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arXiv 2023年

作者： Augustino, Brandon Leng, Jiaqi Nannicini, Giacomo Terlaky, Tamás Wu, Xiaodi Sloan School of Management Massachusetts Institute of Technology United States Simons Institute for the Theory of Computing Department of Mathematics University of California Berkeley United States Department of Industrial and Systems Engineering University of Southern California United States Department of Industrial and Systems Engineering Lehigh University United States Quantum Computing and Optimization Lab Lehigh University United States Department of Mathematics University of Maryland College Park United States Department of Computer Science University of Maryland College Park United States Joint Center for Quantum Information and Computer Science University of Maryland United States

We propose a novel quantum algorithm for solving linear optimization problems by quantum-mechanical simulation of the central path. While interior point methods follow the central path with an iterative algorithm that works with successive linearizations of the perturbed KKT conditions, we perform a single simulation working directly with the nonlinear complementarity equations. This approach yields an algorithm for solving linear optimization problems involving m constraints and n variables to Ε-optimality using O (√m + n · RΕ1) queries to an oracle that evaluates a potential function, where R1 is an 1-norm upper bound on the size of the optimal solution. In the standard gate model (i.e., without access to quantum RAM) our algorithm can obtain highly-precise solutions to LO problems using at most O (√m + n · nnz(A)RΕ1) elementary gates, where nnz(A) is the total number of non-zero elements found in the constraint matrix. © 2023, CC BY.

关键词： Optimization algorithms

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Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning

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BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2022年第4期2卷 100088-100088页

作者： Islam, Md. Milon Islam, Md. Zabirul Asraf, Amanullah Al-Rakhami, Mabrook S. Ding, Weiping Sodhro, Ali Hassan Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Research Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Information Science and Technology Nantong University Nantong226019 China Department of Computer Science Kristianstad University KristianstadSE-29188 Sweden Shenzhen Institutes of Advanced technology Chinese Academy of Sciences Shenzhen China

Combating the COVID-19 pandemic has emerged as one of the most promising issues in global healthcare. Accurate and fast diagnosis of COVID-19 cases is required for the right medical treatment to control this pandemic. Chest radiography imaging techniques are more effective than the reverse-transcription polymerase chain reaction (RT-PCR) method in detecting coronavirus. Due to the limited availability of medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 patients from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2, where CNN is used to extract complex features from samples and classify them using RNN. In our experiments, the VGG19-RNN architecture outperformed all other networks in terms of accuracy. Finally, decision-making regions of images were visualized using gradient-weighted class activation mapping (Grad-CAM). The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff. All the data used during the study are openly available from the Mendeley data repository at https://***/datasets/mxc6vb7svm. For further research, we have made the source code publicly available at https://***/Asraf047/COVID19-CNN-RNN. © 2023 The Authors

关键词： COVID-19

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Optimal Bandwidth Utilization Techniques for IoT Device Networks using Edge Computing

Optimal Bandwidth Utilization Techniques for IoT Device Netw...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Dankan Gowda V G. Vijayakumar J. Chandrasekar Shekhar R KDV Prasad Senthil Kumar C Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Social Work ( Aided) Madras School of Social Work Chennai Tamilnadu India Department of Computer Science and Engineering Alliance University Bangalore Karnataka India Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Department of Information Technology School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

The IoT devices have persisted on growing at a very fast pace and this has proven very challenging in managing the network bandwidth. This paper spins the new concept of increasing the IoT networks bandwidth by adopting the edge computing strategies. It also offers data processing near the sources which reduces the load on the central servers and cuts down data transfer requirements. The usage of the Dynamic Bandwidth Allocation algorithm in the proposed method also enhances the priority of the imperative data and scales up the transmission rates according to the network status. The number of data units achieve higher values, the latency is less and overall, better results have been obtained for the total network performance. Consequently, the results obtained presented pathways through which the bandwidth challenge in IoT could be addressed by means of edge computing to enhance IoT capacity in the corresponding surroundings.

关键词： Performance evaluation Technological innovation Costs Heuristic algorithms Bandwidth Channel allocation Dynamic scheduling Internet of Things Time factors Edge computing

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Information Security and Privacy in the Digital World: Some Selected Topics

arXiv

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arXiv 2024年

作者： Sen, Jaydip Mayer, Joceli Dasgupta, Subhasis Nandi, Subrata Krishnaswamy, Srinivasan Mitra, Pinaki Singh, Mahendra Pratap Kundeti, Naga Prasanthi Rao, Chandra Sekhara M.V.P. Chekuri, Sudha Sree Pallapothu, Seshu Babu Nanjundan, Preethi George, Jossy P. Allali, Abdelhadi E.I. Morino, Ilham Oussous, Salma A.I.T. Beloualid, Siham Tamtaoui, Ahmed Bajit, Abderrahim Department of Data Science Praxis Business School Kolkata India Department of Computer Science and Engineering IIT Guwahati Assam India Department of Electronics and Electrical Engineering IIT Guwahati Assam India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangaluru India Department of CSE Lakireddy Balireddy College of Engineering Affiliated to JNTU Mylavaram Kakinada India Department of CSE RVR & JC college of Engineering Guntur India Department of Mathematics and statistics K.B.N. College Vijayawada India Department of Electrical Engineering Federal University of Santa Catarina Florianópolis Brazil Department of Data Science Christ University Lavasa Pune India National School of Applied Sciences Kenitra Morocco SC Department Mohammed V University Laboratory of Advanced Systems National Institute of Posts and Telecommunications Rabat Morocco

In the era of generative artificial intelligence and the Internet of Things,while there is explosive growth in the volume of data and the associated needfor processing, analysis, and storage, several new challenges are faced inidentifying spurious and fake information and protecting the privacy ofsensitive data. This has led to an increasing demand for more robust andresilient schemes for authentication, integrity protection, encryption,non-repudiation, and privacy-preservation of data. The chapters in this bookpresent some of the state-of-the-art research works in the field ofcryptography and security in computing and communications. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Utilizing Multi-layer Perceptron for Esophageal Cancer Classification Through Machine Learning Methods

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Open Public Health Journal 2024年 17卷

作者： Kumar, Sandeep Singh, Jagendra Ravi, Vinayakumar Singh, Prabhishek Al Mazroa, Alanoud Diwakar, Manoj Gupta, Indrajeet Department of Computer Science & Engineering Maharaja Surajmal Institute of Technology Delhi India School of Computer Science Engineering & Technology Bennett University Greater Noida India CSE Department Graphic Era deemed to be University Uttarakhand Dehradun India Graphic Era Hill University Uttarakhand Dehradun India School of Computer Science & AI SR University Telangana Warangal 506371 India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia

Aims: This research paper aims to check the effectiveness of a variety of machine learning models in classifying esophageal cancer through MRI scans. The current study encompasses Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), Recurrent Neural Network (RNN), and Visual Geometry Group 16 (VGG16), among others which are elaborated in this paper. This paper aims to identify the most accurate model to facilitate increased, improved diagnostic accuracy to revolutionize early detection methods for this dreadful disease. The ultimate goal is, therefore, to improve the clinical practice performance and its results with advanced machine learning techniques in medical diagnosis. Background: Esophageal cancer poses a critical problem for medical oncologists since its pathology is quite complex, and the death rate is exceptionally high. Proper early detection is essential for effective treatment and improved survival. The results are positive, but the conventional diagnostic methods are not sensitive and have low specificity. Recent progress in machine learning methods brings a new possibility to high sensitivity and specificity in the diagnosis. This paper explores the potentiality of different machine-learning models in classifying esophageal cancer through MRI scans to complement the constraints of the traditional diagnostics approach. Objective: This study is aimed at verifying whether CNN, KNN, RNN, and VGG16, amongst other advanced machine learning models, are effective in correctly classifying esophageal cancer from MRI scans. This review aims at establishing the diagnostic accuracy of all these models, with the best among all. It plays a role in developing early detection mechanisms that increase patient outcome confidence in the clinical setting. Methods: This study applies the approach of comparative analysis by using four unique machine learning models to classify esophageal cancer from MRI scans. This was made possible through the intensive training a

关键词： Accuracy Classification Esophageal cancer Machine learning MRI scans Networks (CNNs)

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Towards Advancing Distributed Data Mining: Intelligent Agent systems

Towards Advancing Distributed Data Mining: Intelligent Agent...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Mais Haj Qasem Mohammad Aljaidi Ghassan Samara Ayoub Alsarhan Raed Alazaidah Y ousef Ali Al-Gumaei Esra Alzaghoul Department of Computer Science Zarqa University Zarqa Jordan Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Artificial Intelligence Zarqa University Zarqa Jordan Global College Heriot Watt University Edinburgh UK Department of Computer Information Systems Faculty of King Abdullah II College of Information Technology University of Jordan Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Distributed data mining (DDM) deals with the analysis of distributed data and proposes algorithmic solutions to perform different data analysis and mining operations in a distributed manner by considering resource constraints. Here, patterns are discovered, and predictions are implemented based on multiple distributed data sources. However, DDM faces several problems in terms of performance and implementation. Specifically, the main problems include issues of autonomy and privacy, which this paper aims to solve. The distributed sites must be in the same environment or organization and under the control of the same administration, implying their agreement on the same classification algorithm. Consequently, the best solution is the use of intelligent agent systems that contain a group of autonomous agents with communication and coordination facilities with different classification algorithms and environments, can collaborate with each other, and can decide whether and when to request information from other agents.

关键词： Privacy Soft sensors Standards organizations Distributed databases Organizations Prediction algorithms Classification algorithms

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Emulating the Global Change Analysis Model with Deep Learning

arXiv

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arXiv 2024年

作者： Holmes, Andrew Jensen, Matt Coffland, Sarah Shen, Hidemi Mitani Sizemore, Logan Bassetti, Seth Nieva, Brenna Tebaldi, Claudia Snyder, Abigail Hutchinson, Brian Computer Science Dept Western Washington University BellinghamWA United States Applied Artificial Intelligence Systems Pacific Northwest National Laboratory SeattleWA United States Computer Science Department Utah State University LoganUT United States Joint Global Change Research Institute Pacific Northwest National Laboratory College ParkMD United States Utah State University LoganUT United States

The Global Change Analysis Model (GCAM) simulates complex interactions between the coupled Earth and human systems, providing valuable insights into the co-evolution of land, water, and energy sectors under different future scenarios. Understanding the sensitivities and drivers of this multisectoral system can lead to more robust understanding of the different pathways to particular outcomes. The interactions and complexity of the coupled human-Earth systems make GCAM simulations costly to run at scale - a requirement for large ensemble experiments which explore uncertainty in model parameters and outputs. A differentiable emulator with similar predictive power, but greater efficiency, could provide novel scenario discovery and analysis of GCAM and its outputs, requiring fewer runs of GCAM. As a first use case, we train a neural network on an existing large ensemble that explores a range of GCAM inputs related to different relative contributions of energy production sources, with a focus on wind and solar. We complement this existing ensemble with interpolated input values and a wider selection of outputs, predicting 22, 528 GCAM outputs across time, sectors, and regions. We report a median R2 score of 0.998 for the emulator's predictions and an R2 score of 0.812 for its input-output sensitivity. Copyright © 2024, The Authors. All rights reserved.

关键词： Man machine systems

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Towards Grouping in Large Scenes with Occlusion-aware Spatio-temporal Transformers

arXiv

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arXiv 2023年

作者： Zhang, Jinsong Gu, Lingfeng Lai, Yu-Kun Wang, Xueyang Li, Kun The College of Intelligence and Computing Tianjin University Tianjin300350 China The School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom The Department of Electronic Engineering Tsinghua University Beijing100190 China

Group detection, especially for large-scale scenes, has many potential applications for public safety and smart cities. Existing methods fail to cope with frequent occlusions in large-scale scenes with multiple people, and are difficult to effectively utilize spatio-temporal information. In this paper, we propose an end-to-end framework, GroupTransformer, for group detection in large-scale scenes. To deal with the frequent occlusions caused by multiple people, we design an occlusion encoder to detect and suppress severely occluded person crops. To explore the potential spatio-temporal relationship, we propose spatio-temporal transformers to simultaneously extract trajectory information and fuse inter-person features in a hierarchical manner. Experimental results on both large-scale and small-scale scenes demonstrate that our method achieves better performance compared with state-of-the-art methods. On large-scale scenes, our method significantly boosts the performance in terms of precision and F1 score by more than 10%. On small-scale scenes, our method still improves the performance of F1 score by more than 5%. The project page with code can be found at http://***/faculty/likun/projects/GroupTrans. © 2023, CC BY-NC-ND.

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Feature Comparison for Automatic Bug Report Classification 15th

Feature Comparison for Automatic Bug Report Classification

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15th International Conference on Computing and Information Technology, IC2IT 2019

作者： Luaphol, Bancha Srikudkao, Boonchoo Kachai, Tontrakant Srikanjanapert, Natthakit Polpinij, Jantima Bheganan, Poramin Intellect Laboratory Department of Computer Science Faculty of Informatics Mahasarakham University Mahasarakham province Thailand Computer Science Program Mahidol University International College Mahidol University Nakhonpathom province Thailand

ISBN: (纸本)9783030198602

Nowadays, various bug tracking systems (BTS) such as Jira, Trace, and Bugzilla have been developed and proposed to gather the issues from users worldwide. This is because those issues, called bug reports, contain a significant information for software quality maintenance and improvement. However, many bug reports with poor quality might have been submitted to the BTS. In general, the reported bugs in the BTS are firstly analyzed and filtered out by bug triagers. However, with the increasing amount of bug reports in the BTS, manually classifying bug reports is a time-consuming task. To address this problem, automatically distinguishing of bugs and non-bugs is necessary. To the best of our knowledge, this task is never easy for bug reports classification because the problem of bug reports misclassification still occurs to date. The background of this problem may be arise from using inappropriate or confusing features. Therefore, this work aims to study and discover the most proper features for binary bug report classification. This study compares seven features such as unigram, bigram, camel case, unigram+bigram, unigram+camel case, bigram+ camel case, and all features together. The experimental results show that the unigram+camel case should be the most proper features for binary bug report classification, especially when using with the logistic regression algorithm. Consequently, the unigram+camel case should be the proper feature to distinguish bug reports from the non-bugs ones. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System

Using Arduino Iot Modules As A Low Cost Environmental Resear...

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computer science and Emerging Technologies (CSET), International Conference on

作者： Murad Magableh Ziyad Marie Rajina R. Mohamed Mohd Hafizuddin Bin Ibrahim Julaily Aida Jusoh Rohit Kumar MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Public administration Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Palestine College of Computing dan Informatics Universiti Tenaga Nasional Malaysia Department of Electrical Engineering Politeknik Kuala Terenngganu Kuala Terengganu Malaysia Faculty of Informatics and Computing UniSZA Malaysia School of Computer Science and Engineering Jain Deemed To Be University Bangalore India

Environmental monitoring plays a crucial role in understanding the impact of human activities on our ecosystems. However, the cost and complexity of traditional monitoring systems often hinder widespread deployment, especially in resource-constrained regions. This research paper explores the feasibility and effectiveness of employing Arduino Internet of Things (IoT) modules as a low-cost alternative for environmental research monitoring. The study focuses on designing and implementing an Arduino-based environmental monitoring system capable of gathering real-time data on key environmental parameters, such as temperature, humidity, air quality, and soil moisture. The hardware setup comprises Arduino microcontrollers interfaced with a range of environmental sensors and connectivity modules for data transmission. To assess the system's reliability and accuracy, a series of field experiments were conducted in diverse environmental settings, including urban areas, agricultural zones, and natural habitats. The collected data was compared against measurements from conventional monitoring equipment to validate the Arduino-based system's performance. The research demonstrates that the Arduino IoT modules can effectively monitor and transmit environmental data with a reasonable level of accuracy and stability, making them suitable for various environmental research applications. The low-cost nature of Arduino components and the open-source ecosystem facilitate customization and scalability, enabling researchers and organizations to develop tailored monitoring solutions for specific environmental contexts. Additionally, the paper discusses the challenges and limitations encountered during the deployment of Arduino-based monitoring systems, including power constraints, sensor calibration, and data processing techniques. Furthermore, it explores potential avenues for improvement and optimization to enhance the system's capabilities and data accuracy. In conclusion, this research

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