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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Nikpey, Hesam Sarkar, Saswati Bidokhti, Shirin Saeedi University of Pennsylvania Department of Computer and Information Science United States University of Pennsylvania Department of Electrical and Systems Engineering United States

ISBN: (纸本)9781665475549

With the growth of big data in the past few decades, compression has become inseparable from data generation. The data generated daily across different platforms are correlated: friend networks on Facebook and Instagram, contact networks in subsequent days, and many more. This raises the question of compressing a dataset using another correlated dataset. For instance, can we compress the Facebook graph of friends when we know Instagram's graph? This can be cast as the classical problem of source coding with side information, and the answer is known to be positive when the graphs are "labeled"and/or aligned, meaning we need to know the node corresponding to Jon Doe in both Facebook and Instagram graphs. The classical idea is to utilize joint typicality to decide whether two graphs are correlated or not. In practice, graphs are often not aligned and/or the labels are concealed to keep the identity of the users private. In these scenarios, classical ideas are no longer applicable as joint typicality highly depends on the ordering of sequences. In this work, we prove for the first time the existence of lossless graph compression schemes that utilize unlabeled side information and improve the compression rate. In order to do that, we design binning along with a novel testing criterion that relies on graph matching, the closely related quadratic assignment problem and its asymptotic properties. © 2023 IEEE.

关键词： Combinatorial optimization

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Analysis of Life Expectancy Rate Post Lung Surgery Using Machine Learning 3

Analysis of Life Expectancy Rate Post Lung Surgery Using Mac...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Shreya Jain, Tarun Sharma, Vijay Shankar Manipal University Jaipur Department of Computer Science and Engineering India Manipal University Jaipur Department of Iot & Intelligent Systems India

ISBN: (纸本)9798350319125

It has become really important for patients to know the precise results that can happen post the treatment of lung cancer or that if they have any underlying health problem which can be the cause of death post lung cancer surgery. Doctors also need to know the full detailed report of patients' health with the risk factors that can affect their survival rate for better treatment of their patients. As such results are crucial, we need to implement such techniques which can generate the most accurate results. For such accurate analysis, we use machine learning techniques to generate better results. Several machine learning and deep learning approaches are being used to estimate the survival rate of lung cancer patients post the surgery. Examination of various health factors is used for prediction, as health factors could be significant predictors. Seven machine learning techniques that are linear regression, XGBooster, random forests, Decision Tree, KNN, SVM, voting classifier and two deep learning techniques that are ANN and BILSTM are used for analyzing performance. Analysis of accuracy has been done using F1 score, precision, and recall as measuring factors. For the analysis of outcome prediction, we have taken accuracy. © 2023 IEEE.

关键词： ANN Decision Tree Machine learning Random Forest Thoracic Surgery

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Automatic Diagnosis of Quantum Software Bug-Fix Motifs 35

Automatic Diagnosis of Quantum Software Bug-Fix Motifs

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35th International Conference on Software engineering and Knowledge engineering, SEKE 2023

作者： Kher, Krishn V. Chandra, M. Bharat Joshi, Ishan Zhang, Lei Rao, M.V. Panduranga Department of Computer Science and Engineering IIT Hyderabad India Department of Information Systems University of Maryland Baltimore County United States

Bug-fix pattern detection has been investigated in the past in the context of classical software. However, while quantum software is developing rapidly, the literature is still lacking automated methods and tools to identify, analyze, and detect bug-fix patterns. To the best of our knowledge, our work is the first to leverage classical techniques to detect bug-fix patterns in quantum code. In this paper, we propose an automated framework, called Q-Diff, for detecting bug-fix patterns in IBM Qiskit quantum code. In the framework, we develop a proof-of-concept tool based on Abstract Syntax Trees. To validate our method, we test Q-Diff with a variety of quantum bug-fix patterns using examples. We hope our work will attract the attention of the quantum software engineering community to improve the quality of quantum software. © 2023 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Software engineering

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Enhancing Accessibility for Visually Impaired on Integrating Object, Distance and Face Detection 8

Enhancing Accessibility for Visually Impaired on Integrating...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Selvarathi, C. Kannan, R. Muthumanickam, K. Pandiaraja, P. Venkateswaran, T.S. Arunraj, R. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Saranathan College of Engineering Department of Computer Science and Business Systems Tamilnadu Tiruchirapalli620 012 India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy621215 India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamilnadu Karur639113 India K. Ramakrishnan college of Engineering Department of Information Technology Tamilnadu Samayapuram621112 India

ISBN: (纸本)9798350376425

This research presents a novel assistive technology system designed to empower visually impaired individuals by providing enhanced object, facial, and distance detection capabilities. The system aims to replace traditional electronic travel aids with a more comprehensive and cost-effective solution. By combining object and facial recognition with distance detection, the system enables users to navigate their surroundings independently and confidently. Additionally, the system incorporates threat detection capabilities, alerting users to potential dangers and triggering SOS alerts to contacts or authorities. This innovative system offers a more reliable and accessible solution for visually impaired individuals, providing them with the freedom and independence to navigate their daily lives with greater confidence and security. © 2024 IEEE.

关键词： Assistive technology

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Particle Swarm Optimization for dietary recommendations

Particle Swarm Optimization for dietary recommendations

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Narendra, Vibha Christopher, Jabez Arunachalam, Vasan BITS Pilani Department of Computer Science and Information Systems Telangana India BITS Pilani Department of Civil Engineering Telangana India

ISBN: (纸本)9798350331790

Recommender systems find relevant information that is correctly tailored to the consumers' preferences. Diet recommender systems provide alternative food recommendations that not only satisfy the user's interests but also adhere to the best dietary practices. This study introduces an adaptive meal recommendation system that provides consumers tailored, nutrientdense items that take into consideration both their preferences and needs. The proposed system uses a swarm intelligence based approach to generate alternate food recommendations for food that the user wishes to substitute in their diet, ensuring that their nutritional requirements are met and their behaviour in selecting food displays an upward trend. A recommendation is modelled as a particle in the swarm and the nutrient constituents are the dimensions. The binary particle swarm algorithm generates a set of solutions and over the iterations improves the global best solution which is the recommended alternative food item. The local and global acceleration coefficients are maintained as constants;the sigmoid function is used to update the position of each particle. The goodness of the recommendations of the system was evaluated by conducting surveys with clinicians and other users too. The recommendations of the system had 21.93 units higher fitness score compared to the recommendations of the clinicians. The system can be used by clinicians, dieticians, and also general users who seek alternative food for a food item. © 2023 IEEE.

关键词： Nutrition

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Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

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computers, Materials & Continua 2023年第2期74卷 3151-3166页

作者： Marwa Obayya Nadhem NEMRI Lubna A.Alharbi Mohamed K.Nour Mrim M.Alnfiai Mohammed Abdullah Al-Hagery Nermin M.Salem Mesfer Al Duhayyim Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science and Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversitySaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has *** evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based *** Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in *** to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from *** such scenario computer-assisted automated diagnostic tools are important for classification of ECG *** current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)*** accomplish this,the proposed IBADL-BECGC model initially pre-processes the input ***,IBADL-BECGC model applies NasNet model to derive the features from test ECG *** addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet ***,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification *** presented IBADL-BECGC model was experimentally validated utilizing benchmark *** comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

关键词： Data science ECG signals improved bat algorithm deep learning biomedical data data classification machine learning

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computer-Aided Potato Disease Detection by Using Deep Learning Techniques

Computer-Aided Potato Disease Detection by Using Deep Learni...

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International Conference on Green Energy, Computing, and Intelligent Technology, GEn-CITy 2023

作者： Razaq, Fareeha Bilal, Muhammad Ramzan, Muhammad Naveed, Muhammad Razzaq, Samreen Department of Computer Science University of Sargodha Sargodha Pakistan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (纸本)9789819998326

Potato is the most widely grown and consumed food throughout the world. There are a number of potato crop diseases that affect production, and these diseases differ in symptoms, circumstances, and controls. Early detection and recognition of disease information can aid in disease prevention and production. This paper presents deep learning models using proposed CNN and pre-trained models for potato disease detection and classification. The proposed model is more efficient and accurate at detection and classification. To perform classification, we used two datasets: PLD and PlantVillage. The Xception CNN model serves as the foundation for our proposed model. It achieved 1.00 accuracy, 0.99 precision, 1.00 recall, and 0.99 F1-score on the PLD dataset. On the PlantVillage dataset, it achieved 1.00 accuracy, 1.00 precision, 1.00 recall, and 1.00 F1-score. We also compared the results of Inception-ResNet-V2, MobileNet-V2, VGG-19, Inception-V3, and Xception models with the performance of our proposed model. For three classes of potato leaves, the proposed CNN model produced more accurate results than other pre-trained models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Multiple Vehicles Detection and Tracking Using Ground and Aerial Images for Vehicular Management System 10th

Multiple Vehicles Detection and Tracking Using Ground and ...

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10th International Conference on Future Data and Security engineering, FDSE 2023

作者： Ali, Sardar Jaffar Omer, Muhammad Raza, Syed Muhammad Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Seoul Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9789819982950

Vehicle Management systems (VMSs) are crucial in location-based services for optimizing transportation efficiency, traffic flow, and overall mobility for individuals and businesses. To further enhance their performance, smart VMSs must leverage existing traffic data it gathers by detecting and tracking vehicles periodically. However, despite recent advancements in traffic monitoring, there remains a need for an efficient and up-to-date system, especially in dense traffic or situations requiring aerial monitoring. This paper focuses on the importance of detecting and tracking multiple vehicles in such systems, presenting a two-way validation architecture that integrates CCTV and drone images to enhance accuracy and authenticity. The architecture utilizes the YOLO pretrained model for CCTV images, which is subsequently fine-tuned for drone images. Results demonstrate an accuracy of 99% and a mean average precision of up to 97%. Lastly, the proposed architecture is used for two real-time applications of vehicle counter and lane detection which shows its validity and ability in aforementioned situations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Drones

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Enhanced Capuchin Search Algorithm Using Cooperative Island Model with Application of Evolutionary Feedforward Neural Networks

Enhanced Capuchin Search Algorithm Using Cooperative Island ...

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2023 International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2023

作者： Thaher, Thaer Awad, Mohammed Sheta, Alaa Aldasht, Mohammed Arab American University Department of Computer Systems Engineering Jenin Palestine Southern Connecticut State University Computer Science Department CT06515 United States Palestine Polytechnic University Department of Computer Engineering Hebron Palestine

ISBN: (纸本)9798350339291

This paper introduces an enhanced version of the Capuchin Search Algorithm (CapSA) called ECapSA. CapSA draws inspiration from the collective intelligence of Capuchin monkeys and has shown success in solving real-world problems. However, it may encounter challenges handling complex optimization tasks, such as premature convergence or being trapped in local optima. ECapSA employs a local escaping mechanism operating the abandonment limit concept to exploit potential solutions and introduce diversification trends. Additionally, the ECapSA algorithm is improved by integrating the principles of the cooperative island model, resulting in the iECapSA. This modification enables better management of population diversity and a more optimal balance between exploration and exploitation. The efficiency of iECapSA is validated through a series of experiments, including the IEEE-CEC2014 benchmark functions and training the feedforward neural network (FNN) on seven biomedical datasets. The performance of iECapSA is compared to other metaheuristic techniques, namely differential evolution (DE), sine cosine algorithm (SCA), and whale optimization algorithm (WOA). The results of the comparative study demonstrate that iECapSA is a strong contender and surpasses other training algorithms in most datasets, particularly in terms of its ability to avoid local optima and its improved convergence speed. © 2023 IEEE.

关键词： Feedforward neural networks

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Bio-Inspired Feature Selection and Fusion for Facial Emotion Recognition 4

Bio-Inspired Feature Selection and Fusion for Facial Emotion...

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4th IEEE Annual Flagship India Council International Subsections Conference, INDISCON 2023

作者： Gunda, Pranavi Teja, Mohan Christopher, Jabez Arunachalam, Vasan Bits Pilani Department of Computer Science and Information Systems Telangana India Bits Pilani Department of Civil Engineering Telangana India

ISBN: (纸本)9798350333558

This work presents an approach to improve emotion recognition systems by using two approaches: selection of feature subset using swarm intelligence based bio-inspired algorithms, and fusion of features from different regions of the face. For feature subset selection, Particle Swarm Optimisation (PSO) and Cuckoo Search algorithms are used. Classification is based on the fusion of features from three regions - entire face, mouth and eyes - and the predicted emotion (class label) is decided as the one given by two or more regions (Majority Vote). The ensemble of classifiers includes Random Forest and k-NN classifiers. It can be inferred that the ensemble model on an average yields 92% accuracy for Random Forest and 90% for k-NN, with a selected subset of almost half, or even less than half of the total features extracted. Moreover, not just in the majority region alone, the ensemble model outperformed the models in the mouth region and the face region. Though this work does not intend to show significant improvement in classification accuracy, the hybrid approaches and feature subsets presented in this work can significantly reduce computations with large datasets along with promising classification performance;moreover, some general observations and outcomes would help follow-ups in this area of research. © 2023 IEEE.

关键词： Feature Selection

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