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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Ohemu, Monday F. Azeta, Ambrose A. Adeyanju, Ibrahim A. Obasi, Chukwuemeka C. Airforce Institute of Technology Department of Electrical/Electronics Engineering Kaduna Nigeria Namibia University of Science and Technology Department of Software Engineering Namibia Federal University Department of Computer Engineering Oye-Ekiti Nigeria Edo State University Department of Computer Engineering Uzairue Nigeria

ISBN: (纸本)9798350348637

Terrorism poses a significant global threat, necessitating comprehensive security measures to protect people and critical infrastructure. With the increasing frequency of explosive-based attacks, there is a pressing need for advanced detection systems to mitigate potential devastation. This paper presents the development of an area-based explosive trace detection system using deep transfer learning. Leveraging deep learning technology trained on a substantial dataset collected from sensor networks, the proposed model, named Deep Transfer Learning for Explosive Trace Detection (DTLETD), demonstrates remarkable capabilities in classifying explosive gases based on concentrations of Carbon (C), Hydrogen (H), Oxygen (O), and Nitrogen (N). The methodology involves converting the dataset into 2D data using a serial data to image generator, followed by the adaptation of a pre-trained model for transfer learning. The DTLETD model outperformed conventional Convolutional Neural Network (CNN) models, achieving a training time reduction of about 92 seconds compared to 1287 seconds for the CNN model. Additionally, the transfer learning model exhibits faster convergence with nearly zero losses during training and validation, yielding an impressive accuracy of 99.7% and an average AUC value of approximately 0.89. This research significantly advances the field of explosive trace detection by integrating machine learning-based approaches with deep transfer learning techniques, thereby enhancing security protocols and mitigating potential threats. © 2024 IEEE.

关键词： Convolutional neural networks

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A Process Oriented Integration Model for Smart Health Services

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Intelligent Automation & Soft Computing 2023年第2期35卷 1369-1386页

作者： Farzana Kausar Gondal Syed Khuram Shahzad Muhammad Arfan Jaffar Muhammad Waseem Iqbal Department of Computer Science Superior UniversityLahore54600Pakistan Department of Informatics and Systems University of Management and TechnologyLahore54000Pakistan Faculty of Computer Science&IT Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan

Cities are facing challenges of high rise in population number and con-sequently need to be equipped with latest smart services to provide luxuries of life to its *** integrated solutions are also a need to deal with the social and environmental challenges,caused by increasing ***,the development of smart services’integrated network,within a city,is facing the bar-riers including;less efﬁcient collection and sharing of data,along with inadequate collaboration of software and *** to resolve these issues,this paper recommended a solution for a synchronous functionality in the smart services’integration process through modeling *** this integration modeling solution,atﬁrst,the service participants,processes and tasks of smart services are identiﬁed and then standard illustrations are developed for the better understand-ing of the integrated service group *** process modeling and notation(BPMN)language based models are developed and discussed for a devised case study,to test and experiment i.e.,for remote healthcare from a smart *** research is concluded with the integration process model application for the required data sharing among different service *** outcomes of the modeling are better understanding and attaining maximum automation that can be referenced and replicated.

关键词： Process modeling business process business process integration business process modeling and notation

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Cloud-Based AI Solutions for Personal Finance Management

Cloud-Based AI Solutions for Personal Finance Management

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Chhabra, Gurpreet Kaur Mishra, Ashutosh Kumar Tyagi, Varsha Mani, Chandra Tyagi, Vani Sagar, Pramod Kumar Management Education and Research Institute Department of Management Delhi India Srm Business School Department of Management UP Lucknow India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Mangalmay Institute of Engineering & Technology Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331508685

Cloud-based AI answers for private finance control are out-of-the-field tools that leverage synthetic intelligence and cloud computing technologies to manage people's money higher. In this article, I have described various solutions with features ranging from budgeting to personalized financial planning and investment suggestions. Utilizing superior system learning algorithms and cloud-based AI solutions can help understand an individual's fiscal information, including expenses, profits, or savings transactions, for generating personalized insights and advice. This lets people learn about their spending habits and make more informed financial choices. Through the power of the cloud, these solutions can securely store and access financial data from multiple devices, which helps individuals, manage their finances on the go easily. It also enables the client to get real-time updates and notifications, which offers him more comprehensive knowledge regarding his existing financial scenario. These offerings also come with more automation features like reminders to pay payments and computerized enough transfers for a price range, keeping customers from spending further effort and time on their funds. They also offer financial advisors, tax planning tools, and wealth management. © 2025 IEEE.

关键词： Decentralized finance

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Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion

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computers, Materials & Continua 2022年第5期71卷 3557-3570页

作者： Abhishek Kumar Shukla Sujoy Das Department of Mathematics Bio-Informatics and Computer ApplicationsMaulana Azad National Institute of Technology BhopalBhopalMadhya Pradesh462003India

The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining,Natural language processing,Image processing,and Information retrieval *** embedding has been applied by many researchers for Information retrieval *** this paper word embedding-based skip-gram model has been developed for the query expansion *** terms are obtained from the top“k”initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user *** performance of the model based on mean average precision is *** proposed model compares with other existing *** improvement of 6.61%,6.93%,and 9.07%on MAP value is observed compare to the Original query,BM25 model,and query expansion with the Chi-Square model *** proposed model also retrieves 84,25,and 81 additional relevant documents compare to the original query,query expansion with Chi-Square model,and BM25 model respectively and thus improves the recall value *** per query analysis reveals that the proposed model performs well in 30,36,and 30 queries compare to the original query,query expansion with Chi-square model,and BM25 model respectively.

关键词： Information retrieval query expansion word embedding neural network deep neural network

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Electrocardiogram Classification Using Convolutional Neural Network 5th

Electrocardiogram Classification Using Convolutional Neural ...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Kachhadiya, Kashyap Hasmani, Abdullah Gorasiya, Drashti Patel, Bansari Patel, Krishna Vadhavana, Vaishali Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Changa India

ISBN: (纸本)9789819773701

Circulation of Blood relies significantly precise electrical initiation. Arrhythmias can arise from any disturbance in the excitatory wave’s regular pattern in the heart. Electrocardiograms (ECG) are a popular diagnostic tool due to their speed, affordability, and non-invasive nature. This article presents a method for classifying ECGs that makes use of Convolutional Neural Network (CNNs) and the Continuous Wavelet Transform. Using Continuous Wavelet Transform (CWT), ECG signals are broken down into different time–frequency parts, while CNN extracts feature from the resulting 2D-scalogram. To further enhance the model considering the RR interval for arrhythmia diagnosis, 4 RR interval parameters are taken out and connected to CNN parameters to provide an ECG that is completely integrated classifier layer. This proposed model examines the impact of CNN and CWT on ECG classification alongside RR intervals. After training Accuracy of 97.27% was achieved. Based on the proposed method further models can designed for efficient clinical applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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DFU_XAI: A Deep Learning-Based Approach to Diabetic Foot Ulcer Detection Using Feature Explainability

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Biomedical Materials and Devices 2024年第2期2卷 1225-1245页

作者： Biswas, Shuvo Mostafiz, Rafid Paul, Bikash Kumar Uddin, Khandaker Mohammad Mohi Hadi, Md. Abdul Khanom, Fahmida Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Institute of Information Technology Noakhali Science and Technology University Department of Software Engineering Daffodil International University Department of Computer Science and Engineering Dhaka International University Department of Computer Science: Information Technology University of Nebraska Omaha Department of Mathematics American International University – Bangladesh

Diabetic foot ulcer (DFU) is a potentially fatal complication of diabetes. Traditional techniques of DFU analysis and therapy are more time-consuming and costly. Artificial intelligence (AI), particularly deep neural networks, has demonstrated remarkable effectiveness in medical applications. Despite this, the lack of explainability of deep learning models is currently viewed as a key hurdle to using these approaches in actual clinical settings. In this research, we present the DFU_XAI framework for assessing the interpretability of explainable-driven deep learning (DL) models. DFU_XAI evaluates five DL models (Xception, DenseNet121, ResNet50, InceptionV3, and MobileNetV2) to establish a transparent DL framework using three state-of-the-art explanation methods: Shapley additive explanation (SHAP), local interpretable model-agnostic explanations (LIME), and gradient-weighted class activation mapping (Grad-CAM). ResNet50 outperformed the other four models with remarkable results: 98.75% accuracy, 99.2% precision, 97.6% recall, 98.4% F1-score, and 98.5% AUC. For the most part, it can locate diabetic foot ulcers precisely on a diabetic foot and discriminate between diabetic foot ulcers and healthy feet in the DFU dataset. A heat map will indicate the precise location of the ulcer that needs care. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC 2024.

关键词： Convolutional neural networks Deep learning Diabetic foot ulcers Explainable AI

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Advancements in Convolutional Neural Networks for Accurate and Efficient Skin Cancer Classification: A Comprehensive Survey 8th

Advancements in Convolutional Neural Networks for Accurate a...

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Takale, Dattatray G. Kashid, Shraddha S. Deshmukh, Gopal B. Gawali, Piyush P. Mahalle, Parikshit N. Sule, Bipin Deshpande, Arati V. Ghule, Vijaykumar R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India and Humanities Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819766741

The cancer of the skin, particularly melanoma, is considered to be one of the most life-threatening types of cancer. The major cause of skin cancer is an unrepaired deoxyribonucleic acid (DNA) disruption in skin cells which results in heritable skin imperfections or mutations that result from the disruptions. It is very important to note that skin cancers are most curable at their earliest stages, but they tend to spread to multiple parts of the body and appear as lesions. Among the lesion parameters that doctors use to identify and distinguish between non-cancer skin cancer and melanoma skin cancer is similarity, structure, spread, color, etc. These parameters provide the doctors with a valuable tool to diagnose skin cancer early on. Due to the parameter study and significant increase in skin cancer incidence rates, many researchers decided to quickly identify and diagnose skin cancer. Early detection of skin cancer is essential to lowering the mortality rate. Using deep learning and transfer learning models, researchers have been able to detect skin cancer using various algorithms. A recent study demonstrates the capability of deep learning and deep transfer learning methods to classify and detect skin cancer early. The results of this study suggest that a comprehensive investigation into the methods of Deep Learning (DL) algorithms for locating skin cancer at several stages and types is required based on the analysis of the results of this study. An investigation into articles published in reputed journals was carried out with regard to the diagnosis of skin cancer based on the findings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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Analysis of Web Application Vulnerabilities using Dynamic Application Security Testing 9

Analysis of Web Application Vulnerabilities using Dynamic Ap...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Singh, Ravinder Kumar Gupta, Mukesh Patil, Dipak Raghunath Maruti Patil, Sarang Suresh Gyan Vihar University Department of Computer Engineering Jaipur India Suresh Gyan Vihar University Department of Electrical Engineering Jaipur India Avcoe Department of Computer Enginerring Sangamner India Skn Sinhgad Institute of Technology & Science Department of Computer Engineering Lonavala India

ISBN: (纸本)9798350394474

In the era of ubiquitous digital connectivity, securing software applications against cyber threats is paramount. Dynamic Application Security Testing (DAST) has emerged as a crucial methodology in the realm of cybersecurity, providing a dynamic, real-time approach to identify and mitigate vulnerabilities within software applications. This research paper delves into the significance and application of DAST in fortifying cybersecurity measures. DAST represents a departure from traditional static testing methods, which analyze source code or binaries, by assessing applications during runtime. The methodology involves the active, controlled probing of live applications through simulated cyber-attacks, including injection of malicious payloads and emulation of common exploit techniques. By dynamically assessing how applications respond to these simulated threats, DAST offers a real-world evaluation of security posture, facilitating the identification of potential *** research paper analyzed the Dynamic Application Security Testing (DAST) reports for different web applications over the past two years and provide a valuable resource for cybersecurity practitioners, researchers, and organizations striving to adopt effective strategies in safeguarding their digital assets against evolving cyber threats in the current environment. © 2024 IEEE.

关键词： Cybersecurity

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Unified Security Framework for Cloud Applications with IoT Fusion via Decentralized Blockchain 3

Unified Security Framework for Cloud Applications with IoT F...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Mohanraj, B. Kalpana A, M. Priya, M. Abdul Jaleel, D. Karthikeyan A, N. Velusamy, A. Sona College of Technology Department of Information Technology Salem India Government College of Engineering Department of Computer Science Engineering Salem India Knowledge Institute of Technology Department of Information Technology Salem India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Madanapalle India Sri Venkateshwara College of Engineering Department of Cse - Artificial Intelligence Vidyanagar India

ISBN: (纸本)9798350375190

Recent advancements in blockchain-based cloud computing highlight its potential in providing robust data security, integrity, and confidentiality. While cloud computing is increasingly utilized for remote resource access, it presents significant security challenges that necessitate innovative solutions. This research proposes a novel SecureCloudChain IoT (SCC-IoT) architecture, which integrates blockchain technology into cloud-supportive platforms to enhance security measures. The SCC-IoT architecture includes a distributed blockchain approach to ensure data security while maintaining privacy and integrity. This system is designed to be adaptable and scalable, meeting real-world requirements effectively. By promoting a decentralized and efficient environment, SCC-IoT improves the reliability, stability, and load balancing of cloud services. Performance assessments through various experimental conditions have been conducted to evaluate the proposed system. The results demonstrate SCC-IoT's effectiveness in mitigating potential attacks and ensuring secure and reliable cloud computing. This research highlights the significance of integrating blockchain technology into cloud platforms, paving way for more secure and efficient cloud computing solutions. © 2024 IEEE.

关键词： Blockchain

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Financial Time Series Prediction on Apple Stocks Using Machine and Deep Learning Models 4

Financial Time Series Prediction on Apple Stocks Using Machi...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Saini, Agampreet Singh, Nisha Singh, Rahul Kumar Sachan, Manoj Kumar School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Sant Longowal Institute of Engineering and Technology Department of Computer Engineering Punjab Longowal India

ISBN: (纸本)9798350395914

The scope of financial time series prediction has evolved significantly, driven by the surging availability of data and computational advancements. This transition has seen the traditional statistical methods give way to present-day machine learning and deep learning techniques. Despite these strides, challenges continue to exist, with market unpredictability, dimensionality issues, and overfitting in high-frequency data standing as primary obstacles. Newer machine learning models are better than old ones at predicting stocks. When electronic trading and yield optimisation come together, they make these models even better. Deep learning has completely changed machine learning, especially in forecasting financial trends over time. Complex deep learning models lack interpretability, hindering real-world adoption. Considering this we have used two deep learning models which are Long Short-Term Memory and Convolutional Neural Network and trained these on apple stock market data over the years along with one machine learning model i.e. Linear Regression. Then we have compared the accuracy of all the models based on their R-squared Score value. © 2024 IEEE.

关键词： Adversarial machine learning

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