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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

computer Modeling in engineering & Sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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Evaluating the Efficacy of Machine Learning Methods in Phishing Detection: A Comparative Analysis 3

Evaluating the Efficacy of Machine Learning Methods in Phish...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Saraswathi, P. Keerthi, S. Reshmika, K.S. Gourav Pritham, G.R. Prabha, M. Dhiyanesh, B. Velammal College of Engineering and Technology Department of Information Technology Madurai India Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354348

In the field of cybersecurity, the ongoing issue of phishing attacks is still of utmost concern because it puts both individuals and companies at serious risk. In order to prevent these fraudulent efforts and protect sensitive data, it is essential to implement effective detection and mitigation mechanisms. This research undertakes a thorough comparative evaluation of various machine learning models designed for phishing detection. Through a review of the literature on phishing detection techniques, we highlight the need for a methodical comparison analysis to identify the most effective strategies. In order to close this gap, we carefully assess the effectiveness of well-known machine learning methods, including Neural Networks, Gradient Boosting, XGBoost, Decision Trees, Random Forests, Support Vector Machines (SVM), and Logistic Regression. Using datasets from reliable sources, such as the PhishTank open-source service and the University of New Brunswick's dataset, we create a balanced dataset with 5000 authentic URLs and the same amount of phishing URLs. Our approach includes thorough feature extraction and preprocessing steps to guarantee the accuracy and applicability of the dataset. To evaluate each model's effectiveness, key performance indicators such F1-score, precision and recall are used. Our findings highlight subtle variations in the models' functionality and provide insight into their advantages and disadvantages for phishing detection. Additionally, we use visualisation approaches to explain each model's outputs, which offer deeper insights into the operational dynamics of each model. Our results reveal subtle differences in the performance of the models, explaining their various advantages and disadvantages in the field of phishing detection. The resulting comparative study provides useful information about the effectiveness of machine learning techniques, which helps decision-makers in practical deployment settings. © 2024 IEEE.

关键词： Phishing

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Personalised Emotion Detection from Text Using Machine Learning 6th

Personalised Emotion Detection from Text Using Machine Learn...

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6th EAI International Conference on computer Science and engineering on Digital Transformation in Organizations: New Challenges, COMPSE 2022

作者： Bhavya, A.V. Dhanush, R.H. Sangeetha, J. Jose, Arun Cyril Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9783031347498

This research discusses an emotion recognition system, which is an important component of many effective computing technologies for natural language processing, based on open-source platforms with automatic speech recognition (ASR) and text analysis, and which is used for user-based customised sentiment analysis. PocketSphinx as ASR and Word2vec model, K-means clustering, and TfidfVectoriser for text automatic analysis are used to design the proposed framework. Further, the dataset that is used for testing and training the model is from International Survey on Emotion Antecedents and Reactions (ISEAR). This research yields a user-dependent system that will function as a tailored assistant for identifying emotional responses and discovering innovative applications. The suggested model greatly outperforms the prior models, with an efficiency of 81% and an f-measure of 89%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Emotion Recognition

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Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, and YOLOv9: A Study on Performance and Explainability

Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, a...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Subair, Rubiya Benedict, Shajulin Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798331540821

Elephant emotion detection has marked a crucial component owing to the increased casualties due to human-elephant conflicts. The difficulty has increased due to the lack of algorithms that quickly identify emotions with explainable features. This paper proposes a holistic architecture that detects the emotions of elephants using YOLOv5, YOLOv8, and YOLOv9 models, The study aims to integrate explainable AI features such as Eigen-CAM to manifest the training and testing capabilities of models. In addition, we evaluated the capability of the proposed system using labeled datasets that distinguish elephant emotions to angry, happy, and sad. Our experimental results, conducted at the IoT Cloud Research laboratory, revealed training accuracy of 79.3% for YOLOv5, 79.5% for YOLOv8, and 81.1 % for YOLOv9 and test accuracy of 93%, 95%, and 95% for YOLOv5, YOLOv8, and YOLOv9. Additionally, we recorded the impact of resolutions while performing model evaluations. The observations learned from the experiments will benefit AI-assisted machines to detect elephant emotions that can be utilized by mahouts or people living near forest locations. © 2024 IEEE.

关键词： Elephant Emotion Explainable AI Yolo V5 Yolo V8 Yolo V9

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

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International Journal of Crowd Science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： Data quality

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Efficacy analysis of NSGAII and multi-objective particle swarm optimization (MOPSO) in agent based weapon target assignment (WTA) model

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International Journal of information technology (Singapore) 2024年第3期16卷 1347-1356页

作者： Tunga, Harinandan Kar, Samarjit Giri, Debasis Pan, Indrajit Department of Computer Science and Engineering RCC Institute of Information Technology Kolkata India Department of Mathematics National Institute of Technology Durgapur Durgapur India Department of Information Technology Maulana Abul Kalam Azad University of Technology West Bengal Haringhata India Department of Information Technology RCC Institute of Information Technology Kolkata India

Weapon target assignment (WTA) is a classic military problem that deals with assigning weapons to targets to protect responsive assets or destroy hostile ones. Usually, the WTA problem is a multi-objective optimization in nature and is considered an NP-complete problem. In this paper, we propose a new multi-objective problem for WTA. The problem is based on the concept of agent in WTA. An agent helps to fire weapons from a set of weapons and it is associated with certain cost. The multi-objective problem is solved by two algorithms: multi-objective particle swarm optimization (MOPSO) and Non-dominated sorting genetic algorithms (NSGA-II). The proposed model is simulated and it is found to be quite efficient for WTA. A comparative efficiency analysis is performed between multi-objective particle swarm optimization (MOPSO) and Non-dominated sorting genetic algorithms (NSGA-II). In order to choose the best algorithm for this problem, a statistical analysis is utilized to compare the result of the algorithms. We use various performance matrices (GD, IGD, space, spread, HV and MPFE) to measure the performances of the two algorithms. It is found that MOPSO is quite efficient in solving the optimization problem. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Agent Maximum Pareto front error MOPSO Multi-objective optimization NSGA-II Weapon target assignment

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An alternative method for Image Encryption by using Neural Networks 15

An alternative method for Image Encryption by using Neural N...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vollala, Satyanarayana International Institute of Information Technology Department of Computer Science and Engineering Naya Raipur India

ISBN: (纸本)9798350370249

information security has emerged as a requirement and a hot topic in the modern digital world. The world today is surrounded by tonnes of gigabytes of data generated from digital platforms continually streaming through the Internet. In this perspective, computer scientists are strongly pushed to protect information against unscrupulous persons who evolve quickly with technology. The goal of cryptography is to encrypt and decrypt information by using complex mathematical functions and logics. Encryption not only for text data, but image, audio, and video kind of data as well. This research focuses on encryption and decryption of images. Different kinds of image encryption algorithms have been developed upto this point. In each algorithm, pixel permutation and pixel substitution steps are performed by using mathematical functions such as Logistic map, Cubic-logistic map, and Arnold map. Various Image cryptanalysis works has been going on these conventional image encryption algorithms. In this work, we introduce a new method of encrypting and decrypting images without using any conventional steps like substitution and permutation of image pixels. This proposed method uses a deep learning technique, i.e., convolutional autoencoders. This method targets both compression and encryption so that images can travel with much speed and security over the network. © 2024 IEEE.

关键词： Autoencoders Convolutional Neural Networks Cryptography Neural Cryptography

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Design and Implementation of IoT based flood alert monitoring system using microcontroller 8051

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Materials Today: Proceedings 2023年 80卷 2840-2844页

作者： Chaduvula, Kavitha Kranthi kumar, K. Markapudi, Babu Rao Rathna Jyothi, Ch. Assistant Professor Department of Information Technology Gudlavalleru Engineering College Andhraparadesh Gudlavalleru India Professor and Head Department of Information Technology Gudlavalleru Engineering College Andhraparadesh Gudlavalleru India Professor and Head Department of Computer Science and Engineering Gudlavalleru Engineering College Andhraparadesh Gudlavalleru India Associate Professor Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and Technology Andhraparadesh Vijayawada India

In the Internet of Things (IoT) the physical objects network is described as "things" linked to sensors, software and other technologies to connect and share information through the Internet with other devices and systems. The floods lead to large losses of life and property in several countries. However, the absence of sufficient technology results in further death and property losses as a result of flooding in developed countries. This is because of a lack of flood alert systems. The objective of this paper is to monitor the flood situation and provide a text message warning in the event of a threat. The main aim of this paper is to detect rising river water levels at a safe distance away from the railways and to allow the respective authorities to take appropriate measures by means of SMS. A sensor is a system that senses and reacts to certain physical data. The 8051 microcontrollers were produced by Intel in 1981. The microcontroller is 8-bit. It has 40 DIP pins, 4 kb of ROM stores and 128 bytes of RAM and two timers and 16 bits. It is equipped with 2 timers. There are four parallel 8-bit ports that are programmable and adjustable as necessary. The hardware device that uses GSM mobile technology for the provision of an information link with a remote network is a GSM modem or a GSM module. A float switch is a level sensor type, a fluid control system that is used inside a tank. A condenser is a tool that stores electrical energy in an electric field. It's a passive electronic feature with two terminals. A LED is a semiconductor light source that transmits light through the flowing current. Liquid Crystal Display stands for LCD. It is a thin, flat display device used in a variety of electronics. A digital electronic level shifter is a system used in the transformation of signs from one logic or voltage domain to another, also known as logic level shifter or voltage level Translation. A diode is an electronic component with a two-terminal that mainly conducts curre

关键词： Microcontrollers

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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