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Interactive Middleware Services for Heterogeneous Systems

computer Systems Science & engineering 2022年第6期41卷 1241-1253页

作者： Vasanthi Raghupathy Osamah Ibrahim Khalaf Carlos Andrés Tavera Romero Sudhakar Sengan Dilip Kumar Sharma Department of Computer Science and Engineering Tagore Institute of Engineering and TechnologyAttur636112India Al-Nahrain University Al-Nahrain Nanorenewable Energy Research CenterBaghdadIraq Universidad Santiago de Cali in Cali Colombia South America Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152India Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226India

Computing has become more invisible, widespread and ubiquitoussince the inception of the Internet of Things (IoT) and Web of Things. Multipledevices that surround us meet user’s requirements everywhere. Multiple Middleware Framework (MF) designs have come into existence because of the rapiddevelopment of interactive services in Heterogeneous Systems. This resulted inthe delivery of interactive services throughout Heterogeneous Environments(HE). Users are given free navigation between devices in a widespread environment and continuously interact with each other from any chosen device. Numerous interactive devices with recent interactive platforms (for example, SmartPhones, Mobile Phones, Personal computer (PC) and Personal Digital Assistant(PDA)) are available in the market. For easy access to information and servicesirrespective of the device used for working and even at the drastic change ofthe environment, the execution of applications on a broad spectrum of computingdevices is propelled by the availability of the above-mentioned platforms. Different applications that need interoperability to coordinate and correspond with eachother should be facilitated. Using a standard interface and data format, HE mustlink various devices from various platforms together to communicate with eachother. To aid the interactive services performed by a middleware framework thatoperates on Application Programming Interface (API) over HEs, this issue aims toendorse an Adaptable Service Application Programming Interface (ASAPI).

关键词： Middleware services ontology interoperability

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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

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Mathematical Biosciences and engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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On Partial Optimal Transport: Revising the Infeasibility of Sinkhorn and Efficient Gradient Methods 38

On Partial Optimal Transport: Revising the Infeasibility of ...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Nguyen, Anh Duc Nguyen, Tuan Dung Nguyen, Quang Minh Nguyen, Hoang H. Nguyen, Lam M. Toh, Kim-Chuan Department of Mathematics National University of Singapore Singapore Department of Computer and Information Science University of Pennsylvania United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States IBM Research Thomas J. Watson Research Center United States Institute of Operations Research and Analytics National University of Singapore Singapore

ISBN: (纸本)1577358872

This paper studies the Partial Optimal Transport (POT) problem between two unbalanced measures with at most n supports and its applications in various AI tasks such as color transfer or domain adaptation. There is hence a need for fast approximations of POT with increasingly large problem sizes in arising applications. We first theoretically and experimentally investigate the infeasibility of the state-of-the-art Sinkhorn algorithm for POT, which consequently degrades its qualitative performance in real world applications like point-cloud registration. To this end, we propose a novel rounding algorithm for POT, and then provide a feasible Sinkhorn procedure with a revised computation complexity of Oe(n2/Ε4). Our rounding algorithm also permits the development of two first-order methods to approximate the POT problem. The first algorithm, Adaptive Primal-Dual Accelerated Gradient Descent (APDAGD), finds an Ε-approximate solution to the POT problem in Oe(n2.5/Ε). The second method, Dual Extrapolation, achieves the computation complexity of Oe(n2/Ε), thereby being the best in the literature. We further demonstrate the flexibility of POT compared to standard OT as well as the practicality of our algorithms on real applications where two marginal distributions are unbalanced. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Gradient methods

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Empowering Breast Cancer Detection with AI: A Modified Support Vector Machine Approach for Improved Classification Accuracy 4

Empowering Breast Cancer Detection with AI: A Modified Suppo...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Shiny, K.V. Sujatha, T. N.r, Wilfred Blessing College of Computing and Information Sciences University of Technology and Applied Information Technology Department Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India College of Computing and Information Sciences Univesity of Technology and Applied Sciences Information Technology Department Ibri Oman

ISBN: (纸本)9798350385793

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer Dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic Food Recognition Using Deep Convolutional Neural Networks with Self-attention Mechanism

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Human-Centric Intelligent Systems 2024年第1期4卷 171-186页

作者： Abiyev, Rahib Adepoju, Joseph Department of Computer Engineering Applied Artificial Intelligence Research Centre Near East University Lefkosa Northern Cyprus

The significance of food in human health and well-being cannot be overemphasized. Nowadays, in our dynamic life, people are increasingly concerned about their health due to increased nutritional ailments. For this reason, mobile food-tracking applications that require a reliable and robust food classification system are gaining popularity. To address this, we propose a robust food recognition model using deep convolutional neural networks with a self-attention mechanism (FRCNNSAM). By training multiple FRCNNSAM structures with varying parameters, we combine their predictions through averaging. To prevent over-fitting and under-fitting data augmentation to generate extra training data, regularization to avoid excessive model complexity was used. The FRCNNSAM model is tested on two novel datasets: Food-101 and MA Food-121. The model achieved an impressive accuracy of 96.40% on the Food-101 dataset and 95.11% on MA Food-121. Compared to baseline transfer learning models, the FRCNNSAM model surpasses performance by 8.12%. Furthermore, the evaluation on random internet images demonstrates the model's strong generalization ability, rendering it suitable for food image recognition and classification tasks.

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Unsupervised Learning for Detection of Radio Frequency Interference Signals

Unsupervised Learning for Detection of Radio Frequency Inter...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Singh, Brahampal Singh, Vijai Pal Bajpai, Anukool Bulla, Chetan Parikh, Swapnil Maranan, Ramya Trinity Institute of Innovations in Professional Studies Department of Cs and It Delhi201 308 India Trinity Institute of Innovations in Professional Studies Department of Applied Mathematics Uttar Pradesh 201 310 India Ggsip University Department of Computer Science New Delhi110 009 India Department of Business Intelligence Synechron Karnataka Bangalore560001 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat India Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350375237

In order to ensure that wireless communication systems operate at their best, it's crucial to develop reliable methods for detecting RF interference. This study compares two unsupervised anomaly detection algorithms, Support Vector Data Description (SVDD) and One-Class Support Vector Machine (SVM), using a small training database. We evaluated these algorithms by detecting various types of RF interference signals, including Continuous Interference (CI), Continuous Wave Interference (CWI), and Modulated Continuous Wave Interference (MCWI). While previous works have made strides in RF interference detection, they have some limitations. For example, some methods may struggle with accurately detecting RF interference in dynamic environments or when faced with varying interference patterns. Additionally, certain algorithms may exhibit reduced performance when dealing with low signal-to-noise ratios or in the presence of complex interference sources. To address these challenges, both SVDD and One-Class SVM (OCS) algorithms are used and described the methodology for evaluating their performance. Throughout the experiments, how well SVDD and OCS could identify abnormal interference signals are measured using the precision metric. The findings revealed that employing normalization techniques like feature scaling over a range or robust normalization significantly improved the accuracy of RF interference detection. Importantly, SVDD consistently outperformed OCS in this regard. © 2024 IEEE.

关键词： Support vector machines

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Privacy-Aware Artificial Intelligence with Homomorphic Encryption using Machine Learning

Privacy-Aware Artificial Intelligence with Homomorphic Encry...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Srinivasa Rao, B. Chattopadhyay, Saumitra Singh, Prashant Hazela, Bramah Sabarinathan, G. Yamini, Kalva Department of Computer Science and Engineering Bachupally Telangana Hyderabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Amity University Amity School of Engineering and Technology Lucknow Department of Computer Science and Engineering Uttar Pradesh India Psna College of Engineering and Technology Department of Mathematics Tamilnadu Dindigul India Sri Ramachandra Institute of Higher Education and Research Cyber Security in Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350333602

Along with the expansion of machine learning (ML) applications, the amount of data required to create predictions increases. Big-data ML has always been limited by off-chip memory capacity and computational speed. Considerably, privacy is one of the limitations of big data, which can be solved by homomorphic encryption (HE). Due to the combination of HE and ML, the multi-party privacy-protected ML suggested in this research may assist numerous users in doing artificial intelligence (AI) without disclosing private data. The technique may train common models in situations of data abuse, particularly in private data protection. The model trained using the ML technique named Artificial Neural Network (ANN) has a similar impact to the model developed using all data on a single computer, according to experiments using the algorithm. The gradient data is simply transmitted by all parties, and homomorphic procedures in the main computing system combine the gradient data. Besides, the optimal key is selected using the significance of the Lion Algorithm (LA). After homomorphic procedures, the learning model is modified depending on the new gradient data. © 2023 IEEE.

关键词： Neural networks

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Application of improved virtual sample and sparse representation in face recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1391-1402页

作者： Yongjun Zhang Zewei Wang Xuexue Zhang Zhongwei Cui Bob Zhang Jinrong Cui Lamin LJanneh College of Computer Science and Technology Guizhou UniversityGuiyangChina Big Data Science and Intelligent Engineering Research Institute Guizhou Education UniversityGuiyangChina School of Mathematics and Big Data Guizhou Education UniversityGuiyangChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina School of Mathematics and Information South China Agricultural UniversityGuangzhouChina

Sparse representation plays an important role in the research of face *** a deformable sample classification task,face recognition is often used to test the performance of classification *** face recognition,differences in expression,angle,posture,and lighting conditions have become key factors that affect recognition ***,there may be significant differences between different image samples of the same face,which makes image classification very ***,how to build a robust virtual image representation becomes a vital *** solve the above problems,this paper proposes a novel image classification ***,to better retain the global features and contour information of the original sample,the algorithm uses an improved non‐linear image representation method to highlight the low‐intensity and high‐intensity pixels of the original training sample,thus generating a virtual ***,by the principle of sparse representation,the linear expression coefficients of the original sample and the virtual sample can be calculated,*** obtaining these two types of coefficients,calculate the distances between the original sample and the test sample and the distance between the virtual sample and the test *** two distances are converted into distance ***,a simple and effective weight fusion scheme is adopted to fuse the classification scores of the original image and the virtual *** fused score will determine the final classification *** experimental results show that the proposed method outperforms other typical sparse representation classification methods.

关键词： representation sample image

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UAV Swarm Formation Control for Outdoor Surveillance Tasks 8th

UAV Swarm Formation Control for Outdoor Surveillance Tasks

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8th International Conference on Computing, Control and Industrial engineering, CCIE 2024

作者： Frolov, Oleg Safin, Ramil Tsoy, Tatyana Martinez-Garcia, Edgar A. Magid, Evgeni School of Applied Mathematics National Research University Higher School of Economics Moscow Russia Intelligent Robotics Department Kazan Federal University Kazan Russia Department of Industrial Engineering and Manufacturing Institute of Engineering and Technology Autonomous University of Ciudad Juarez Ciudad Juarez Mexico

ISBN: (纸本)9789819769339

Multi-robot and swarm control is a promising field in modern robotics. This paper considers a formation control of an unmanned aerial vehicles’ (UAV) swarm focusing on implementation of a leader-follower algorithm within the Robot Operating System (ROS) framework, which is designed as a ROS plugin. The paper describes a concept of a swarm formation control and presents a set of typical formations that could be helpful for swarms’ tasks. The leader-follower algorithm was implemented and validated in the Gazebo simulator. The proposed algorithm novelty lies in its simplicity and effectiveness for outdoor applications, integration of collision-avoidance mechanisms of swarms’ UAVs, and synchronization of UAVs’ actions within the swarm. Virtual experiments with a small swarm of PX4-LIRS UAVs demonstrated consistent results in self-collision avoidance by the swarms’ members while performing formation generation and its further motion within outdoor areas. Expected and real coordinates of each UAV of the swarm were compared, and the control strategy achieved acceptably small absolute errors in UAVs’ positioning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Robot Operating System

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