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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-E'mari, Salam Sanjalawe, Yousef Makhadmeh, Sharif Alqudah, Hamzah University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan American University Of Madaba Faculty Of Information Technology Department Of Cybersecurity Amman Jordan University Of Petra Faculty Of Information Technology Department Of Data Science And Artificial Intelligence Amman Jordan American University Of Madaba Faculty Of Information Technology Department Of Data Science And Artificial Intelligence Amman Jordan

ISBN: (纸本)9798350394962

In the rapidly evolving landscape of cybercrime, ensuring the authenticity and traceability of digital evidence has become paramount. This paper explores the integration of blockchain technology with digital forensics as a novel approach to address these concerns. Blockchain, with its inherent characteristics of immutability and decentralization, can offer significant advancements in ensuring tamper-proof storage and chronological verification of forensic artifacts. By merging these two domains, we introduce a framework that not only bolsters the authenticity of digital evidence but also facilitates a robust traceability mechanism. Through a detailed case study, we demonstrate the practicality and effectiveness of this integrated approach, highlighting its potential to revolutionize current forensic methodologies. However, we also delve into the challenges posed, such as scalability, privacy, and legal implications, and suggest future research avenues. Leveraging blockchain technology in the realm of digital forensics has the potential to redefine evidence integrity and the forensic investigation landscape. © 2024 IEEE.

关键词： Digital forensics

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Attention Span and it's Correlation with Mental Health 1

Attention Span and it's Correlation with Mental Health

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Shah, Richa Bewoor, Laxmi Rak, Sarthak Patne, Ayush Vishwakarma Institute of Information Technology Artificial Intelligence and Data Science Pune India Vishwakarma Institute of Information Technology Computer Engineering Pune India

ISBN: (纸本)9798350355611

This paper delves into the correlation between attention span and mental health. Attention span is the ability to focus on a task before being distracted by certain factors. It ranges from 2 seconds to more than 20 minutes. Meanwhile, mental health constitutes a person's psychological, sociological, and emotional well-being. Using a clustering algorithm, this research seeks to provide a concrete relation between the two across an age group of 18-25. © 2025 IEEE.

关键词： Electronic health record

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Diagnosis of Laryngitis and Cordectomy using Machine Learning with *** and SVD

Diagnosis of Laryngitis and Cordectomy using Machine Learni...

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International Conference on data Analytics and Management, ICDAM 2023

作者： Ali, Syed Irfan Khan, Ahmed Sajjad Ali, Syed Mohammad Nasiruddin, Mohammad Artificial Intelligence and Data Science Engineering Anjuman College of Engineering & Technology Nagpur India Electronics & Telecommunication Engineering Anjuman College of Engineering & Technology Nagpur India

ISBN: (纸本)9789819965434

Nowadays, machine learning is playing an important role in providing automated results to the humanity. It is gaining researchers attention day by day and providing more accurate and fast results in every second research. Additionally, Microsoft's machine learning, an open-source cross-platform, is used in this study. For the classification of speech disorders like Chordektomie and Laryngitis against normal, it is used in a. Net5-based web application. Several experiments were performed with the ML model by training it with different sets of features to evaluate best set of features for accurate result. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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artificial Vision - Yolov8 Algorithm for Objects and Impediments Detection 2

Artificial Vision - Yolov8 Algorithm for Objects and Impedim...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Lakshmi, S. Jothi Sasirekha, S.P. Mekala, N. Reshma, R. Nair, V. Indu Bhagya, S.G. Nehru Institute of Technology Department of Information Technology Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Coimbatore India SNS College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Bapuji Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Davangere India

ISBN: (纸本)9798331534950

This paper presents an object detection method using YOLO technique (You Only Look Once) based deep learning algorithm to help visually impaired people in their daily life. A Cobotic Spectacle is a cutting-edge product designed specifically for visually impaired individuals. The system seamlessly integrates face motion recognition, object identification and text detection functionalities using YOLO Algorithm. The real time object detection method uses YOLO and convolution neural networks (CNN) for single forward propagation from end to end. The proposed system utilizes YOLOv8, a state-of-the-art real-time object detection algorithm, which is trained on the Common Object in Context (COCO) dataset to accurately identify objects in front of a person. With a high degree of accuracy, the system then conveys the identified object information through a synthesized voice output, thereby enhancing visual perception and providing an immersive *** face motion recognition module promotes improved social interaction, while object identification increases environmental awareness. This integrated solution combines edge processing and cloud-based analysis for portability, real-time processing, and computational efficiency. A Cobotic Spectacle empowers visually impaired individuals, providing a state-of-the-art smart spectacle system that enhances visual perception, promotes independence, and improves their overall quality of life. © 2024 IEEE.

关键词： Convolutional neural networks

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Brain Connectivity Using Deep Learning Algorithms 1

Brain Connectivity Using Deep Learning Algorithms

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2023 1st DMIHER International Conference on artificial intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Jiet, Moses Makuei Kamble, Aahash Puri, Chetan Gajanan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798350338423

The integration of deep learning into the examination of brain connectivity marks a transformative era in understanding neural networks. This article provides a concise overview of the burgeoning field of deep learning in brain connectivity analysis. It defines the concept, explores functional and structural connectivity, outlines practical applications, delves into the algorithms involved, compares traditional methods vs advanced deep learning approaches for brain connectivity and highlights its revolutionary impact in clinical practice. In deep learning, brain connectivity investigates neural links using advanced artificial intelligence. Functional connectivity, powered by deep neural networks, reveals patterns in brain activity linked to cognition, emotion, and decision-making. Structural connectivity, often utilizing graph neural networks, reconstructs intricate neural pathways. This technology shows significant progress in diagnosing conditions like Alzheimer's, schizophrenia, and autism. As it evolves, its clinical importance becomes evident, offering early disease detection, tailored treatments, and a deeper understanding of neurological disorders. Beyond clinical applications, deep learning in brain connectivity opens avenues for advancements in brain-computer interfaces, enhanced rehabilitation therapies, and precise interventions. © 2023 IEEE.

关键词： Learning algorithms

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Modeling a Novel Hyper-Parameter Tuned Deep Learning Enabled Malaria Parasite Detection and Classification

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Computers, Materials & Continua 2023年第12期77卷 3289-3304页

作者： Tamal Kumar Kundu Dinesh Kumar Anguraj S.V.Sudha Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAndhra PradeshIndia Department of Artificial Intelligence and Data Science KPR Institute of Engineering and TechnologyCoimbatoreTamil Nadu641407India

A theoretical methodology is suggested for finding the malaria parasites’presence with the help of an intelligent hyper-parameter tuned Deep Learning(DL)based malaria parasite detection and classification(HPTDL-MPDC)in the smear images of human peripheral *** existing approaches fail to predict the malaria parasitic features and reduce the prediction *** trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online *** Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network(DNN)with the help of the contrastive divergence method to *** features are extracted from the images in the proposed system to train the DNN for initializing the visible *** smear images show the concatenated feature to be utilized as the feature vector in the proposed ***,hyper-parameters are used to fine-tune DNN to calculate the class labels’*** suggested system outperforms more modern methodologies with an accuracy of 91%,precision of 89%,recall of 93%and F1-score of 91%.The HPTDL-MPDC has the primary application in detecting the parasite of malaria in the smear images of human peripheral blood.

关键词： Malaria parasite classification hyper-parameter deep neural network the feature vector

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Intelligent Hotel Recommendation System Using Dynamic Graph Learning 3

Intelligent Hotel Recommendation System Using Dynamic Graph ...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kiruthiga, G. Kamalesh, S. Jegadeesan, S. Manojkumar, S. Kavinesh, K.K. Ruban, A.S. Karpagam College of Engineering Department of Artificial Intelligence And Data Science Coimbatore India Velammal College of Engineering and Technology Department of Information Technology Tamilnadu Madurai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Inherently, the framework of Inn Suggestion presents an entirely hybrid approach to making hotel recommendations. The framework encompasses content-based and collaborative filtering techniques into a single system by analyzing user behaviors, including past bookings and reviews, besides features, which are generally associated with hotels, such as location and price, to get relevant suggestions. At the heart of the framework lies the Adaptive Meta-Learning Hybrid algorithm that coordinates several machine learning techniques, like RNN, CNN, KNN, and Node2Vec. This meta-learning layer can recalculate the selection of algorithms dynamically based on performance in targeted subsets of users. The feature affords rapid adaptation to shifts in the user behavior and underlying data distributions, thus significantly enhancing both accuracy and diversity of recommendations over time. Key features include real-time updates, an intuitive user interface, and a robust feedback system. The technologies which architecture is based on including ***, Python, ***, and cloud services, with which scalability and security are the essence of their execution. Introducing these methodologies with AMLH, the Brilliantly Inn Suggestion Framework aims to change the entire experience of booking hotels. Which means a more dynamic personalized experience through booking in respect of each user's needs and overall satisfaction toward travel planning. © 2025 IEEE.

关键词： Collaborative filtering

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Adversarial dual decision-based model for event-related opinion sentence recognition

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Multimedia Tools and Applications 2024年 1-18页

作者： Xiang, Yan Qin, Yuan Yu, Zhengtao Huang, Yuxin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China

Event-related opinion sentences recognition aims to mine the valuable comments discussing about the specific event from the mass of microblogs comments. It’s difficult to label sufficient comments for a new microblogs event, and domain adaptation methods are suitable for this task, which can transfer the knowledge from the source domain(labeled event) to the target domain (unlabeled event). However, existing domain adaptation models do not make full use of the domain invariance to realize categories alignments of the two domains. Therefore, we propose a adversarial dual decision model for opinion sentences recognition, which takes advantage of the domain invariance that the opinion sentences are correlated with the event description. The proposed model has two generators to obtain different event-correlated features, and two classifiers to make dual decisions based on the two features. Through the adversarial process of generators and classifiers, the model completes the category feature alignment and realizes the opinion sentences recognition for the new event. The comparative experiment results show that our model outperforms the state-of-the-art models on six opinion sentences recognition tasks. In addition, we verify the effectiveness of each component of our model by ablation experiments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Adversarial machine learning

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural Computing and Applications 2025年第17期37卷 11583-11605页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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A Hybrid Model for Forecasting Stock Prices Using Bayesian and LSTM 11th

A Hybrid Model for Forecasting Stock Prices Using Bayesian a...

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11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Pinapatruni, Rohini Mohammed, Faizan Mohiuddin, Syed Anas Patel, Dheeraj Department of Data Science and Artificial Intelligence Faculty of Science and Technology ICFAI Foundation for Higher Education Hyderabad India

ISBN: (纸本)9789819967056

Forecasting stock price and intraday direction is the main problem in the area of Quantitative Finance. This paper explores the efficacy of Bayesian Long Short-Term Memory Neural Network Model (to be precise LSTM + BNN) in price forecasting. Performance was tested against ML models like Random Forest, XGBoost, and Vanilla LSTM. Public data on Indian stocks of five companies Reliance, Dr Reddy, Dmart, TCS and Hindunilvr is collected from YahooFinance. Performance of the proposed model is measured using root mean square error and mean absolute error. The model is deployed through a webapp, which displays the predictions for stocks along with statistical metrics such as level of confidence and uncertainty with respect to the predictions. Objective of the proposed research is to create a hybrid model B-LSTM and compare the result with existing models which outperforms with low error rate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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