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International Conference on Advanced Computer Control, ICACC

作者： Aiswarya Mohan Nafla Iqbal Manaal Mashpher Department of Artificial Intelligence and Data Science Rajagiri School of Engineering and Technology Kakkanad India

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379143

These days, a range of issues are being caused by fake news, including satirical pieces, fake news, and intentional government propaganda. Because of the blathering social media discourse, the idea of "fake news" has recently evolved. Wide-ranging consequences of fake news dissemination include the development of distorted opinions and the tampering of election outcomes to benefit specific politicians. Spammers also use captivating news headlines to entice readers to click on their advertisements. These days, it can be quite difficult to tell if the news we hear is accurate. There aren’t many methods for assessing authenticity, and the ones that are accessible are all difficult and beyond the capabilities of the average person. A fact-checking platform that gives us that chance is desperately needed. Deep Learning (DL) approaches are suited for more accurately and efficiently detecting false news because of their recent accomplishments in difficult natural language processing tasks. The suggested approach is a multi-layered, optimal CNN model for detecting fake news. The OPCNN and Word embedding are optimized for feature extraction using the hyperopt optimization method in order to produce highly accurate results. In this study, we generated word embeddings using the FastText technique. The purpose of the model is to identify news stories published in Malayalam that are either false or misleading. The model is trained on 80% of a newly compiled Malayalam dataset consisting of news articles from various online sources having 11,686 news items with 5843 false and real news entries apiece and then evaluated on the remaining 20%. The evaluation’s findings show that the suggested model works better than current methods and has a high degree of accuracy when it comes to identifying false information in Malayalam.

关键词： Visualization Accuracy Social networking (online) Voting Predictive models data models Robustness Convolutional neural networks Fake news Videos

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Predictive Modeling and Analysis of Campus Placements Using Machine Learning Techniques 5

Predictive Modeling and Analysis of Campus Placements Using ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Gaurkhede, Sanskruti Burle, Rushikesh Gudadhe, Amit Chopra, PKP Gaitry Datta Meghe Institute of Higher Education and Research Computer science And Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331530013

A prediction method for campus placements is designed to determine the likelihood that a student will be hired by a firm after completing a campus placement. Numerous parameters are taken into account by the model, which may be used to estimate the student's ability level. Some information is gathered from examinations administered through the placement management system, while other information is gathered from college-level sources such as academic performance, CGPA, pointers, projects, internships, HSC and SSC marks, etc. This placement prediction technique aims to assist students in enhancing their academic performance and outcomes while also helping them to acquire a soft skill that would increase their placement chances. The reputation and annual admissions of the institute are always contingent upon the student placements made by the institute. Thus, in an effort to enhance their organisation as a whole, the majority of schools diligently work to grow their placement department. The goal of this study is to evaluate student placement data from the previous year and use it to calculate the likelihood that current students will be placed on campus. Four distinct machine learning algorithms Logistic Regression, Decision Tree, Support Vector Machine, K Neighbour Classifier, and Random Forest have been tested for this purpose. © 2024 IEEE.

关键词： Self-supervised learning

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Adaptive Attention-Enhanced Transformer With Modified Graph Cuts (AAET-MGC) Algorithm for Cardiovascular Disease Diagnosis 2

Adaptive Attention-Enhanced Transformer With Modified Graph ...

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2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Vaanathi, S. Palanisamy, C. Steephan Amalraj, J. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam Tamilnadu Erode India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam Tamilnadu Erode India Bannari Amman Institute of Technology Department of Computer Science & Engineering Sathyamangalam Tamilnadu Erode India

ISBN: (纸本)9798350368413

One of the core prerequisites in the diagnosis and treatment of cardiovascular diseases is accurate segmentation of the myocardium walls and small vessels in cardiac MRI datasets. Traditional segmentation methodologies often do not tackle this complexity of cardiac structures and their variability across MRI images. This may further enhance overfitting and local minima problems during training of deep learning models for cardiac image segmentation. Against this backdrop, a new hybrid approach is introduced called Adaptive Attention Enhanced Transformer with Modified Graph Cuts. This methodology is devised to merge the local feature extraction using a Convolutional Neural Network with the global context awareness provided by the transformer encoders further enhanced by attention mechanisms;user interest is oriented toward the most relevant features. The AAET-MGC model integrates an adaptive energy function within a modified graph cuts framework with spatial and intensity information, aiming at refining the segmentation results. This hybrid approach uses the power of deep learning in an initial segmentation process and classical graph cuts for accurate and smooth boundary detection. The method is evaluated against a comprehensive cardiac MRI dataset with an accuracy of 97%, avoiding overfitting and local minima. The AAET-MGC approach outperforms competing methods in the accurate segmentation of thin and complex structures, conductive to better diagnostic accuracy for the related clinical applications. © 2024 IEEE.

关键词： Image segmentation

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The Necessary Requirements for Utilizing AI in data Access Setting 15

The Necessary Requirements for Utilizing AI in Data Access S...

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

作者： Swapnil, K. Sharma, Bhuvnesh Singh, Manpreet Yadav, Rakesh Kumar Devaraj Verma, C. Umarani, K. Vishwakarma Institute of Technology Department of Artificial Intelligence &data Science Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi University of Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

AI has been an increasing number of adopted in records get admission to settings to improve the overall performance of statistics get admission to and extract significant insights, using extended productiveness and value-effectiveness. However, the success implementation of AI calls for positive essential requirements to be met, together with facts accuracy, connectivity, security, and scalability. First, facts accuracy is critical for AI applications in facts get entry to settings. Information gathered need to be of excessive quality and accuracy, as AI working on erroneous facts can yield unreliable and probably harmful effects. Records should be of suitable granularity, taking pictures the most relevant elements, to make certain a complete and steady training performance.2nd, linked statistics assets, networks, and services are had to leverage the capability of AI in information get entry to settings. This gives an interconnected and up to date dataset at scale, while requiring included answers for uninterrupted get entry to. Moreover, single sign-on and alertness orchestration abilities are vital to decorate user revel in. © 2024 IEEE.

关键词： Connectivity Gathered Increasing Productiveness Security

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A Quantum Algorithm for Evaluating the Hamming Distance

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Computers, Materials & Continua 2022年第4期71卷 1065-1078页

作者： Mohammed Zidan Manal G.Eldin Mahmoud Y.Shams Mohamed Tolan Ayman Abd-Elhamed Mahmoud Abdel-Aty Department of Artificial Intelligence Hurghada Faculty of Computers and Artificial IntelligenceSouth Valley UniversityEgypt Faculty of Engineering King Salman International UniversitySouth SinaiEgypt Department of Mathematics and Computer Science Faculty of ScienceBeni-Suef UniversityBeni-SuefEgypt Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityEgypt Mechanical Department Faculty of Technology and EducationSuez UniversitySuezEgypt Faculty of Technological Industries King Salman International UniversitySouth SinaiEgypt Faculty of Engineering at Mataria Helwan UniversityCairoEgypt Department of Mathematics Faculty of SciencesSohag University82524 SohagEgypt

We present a novel quantum algorithm to evaluate the hamming distance between two unknown oracles via measuring the degree of entanglement between two ancillary *** particular,we use the power of the entanglement degree based quantum computing model that preserves at most the locality of interactions within the quantum model *** model uses one of two techniques to retrieve the solution of a quantum computing problem at *** the first technique,the solution of the problem is obtained based on whether there is an entanglement between the two ancillary qubits or *** the second,the solution of the quantum computing problem is obtained as a function in the concurrence value,and the number of states that can be generated from the Boolean *** proposed algorithm receives two oracles,each oracle represents an unknown Boolean function,then it measures the hamming distance between these two *** hamming distance is evaluated based on the second *** is shown that the proposed algorithm provides exponential speedup compared with the classical counterpart for Boolean functions that have large numbers of Boolean *** proposed algorithm is explained via a case ***,employing recently developed experimental techniques,the proposed algorithm has been verified using IBM’s quantum computer simulator.

关键词： Quantum computing quantum algorithm quantum circuit

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IoT-Enabled Precision Irrigation for Coconut Plantations: Enhancing Growth and Sustainability 8

IoT-Enabled Precision Irrigation for Coconut Plantations: En...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Reddy, M.Jaganmohan Ramu, V. Grace Shalini, T. Vincysahaba, R. Devaki, M. Athiraja, A. Telangana Secunderabad India Telangana Hanamkonda India Srm Institute of Science and Technology Kattankulathur School of Computing Department of Computational Intelligence Chengalpattu India St.Joseph College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Velammal College of Engineering and Technology Department of Eee Tamil Nadu Madurai India

ISBN: (纸本)9798350367904

In agriculture, precision irrigation plays a pivotal role in optimizing crop growth and resource utilization. In this study, must proposean Internet of Things based irrigation remote monitoring system tailored for enhancing coconut plant growth. The system integrates sensor technology, Internet of Things connectivity, and data analytics to provide real-time monitoring and control of irrigation parameters such as soilmoisture, temperature, and humidity. Leveraging a network of sensors deployed in the coconut plantation, the system collects and analyzes environmental data to optimize irrigation schedules and ensure optimal growing conditions for coconut plants. Through a user-friendly interface accessible via web or mobile applications, farmers can remotely monitor the status of their plantation and adjust irrigation settings as ***, the system incorporates predictive analytics capabilities to anticipate irrigation needs based on historical data and weather forecasts, thereby enhancing water efficiency and crop productivity. Field trials demonstrate the effectiveness of this Internet of Things based irrigation system in improving coconut plant growth while reducing water consumption and operational costs. This findings underscore the potential of Internet of Things technologies in revolutionizing agricultural practices, facilitating sustainable crop management, and enhancing food security in coconut-producing regions. © 2024 IEEE.

关键词： Precision agriculture

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Overview of Challenges Faced by Digital Forensic 2

Overview of Challenges Faced by Digital Forensic

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Fakhouri, Hussam N. Alsharaiah, Mohammad A. Al Hwaitat, Ahmad K. Alkalaileh, Mohannad Dweikat, Fawzi Fayez University Of Petra Faculty Of Information Technology Data Science And Artificial Intelligence Department Amman Jordan Al-Ahliyya Amman University Data Science And Artificial Intelligence Department Amman Jordan The University Of Jordan Amman Amman Jordan College Of Education Al Ain University Humanities And Social Sciences Al-Ain United Arab Emirates Hamdan Bin Mohammed Smart University Chancellor Office United Arab Emirates

ISBN: (纸本)9798350394962

Digital forensics, a field pivotal in the scientific examination and analysis of digital device data, has escalated in relevance as technology becomes ubiquitous. This research offers a detailed examination of the multifarious challenges confronting forensic practitioners, categorized into technical, procedural, organizational, and human aspects. Employing a multifaceted approach that includes a comprehensive literature review and case study analysis, this study elucidates the dynamic and evolving nature of these obstacles. Technical challenges, including the overwhelming data volumes and the complexities associated with IoT and mobile device forensics, are shown to significantly hinder effective data extraction and analysis. Procedural challenges are characterized by issues in standardization, inter-agency collaboration, and legal ambiguities, while organizational challenges encompass budgetary constraints, the need for reliable tool validation, and maintaining an unbroken chain of custody. Human factors also introduce significant variability and potential for error in forensic investigations. The study's findings indicate that these challenges are interrelated and require a cohesive approach to address. It concludes by proposing a series of integrated solutions and best practices aimed at mitigating these challenges. These include the adoption of advanced analytical tools, the development of universal procedural standards, enhanced training and resources for forensic practitioners, and a greater emphasis on ethical considerations. © 2024 IEEE.

关键词： Cybersecurity

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Advancing Alzheimer's Nodule Detection Through a Comprehensive Multi-Scene Deep Learning Framework 10

Advancing Alzheimer's Nodule Detection Through a Comprehensi...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Premkumar, C. Rajeshwari, R. Remya, R. Cholaathiraj, M.S. Reethika, A. Bharathidasan, S. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr Sagunthala R&d institute of science and Technology Department of Electronics and Communication Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350384369

Determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAD modules, including MRI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene Deep Learning Framework (MSDLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSDLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Shared Encoding and Representation of Tool-Mediated and Direct Touch Via Superposition Mechanisms

Shared Encoding and Representation of Tool-Mediated and Dire...

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2025 IEEE Symposium on Computational intelligence in artificial Life and Cooperative Intelligent Systems, ALIFE-CIS 2025

作者： Kasashima, Haruna Noguchi, Wataru Tamura, Yasumasa Yamamoto, Masahito Iizuka, Hiroyuki Graduate School of Information Science and Technology Hokkaido University Japan Hokkaido University Education and Research Center for Mathematical and Data Science Japan Hokkaido University Faculty of Information Science and Technology Japan Hokkaido University Center for Human Nature Artificial Intelligence and Neuroscience Japan

ISBN: (纸本)9798331508371

This study proposes a multimodal predictive neural network model that simulates tool-mediated object perception, addressing the neurologically observed phenomenon where subjective experiences during tool use are similar to direct tactile perception. To explain this phenomenon, we apply a superposition mechanism that processes experiences from both tool use and non-tool use scenarios indistinguishably. Our model processes visual, tactile, and proprioceptive inputs from a simulated robotic arm using an encoder-decoder architecture with a shared Long Short-Term Memory (LSTM) module. Trained on long-term sensorimotor data collected from robot movements during tool use and non-tool use, the network demonstrates the ability to predict visual and tactile sensations even when certain sensory inputs are masked. Notably, our results reveal that tactile stimuli in similar situations are processed uniformly, regardless of tool use. When the model's internal state during tool use is employed to simulate non-tool experiences, it predicts as if the tool were an extension of the arm itself. This suggests that the model has incorporated the tool as an integral part of its body representation, mirroring the phenomenon of tool embodiment observed in humans. © 2025 IEEE.

关键词： Robotic arms

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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical data

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Computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams 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 Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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