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A Quantum Algorithm for Evaluating the Hamming Distance

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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Liver Tumor detection and segmentation using Cascaded FCNN

Liver Tumor detection and segmentation using Cascaded FCNN

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2024 International Conference on Computational intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Valarmathy, G. Rashme, M. Umapathi, N. Saai Srivathsan, R. Sri Dhivya Krishnan, K. Chennai India Sri Sairam Institute of Technology Department of Computer and Communication Chennai India Jyothishmathi Institute of Technology and Science Department of Electronics and Communication Karimnagar India Sri Sairam Engineering College Department of Artificial Intelligent & Machine Learning Chennai India

ISBN: (纸本)9798350381887

One of the leading causes of death in humans is liver tumors. Ignorance at an early age leads to serious health issues and may even be fatal. The earlier a tumor is identified and treated, the lower the chance of death. Manual procedures require a lot of time to find tumors. Even if there are numerous approaches for this procedure, automatic tumor detection will solve this issue, yet it still has certain limitations, such as inaccurately identifying larger cells and not being able to detect tumors in their early stages. This paper presents a deep learning approach to the identification and segmentation of liver cancer. To prevent these limitations, a fully Cascaded Convolutional Neural Network (FC-CNN) is proposed. From this FCCNN technique will improve the accuracy to 84% and the time consumption also was reduced. © 2024 IEEE.

关键词： Convolutional neural networks

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Harnessing NLP and Deep learning to Decode Disease–Gene Interactions in Lung Cancer

Harnessing NLP and Deep Learning to Decode Disease–Gene Int...

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Women in Computing (InCoWoCo), International Conference for

作者： K. Mary Sudha Rani V. Kamakshi Prasad Department of Artificial Intelligence and Machine Learning Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Hyderabad Hyderabad India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Lung cancer remains a critical global health challenge, necessitating advanced predictive models to enhance diagnostic and prognostic accuracy. This study employs natural language processing (NLP) and deep learning to advance personalized lung cancer prognosis and treatment response prediction. Using a synthetic dataset, our novel neural network model integrates clinical data with patient narratives to extract meaningful insights, correlating patient transcripts with existing datasets. The model interprets lung cancer histology types, gene sets, and genetic mutations, achieving 85% accuracy in predicting treatment responses, with a sensitivity of 0.88 and specificity of 0.83. By merging clinical and transcript-based data, this research provides clinicians with a reliable tool to support personalized medicine and informed decision-making. The results highlight the potential of AI-driven approaches to improve disease-gene association analysis, paving the way for innovative computational methodologies in lung cancer research.

关键词： Deep learning Ethics Accuracy Neural networks Genetic mutations Lung cancer Predictive models Natural language processing Data models Synthetic data

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Vehicle Type Detection and Classification Using Improved Relief Feature Selection Algorithm and Bidirectional Gated Recurrent Unit 2

Vehicle Type Detection and Classification Using Improved Rel...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Monica, K.M. Al-Farouni, Mohammed Madhura, G.K. Ghamya, K. Geethalakshmi, M. Vellore Institute Of Technology School Of Computer Science And Engineering Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Artificial Intelligence And Machine Learning Bengaluru India Tirupati India Kongunadu College Of Engineering And Technology Department Of Electronics And Communication Engineering Trichy India

ISBN: (纸本)9798350381665

Nowadays, Traffic density estimation can be utilized to control traffic light signals for managing the traffic effectively. Vehicle Type Classification (VTC) is used in this traffic density. Because of the factors like minimal interclass variance and maximum intraclass variation, the VTC is challenging which is handled by proposed Improved Relief Feature Selection Algorithm and Bi-directional Gated Recurrent Unit (IRFSA-Bi-GRU) method. Bi-directional Gated Recurrent Unit (Bi-GRU) model is proposed for enhancing Vehicle Type Classification (VTC) within Intelligent Transport Systems. Multiview In Out Traffic Complexity Database (MIOTCD) is used for data collection then a camera response model is used for enhancing visual data quality. Kalman filtering and Gaussian mixture model techniques are utilized for vehicle recognition and tracking. Local Binary Pattern (LBP), Steerable Pyramid Transform (SPT), and Dual Tree Complex Wavelet Transform (DTCWT) are used for feature extraction and Improved Relief Feature Selection Algorithm (IRFSA) is used for feature selection. Finally, the Bi-directional Gated Recurrent Unit (Bi-GRU) model is used for classifying 11 vehicle categories on MIO-TCD. The obtained results exposed that the IRFSA-BiGRU method outperformed the existing methods by achieving an accuracy of 99.02%, 98.34% F-score, 98.78% MCC,99.93% specificity, 99.32% NPV, 98.75% precision, 98.14% recall on the MIO-TCD database. © 2024 IEEE.

关键词： Classification (of information)

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Optimizing Medical Image Classification: Leveraging Advanced Segmentation Models for Enhanced Object Detection in Real-World Scenarios

Optimizing Medical Image Classification: Leveraging Advanced...

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Future Telecommunications and artificial intelligence (IC-FTAI), International Conference on

作者： Nore Elrashidy Nourhan Mohamed Elsabawy Alaa Ebraheem Elnakeeb Alaa Hussien Mai Saeed Menisy Abdelkareem Elkhateb Shaker El Sappagh Dept. Machine Learning and Information Retrieval Faculty of Artificial Intelligence Kaferelshikh University Kaferelshikh Egypt Dept. Biological Artificial Intelligence Faculty of Artificial Intelligence Kaferelshikh University Kaferelshikh Egypt Computer Engineering Galala University Suez Egypt

ISBN: (数字)9798331534080

ISBN: (纸本)9798331534097

In medical imaging, classifiers typically rely on objects of interest, such as nodules, being centered and fully visible within the frame to effectively learn the underlying relationships necessary for accurate classification. However, in real-world scenarios, such conditions are not always met, leading to challenges in classification accuracy. This paper explores the enhancement of classifier performance by incorporating advanced segmentation models, including SAM, SAM2, and Dinov2, all of which are pretrained for automatic semantic segmentation. We compare the results of integrating these modelsto preprocess and segment medical images, thereby improving the classifier's ability to correctly identify and classify objectsin non-ideal conditions. The study aims to demonstrate how leveraging state-of-the-art segmentation techniques can bridge the gap between idealized assumptions and real-world medical data, ultimately leading to more robust and accurate classificationoutcomes.

关键词： Accuracy Semantic segmentation Object detection Telecommunications Object recognition artificial intelligence Biomedical imaging Image classification

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Bottom-Up Cracks Detection in Road Pavement in artificial Neural Network 1

Bottom-Up Cracks Detection in Road Pavement in Artificial Ne...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Sunil Kumar, V. Alzubaidi, Laith H. Ramesh, G.P. Sreenath, M.V. Gayathri, T. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India The Islamic University of Babylon Department of Computers Techniques Babylon Iraq Department of Electronics and Communication Engineering Chennai India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bangalore India Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350352931

In recent years, road crack detection is a significant task for detecting surface defects, road safety and road maintenance. But there is lack of approach that able to detect the concealed cracks especially bottom-up cracks, and this is challenging due to poor continuity of cracks, various crack's widths, and low contrast among cracks and the surrounding pavement. The artificial Neural Network (ANN) algorithm is proposed for detecting and classifying the structural health status of bottom-up crack in road pavements, which allows detecting surface defects and failures. The approach aims at collection of these signatures by utilizing acoustic-sensors, located at roadside, and also classification of SHS in pavements over the ANN approaches. The various machine learning (ML) classifiers such as Multilayer Perceptron (MLP), 2-Dimensional Convolutional Neural Network (2D-CNN), Support Vector Classifier (SVC) and Random Forest Classifier (RFC) are utilized as well as compared for the analysis of crack in road pavement. The experimental results illustrate the probability of associating with great accuracy 92.7 %of MLP, 96.7 %of 2D-CNN, 92.5 %of RFC and 9 9. 3 %of SVC respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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A Reinforcement learning Method in Cooperative Multi-Agent System for Production Control System 2

A Reinforcement Learning Method in Cooperative Multi-Agent S...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Malathy, V. Al-Jawahry, Hassan M. Madhura, G.K. Suganya, G. Rashmi, P. Sr University Department of Ece Warangal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Dr. Sns Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore India Inventuriz Labs Private Ltd Bengaluru India

ISBN: (纸本)9798350373110

Nowadays, in various domains including distributed control, telecommunication, robotics and economics to address the problems, Multi-Agent Systems (MAS) is used. To solve with preprogrammed agent behavior complexity of many tasks of these domains making it difficult. The multi- agent systems can limit problem of control in complex system production, to solve more efficiently. However, local optimization tendencies are often shown in iterative development algorithms. So, by using reinforcement learning and considering indicators of global key performance this paper presents a new method for cooperative multi-agent system. To cooperative order agents, for the purpose of this a central deep and double deep learning module its knowledge is being transferred. From the results, subsequent reinforcement learning using memory, the order's experience is stored and increases 50% of average mean cycle. In comparison of production control conventional methods, nonlinear production system and parameters of learning interdependencies between the characteristics are investigated and the performance is evaluated. © 2024 IEEE.

关键词： Deep reinforcement learning

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machine learning and Deep learning Approaches for Automated Diabetic Retinopathy Diagnosis

Machine Learning and Deep Learning Approaches for Automated ...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Bhushan Fulkar Rushikesh Burle Pawan Patil Sanskruti Gaurkhade Swapnil Gundewar Utkarsha Pacharaney Department of Artificial Intelligence and Data science Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research(DU) Wardha Maharashtra Department of Computer Science and Design Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Since diabetic retinopathy (DR) is the primary cause of blindness in working-age adults worldwide, vision loss must be avoided by receiving early detection and treatment. In recent years, deep learning techniques have become more and more useful tools for automated drug discovery, possibly offering solutions to overcome the limitations of traditional screening methods. In-depth descriptions of the most recent deep learning methods for DR detection are provided in this review article. We begin by discussing the importance of DR screening, its prevalence, and the challenges faced by healthcare systems in managing this condition. Next, we discuss different deep learning architectures used for DR detection, such as CNNs and RNNs, and their combinations. We investigate how to improve the precision, resilience, and interpretability of DR detection models through the use of explainable AI, ensemble methods, transfer learning, and attention mechanisms. We also examine publicly available datasets, evaluation metrics, and preprocessing techniques that are commonly used to benchmark DR detection algorithms. We discuss the limitations and possible uses of deep learning-based deep readout detection, emphasizing the significance of large-scale, diverse datasets, the interpretability of the model, and real-world deployment concerns. The overall objectives of this review are to advance the field of automated DR detection by highlighting recent developments, compiling existing knowledge, and identifying areas that require further research.

关键词： Deep learning Diabetic retinopathy Reviews Transfer learning Imaging Transforms Medical services

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Dynamic Neural Style Transfer for Artistic Image Generation using VGG19

arXiv

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arXiv 2025年

作者： Kashyap, Kapil Garg, Mehak Fargose, Sean Nair, Sindhu Department of Computer Engineering Dwarkadas. Jivanlal Sanghvi College of Engineering India Department of Artificial Intelligence and Machine Learning Manipal University Jaipur India

Throughout history, humans have created remarkable works of art, but artificial intelligence has only recently started to make strides in generating visually compelling art. Breakthroughs in the past few years have focused on using convolutional neural networks (CNNs) to separate and manipulate the content and style of images, applying texture synthesis techniques. Nevertheless, a number of current techniques continue to encounter obstacles, including lengthy processing times, restricted choices of style images, and the inability to modify the weight ratio of styles. We proposed a neural style transfer system that can add various artistic styles to a desired image to address these constraints allowing flexible adjustments to style weight ratios and reducing processing time. The system uses the VGG19 model for feature extraction, ensuring high-quality, flexible stylization without compromising content integrity. © 2025, CC BY.

关键词： Convolutional neural networks

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A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics

arXiv

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arXiv 2024年

作者： Bode, Jonas Pätzold, Bastian Memmesheimer, Raphael Behnke, Sven The Autonomous Intelligent Systems group Computer Science Institute VI – Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Germany

Recent advances in Large Language Models (LLMs) have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLMs to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models. We make our code, including all prompts, available at https://***/AIS-Bonn/Prompt_Engineering. Copyright © 2024, The Authors. All rights reserved.

关键词： Human robot interaction

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