multivariate Time Series Forecasting: A Hybrid Model Perspective

Navigating Data Scarcity in Multivariate Time Series Forecas...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Saravana, M.K. Roopa, M.S. Arunalatha, J.S. Venugopal, K.R. University of Visvesvaraya College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364866

Data scarcity in Multivariate Time series (MTS) analysis (MTS) poses significant challenges across various fields, including scientific research, business, healthcare, and technology development. Factors such as, high costs of data collection, sensor failures, and the complexity of maintaining synchronized multivariable datasets limits the data availability for MTS analysis. The paper presents an innovative approach for energy consumption forecasting in the context of limited historical data by integrating Graph Neural Networks (GNNs) with Long Short-Term Memory (LSTM) networks. It begins with thorough data preprocessing, enhancing the dataset with temporal features. Sophisticated GNN model is fine-tuned using hyperparameter optimization, bolstered by K-Fold cross-validation for robustness. Synthetic data generated from the trained GNN model enriches the training set. Subsequently, an LSTM network captures temporal dependencies, trained with early stopping techniques to prevent overfitting. Evaluation encompasses metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 score. Visual inspection of real versus forecasted data validates the model's performance. By seamlessly integrating data science and machine learning, this approach advances energy consumption forecasting for sustainable energy management. © 2024 IEEE.

关键词： Graph neural networks

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Analysis of deep actor-critic methods for classifying cancer subtypes through gene expression

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第4期8卷 46-66页

作者： Jayakrishnan, R. Meera, S. Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

The word “cancer” denotes a syndromes that can spread to various bodily areas and are brought on by abnormal cell proliferation. After cardiovascular illnesses, according to the World Health Organisation (WHO), cancer is the second largest cause of death in the world. To better understand molecular processes behind various cancer subdivisions, cancer categorization depends on gene expression information is essential. Conventional machine learning methods have proven helpful in this situation, but new approaches are needed for accurate and understandable categorisation due to the difficulty and dimensionality of gene expression datasets. In this article, we analyse various methods for multiclass categorising cancer subtypes using deep structured reinforcement learning (DSRL). Our methodology addresses several significant issues in cancer subtype classification by combining the strength of deep neural networks with reinforcement learning. In this research, seven different gene expression datasets are utilised to classify the cancer subtype. We also used different classification approaches in Python for the same dataset to perform a comparative study. Deep reinforcement learning for cancer subtype classification improves the accuracy of gene expression data by integrating intricate data patterns, enabling customised therapies, and expanding the field of precision medicine research. The analysis reveals that the newly suggested model exceeds the contemporary state-of-the-art classifiers, achieving the highest accuracy across all seven datasets, ranging from 55% to 100%, while attaining the lowest loss, which varies between 0.02 and 0.11. This work offers a viable method for classifying cancer subtypes into many categories using gene expression data. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Cancer subtypes Deep neural networks Deep-learning Gene expression Machine-learning Reinforcement learning

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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Formula 1 Race Winner Prediction Using Random Forest and SHAP Analysis

Formula 1 Race Winner Prediction Using Random Forest and SHA...

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2025 International Conference on Control, Automation, and Instrumentation, IC2AI 2025

作者： El Haber, Elias Sawaya, Elie Attieh, Maroun Tannous, Aldo Ghazaly, Weam Owayjan, Michel American University of Science & Technology Faculty of Engineering and Computer Science Beriut Lebanon Nantes University Saint-Nazaire France

ISBN: (纸本)9798331529604

Predicting the outcomes of Formula 1 (F1) races presents a significant challenge due to the complex interplay of numerous factors, including driver skill, vehicle performance, team strategy, and unpredictable race-day conditions. This paper investigates the application of the Random Forest algorithm enhanced with SHAP (Shapley Additive Explanations) analysis to forecast race winners while providing interpretability to the model's predictions. By leveraging extensive historical data encompassing driver metrics and race conditions, we aim to build a model that achieves high predictive accuracy and offers valuable insights into the most influential factors affecting race outcomes. Our approach demonstrates how integrating machine learning with explainable AI techniques can assist F1 teams in making data-driven decisions, ultimately optimizing their strategies in the highly competitive environment of professional racing. © 2025 IEEE.

关键词： Random forests

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Machine Learning Technique to Detect Radiations in the Brain

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computer Systems science & engineering 2022年第7期42卷 149-163页

作者： E.Gothai A.Baseera P.Prabu K.Venkatachalam K.Saravanan S.SathishKumar Department of Computer Science and Engineering Kongu Engineering CollegeErode638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Science and Engineering CHRIST(Deemed to be University)BangaloreIndia Department of Computer Science and Engineering Erode Sengunthar Engineering CollegeThudupathi638057 Department of EEE M.Kumarasamy College of EngineeringKarur639113TamilnaduIndia

The brain of humans and other organisms is affected in various ways through the electromagneticﬁeld(EMF)radiations generated by mobile phones and cell phone *** variations in the brain are caused by the neurological changes due to the revelation of *** level analysis is used to measure and detect the effect of mobile radiations,but its utilization seems very expensive,and it is a tedious process,where its analysis requires the preparation of cell *** this regard,this research article proposes optimal broadcast-ing learning to detect changes in brain morphology due to the revelation of ***,Drosophila melanogaster acts as a specimen under the revelation of *** segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of *** geometrical characteristics of the brain image of that is microscopic segmented are *** results reveal the occur-rence of several prejudicial characteristics that can be processed by machine learn-ing *** important prejudicial characteristics are given to four varieties of classiﬁers such as naïve Bayes,artiﬁcial neural network,support vector machine,and unsystematic forest for the classiﬁcation of open or nonopen micro-scopic image of *** *** results are attained through various experimental evaluations,and the said classiﬁers perform well by achieving 96.44%using the prejudicial characteristics chosen by the feature selection *** proposed system is an optimal approach that automatically identiﬁes the effect of revelation of EMF with minimal time complexity,where the machine learning techniques produce an effective framework for image processing.

关键词： Electromagneticﬁeld radiations brain morphology segmentation machine learning image processing

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An overview of thalassemia: A review work

An overview of thalassemia: A review work

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Begum, Ruqqaiya Suryanarayana, G. Rama, B. V. Saketha Swapna, N. Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Tamil Nadu Chennai India Department of Computer Science & Engineering Vijay Rural Engineering College Telangana Nizamabad India

ISBN: (纸本)9781032669663

Thalassemia is a Disease that passes from parents to children through genes. It is a red blood cell disorder caused when the body doesn’t make enough of a protein in the blood called hemoglobin. Three classifications of Thalassemia are Alpha(a), Beta(b), DeltaBeta(db) And Beta thalassemia is the most effective disease compared to other types. Out of 5% of patients 3% comes in Beta Thalassemia. It is first identified in the early 19’s with the symptoms like poor growth, severe anemia, and huge abdominal organs. It is a disease of which most people are deceased due to unawareness, and not getting proper Medication, especially in rural areas as they cannot find symptoms because other disease symptoms differ from thalassemia. Out of 10000 newborns, approximately an average of 4.4 will be affected by thalassemia. Out of 32 districts In India, 10 to 12 thousand children were born with this disease. To date, no research work has shown the exact death rates of thalassemia worldwide. Blood transfusion is the major method used for the prevention of thalassemia-affected patients. Apart from that Bone marrow is another prevention method used for Beta Carriers. In this work, we gathered detailed Information about thalassemia and its types so that people will get aware of this disease. This work provides details of how this disease is getting inherited from parents to children, the depth of the characteristics of every subtype, and the statistics about causes and death rates from this disease. © 2024 The Author(s).

关键词： Blood

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Model-based framework for exploiting sensors of IoT devices using a botnet: a case study with android

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Cyber-Physical Systems 2025年第1期11卷 1-46页

作者： Khaliq, Zubair Khan, Dawood Ashraf Baba, Asif Iqbal Ali, Shahbaz Farooq, Sheikh Umar Department of Computer Science University of Kashmir Jammu and Kashmir Srinagar India AI Thndr Dubai United Arab Emirates Department of Computer Science Tuskegee University TuskegeeAL United States Data Engineering hyke.ai Dubai United Arab Emirates

Botnets have become a severe security threat not only to the Internet but also to the devices connected to it. Factors like the exponential growth of IoT, the COVID-19 pandemic, and the ever-larger number of cybercriminals are incentivising the growth of botnets in this domain. The recent outbreak of botnets like Dark Nexus (derived from Qbot and Mirai), Mukashi, LeetHozer, Hoaxcalls, etc. shows the alarming rate at which this threat is converging. The botnets have attributes that make them an excellent platform for malicious activities in IoT devices used by organisations to safeguard the personal and confidential data of their customers, employees, and business partners. The IoT devices have built-in sensors or actuators that can be exploited to monitor or control the physical environment of the entities connected to them, thereby violating the fundamental concept of privacy-by-design of these devices. In this paper, we design and describe a modular botnet framework for IoT. The framework uses an enhanced centralised architecture associated with a novel ‘Domain Fluxing Technique’. The proposed framework will provide insights into how privacy in IoT devices can be incorporated at design time to check the sensors and actuators in these devices against malicious exploitation, consequently preserving privacy. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of things

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Optimized Local Secret Sharing Techniques for Distributed Blockchain Networks 9

Optimized Local Secret Sharing Techniques for Distributed Bl...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Roobini, M.S. Sindhuja, Chappidi Shravan, Chanda Nair, Manju C. Gowri, M. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai600119 India

ISBN: (纸本)9798350365092

In blockchain systems, transaction details are stored across a distributed ledger, and each peer bears the responsibility of upkeeping an identical replica of this ledger. This project explains the challenges faced by the high storage costs occurred in the traditional blockchain systems, where each peer maintains an identical copy of transaction data. A DSB system tries to tackle the high storage cost issue by incorporating cryptographic techniques. The Distributed Storage Blockchain (DSB) systems, however face communication costs during peer failures, which are often caused by denial-of-service attacks. To overcome these challenges, the DSB approach has been using a LSS approach. This method utilizes a hierarchical privacy scheme comprising a primary global secret node and multiple subordinate local secret nodes. The suggested Distributed Storage Blockchain (DSB) approach, incorporating Local Secret Sharing (LSS), enhances both storage efficiency and reduces recovery communication costs. This approach aims enhancing both the effectiveness of storage and reducing communication costs. During recovery processes. In the case of peer failures and security threats the DSB with LSS approach provides a solution for creating more resilient and resource-efficient blockchain systems. © 2024 IEEE.

关键词： Blockchain

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