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2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Kumar, Ashish Lourens, Melanie Elizabeth Tiwari, Nitin Dass, Pranav Kumar, M V Suresh Abdullah, Khairul Hafezad Computer Science and Engineering Its Engineering College Greater Noida India Durban University of Technology Faculty of Management Sciences South Africa Community Medicine Affiliation- Asmc Uttar Pradesh India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Koneru Lakshmaiah Education Foundation Guntur India Universiti Teknologi Mara Science Laboratory Unit Department of Academic Affairs Malaysia

ISBN: (纸本)9781665437899

Emotion detection is an important aspect in healthcare industries. Effective analysis of emotion detection helps in analyses patient's mental state, psychological state, disease progression rate etcetera. Emotion detection is also required for healthcare professionals (doctors and nurses). Automatic emotion detection is usually done with different technologies such as AI technology, multimodal system, pattern recognition, signal analysis, audio-visual analysis etcetera. The present research analyses the most effective technology for auto-emotion detection among all the technologies. The survey-based statistical analysis has been done in this research with 53 participants from different healthcare sectors of the united Kingdom. The data shows that AI-based multimodal system and Pattern recognition using Electrocardiogram and Electroencephalogram are the most effective technologies for automatic-emotion detection. The analysis also showed that emotion-detection is necessary for healthcare professionals and this analysis helps in enhancing patient's recovery rate by analysing their mental state. © 2022 IEEE.

关键词： Electrocardiograms

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Optimizing ASD Diagnosis: Enhanced Autism Spectrum Disorder Prediction Using Machine Learning Classifiers

Optimizing ASD Diagnosis: Enhanced Autism Spectrum Disorder ...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Arar Al Tawil Ibrahim A Gomaa Magdy Abd-Elghany Zeid AbdulRahman S. Marzouk Adam L. Hamad Khaled Y. Zakaria Moshir A. Danial Mounir N. Nassief Diaa Salama AbdElminaam Faculty of Information Technology Applied Science Private University Amman Jordan Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Jadara Research Center Jadara University Irbid Jordan Faculty of Computers Misr International University Egypt MEU Research Unit Middle East University Amman Jordan

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Autism Spectrum Disorder is a multidimensional neurological developmental disorder that includes a wide plethora of mental disorders targeting one's social and behavioral abilities. Gaining a perceptive understanding of a topic like ASD is essential in one's lifespan, whether the symptoms displayed are mild or severely debilitating. Due to the populous size of clinical terms under the disorder's spectrum and the drastic gap between each symptom's severity, the diagnosis of ASD became a long and tribulating task for experts and professionals alike. The purpose of this paper is to present and compare different Machine Learning Classifiers Categorical Boosting, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, Naïve Bayes and use their Training Algorithms Train-Test Splitting, K-Folds Cross-Validation to develop a quick and accurate Labeler ASD: “YES” ? “NO” for afflicted individuals based on multiple Datasets Generic, Adolescent, Children, Young, Adult, ASD aiming for an early diagnosis and an overall better understanding of the disorder. For each dataset, four Performance Metrics are extracted after applying the 7 ML models and the two training algorithms Accuracy, Precision, Recall, F1-Score. The resulting metrics are then displayed and compared to conclude the most accurate trained model for each dataset and with each of the training algorithms.

关键词： Training Autism Analytical models Accuracy Machine learning algorithms Predictive models Boosting

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Deep Learning for Localization of Damaged Photovoltaic Panels with Hotspot Detection 59

Deep Learning for Localization of Damaged Photovoltaic Panel...

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59th International Universities Power Engineering Conference, UPEC 2024

作者： Shaharin, Nur Khalida Maisyara Mohd Binti Omar, Madiah Farhana Binti Salehuddin, Nurliana Bin Ibrahim, Rosdiazli Nordin Bin Zakaria, Mohamed Faqih, Mochammad Universiti Teknologi Petronas Computer and Information Science Department Perak Bandar Seri Iskandar32610 Malaysia Universiti Teknologi Petronas Chemical Engineering Department Perak Bandar Seri Iskandar32610 Malaysia Renewable Energy Green Technology Unit Selangor Kajang43000 Malaysia Universiti Teknologi Petronas Electrical and Electronic Engineering Department Perak Bandar Seri Iskandar32610 Malaysia

ISBN: (纸本)9798350379730

The use of photovoltaic panels has been increasing over the last decade resulting in a growing need for advanced and robust condition monitoring systems. Implementation of photovoltaic panels in a large-scale farm requires predictive maintenance to ensure their reliability and effectiveness. In this study, a deep learning model has been developed for hotspot detection in photovoltaic panels through the processing of thermal images captured by a thermal imaging drone. An advanced convolutional neural network model is used wherein the task of the model is to detect hotspots on the photovoltaic panel, identify the region of interest of the hotspot, and provide a confidence level to determine the quality of hotspots. The associated GPS data will then be employed to geolocate and align these thermal images, creating a comprehensive thermal representation of the entire solar farm. This innovative approach allows for the precise localization of damaged photovoltaic panels within the solar farm, facilitating targeted maintenance and repair interventions, thereby optimizing farm performance and efficiency. This study leverages cutting-edge artificial intelligence and autonomous drone technology to revolutionize solar farm maintenance. Through a well-trained deep learning model, we can achieve precise hotspot detection in photovoltaic panels, minimizing physical interaction for workers and eliminating the need for extensive manual interventions. A deep learning model has been developed that provides hotspot localization with accuracy using bounding box predictions and confidence intervals. The model can reliably identify hotspots in thermal images with accuracy of 80% and confidence level above 90%. © 2024 IEEE.

关键词： Predictive maintenance

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MINIMUM CONSISTENT SUBSET IN TREES AND INTERVAL GRAPHS

arXiv

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arXiv 2024年

作者： Banik, Aritra Das, Sayani Maheshwari, Anil Manna, Bubai Nandy, Subhas C. Krishna Priya, K.M. Roy, Bodhayan Roy, Sasanka Sahu, Abhishek National Institute of Science Education and Research An OCC of Homi Bhabha National Institute Odisha Bhubaneswar752050 India Theoretical Computer Science The Institute of Mathematical Sciences India School of Computer Science Carleton University Canada Department of Mathematics Indian Institute of Technology Kharagpur India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India

In the Minimum Consistent Subset (MCS) problem, we are presented with a connected simple undirected graph G = (V, E), consisting of a vertex set V of size n and an edge set E. Each vertex in V is assigned a color from the set {1, 2, . . ., c}. The objective is to determine a subset V ′ ⊆ V with minimum possible cardinality, such that for every vertex v ∈ V , at least one of its nearest neighbors in V ′ (measured in terms of the hop distance) shares the same color as v. The decision problem, indicating whether there exists a subset V ′ of cardinality at most l for some positive integer l, is known to be NP-complete even for planar graphs. In this paper, we establish that the MCS problem for trees, when the number of colors c is considered an input parameter, is NP-complete10. We propose a fixed-parameter tractable (FPT) algorithm for MCS on trees running in O(26cn6) time, significantly improving the currently best-known algorithm whose running time is O(24cn2c+3). In an effort to comprehensively understand the computational complexity of the MCS problem across different graph classes, we extend our investigation to interval graphs. We show that it remains NP-complete for interval graphs, thus enriching graph classes where MCS remains intractable. Copyright © 2024, The Authors. All rights reserved.

关键词： Color

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Predictive coding beyond correlations 24

Predictive coding beyond correlations

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Proceedings of the 41st International Conference on Machine Learning

作者： Tommaso Salvatori Luca Pinchetti Amine M'Charrak Beren Millidge Thomas Lukasiewicz VERSES Research Lab Los Angeles CA and Institute of Logic and Computation Vienna University of Technology Austria Department of Computer Science University of Oxford UK MRC Brain Network Dynamics Unit University of Oxford UK and Zyphra Palo Calto CA Institute of Logic and Computation Vienna University of Technology Austria and Department of Computer Science University of Oxford UK

Recently, there has been extensive research on the capabilities of biologically plausible algorithms. In this work, we show how one of such algorithms, called predictive coding, is able to perform causal inference tasks. First, we show how a simple change in the inference process of predictive coding enables to compute interventions without the need to mutilate or redefine a causal graph. Then, we explore applications in cases where the graph is unknown, and has to be inferred from observational data. Empirically, we show how such findings can be used to improve the performance of predictive coding in image classification tasks, and conclude that such models are able to perform simple end-to-end causal inference tasks.

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Harnessing Deep Learning for Effective Extractive Text Summarization: A Comparative Study

Harnessing Deep Learning for Effective Extractive Text Summa...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Deema mohammed Alsekait Waref Almanaseer Ibrahim A Gomaa Magdy Abd-Elghany Zeid Salma Baligh Hana Alaa Jasmine Hegazy Julia Magdy Alaa Tariq Eldmrat Diaa Salama AbdElminaam Department of Computer Science and Information Technology Applied College Princess Nourah Bint Abdulrahman University Saudi Arabia Faculty of Information Technology Applied Science Private University Amman Jordan Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Human Sciences Midocean University Jadara Research Center Jadara University Irbid Jordan Faculty of Computers Misr International University Egypt. MEU Research Unit Middle East University Amman Jordan

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

Text Summarization might seem like a trivial problem, but in reality, is essential for data acquisition and understanding by compressing large amounts of text into specific a nd to-the-point summaries. This research is conducted to help compare the proposed methods' applications and make a summary system accessible to applications such as information retrieval, legal documents summarization and much more. from this, we intend to start by acquiring the dataset, preprocessing and training, and testing the models. In this paper will explore the methods that can be applied on summarization. These methods are Machine learning models like the random forest, naive Bayes, SVM, logistic regression, Deep learning like a neural networks, Transformers-based models like T5, and performance metrics like ROUGE. By trying all these various methods and models on a news article dataset we are going to compare the results from each of them and get the best performance. Finally, the results would be a comparison between all outputs and found that Natural Language Processor. In conclusion, Text Summarization is best done using NLP, and challenges in this project would be due to the diversity of text and context.

关键词： Support vector machines Deep learning Training Semantics Text summarization Transformers Information retrieval

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Detection of GAN Generated Fake Satellite Images Using Deep Learning 3

Detection of GAN Generated Fake Satellite Images Using Deep ...

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3rd International Conference on Applied Mathematics, Modeling and computer Simulation, AMMCS 2023

作者： Snehith, K. Anuradha, G. Vemuri Krishna N, Hari Sathishkumar, V.E. Abualigah, Laith Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of Industrial Engineering Hanyang University 222 Wangsimini-ro Seongdong-gu Seoul Korea Republic of Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Meu Research Unit Middle East University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9781643684581

Fake Images generation has become a common practice nowadays and this process has become elementary with the advent of Generative Adversarial Networks. Although though fake images are entertaining and have their benefits, they also have a few negative effects. Modern satellite images offer crucial data that aids in tracking various packages, including spatial decision, spectral properties, sensor sensitivity, and many others. Critical details needed for a few applications may be massively obtained from these photos. Yet, altering these images by removing, incorporating, or replicating objects leads to false perceptions of reality. It is our intention to come across those kind of fake satellite images. We use in-depth learning architectures to identify fake Satellite photos in order to distinguish between authentic and fake photos. © 2023 The Authors.

关键词： Generative adversarial networks

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Correction to: A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference (Archives of Computational Methods in Engineering, (2024), 31, 3, (1749-1822), 10.1007/s11831-023-10030-1)

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Archives of Computational Methods in Engineering 2024年 1-1页

作者： Salgotra, Rohit Sharma, Pankaj Raju, Saravanakumar gandomi, Amir H. Faculty of Physics and Applied Computer Science AGH University of Science and Technology Kraków Poland MEU Research Unit Middle East University Amman Jordan School of Electrical Engineering Vellore Institute of Technology Vellore India Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW 2007 Australia Óbuda University Budapest1034 Hungary

https://***/10.1007/s11831-023-10030-1. In this article the optimization algorithm ‘Geometric Mean Optimizer’ was abbreviated as ‘GeMO’, but should instead have been abbreviated as ‘GMO’. The original article ha...

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A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

arXiv

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arXiv 2024年

作者： Chauhan, Nidhika Kaur, Navneet Saini, Kamaljit Singh Verma, Sahil Alabdulatif, Abdulatif Khurma, Ruba Abu Garcia-Arenas, Maribel Castillo, Pedro A. University Institute of Computing Chandigarh University Punjab 143001 India Department of Computer Science and Engineering Chandigarh University Punjab 143001 India Department of Computer Science and Engineering Uttaranchal University Premnagar Uttarakhand Dehradun248007 India Department of Computer Science College of Computer Qassim University Buraydah52571 Saudi Arabia MEU Research Unit Faculty of Information Technology Middle East University Amman11831 Jordan Department of Computer Engineering Automatics and Robotics University of Granada Granada Spain

As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation research topics and seven performance management methods. Task allocation research areas are resource allocation, load-Balancing, and scheduling. Performance management includes monitoring and control, power and energy management, resource utilization optimization, quality of service management, fault management, virtual machine management, and network management. The study proposes new techniques to enhance cloud computing work allocation and performance management. Short-comings in each approach can guide future research. The research's findings on cloud data center task allocation and performance management can assist academics, practitioners, and cloud service providers in optimizing their systems for dependability, cost-effectiveness, and scalability. Innovative methodologies can steer future research to fill gaps in the literature. © 2024, CC BY.

关键词： Cloud computing

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Quantum Quest: Resource Requirement of RC4 Cryptanalysis under Grover’s Lens

Quantum Quest: Resource Requirement of RC4 Cryptanalysis und...

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IEEE Calcutta Conference (CALCON)

作者： Sarbani Sen Soumen Bajpayee Imon Mukherjee Goutam Paul Bhupendra Singh Computer Science & Engineering Indian Institute of Information Technology Kalyani India Cryptology & Security Research Unit Indian Statistical Institute Kolkata India Centre for Artificial Intelligence and Robotics DRDO Bengaluru India

ISBN: (数字)9798331516666

ISBN: (纸本)9798331516673

The RC4 cipher is a widely used software stream cipher which has long stood as a cornerstone in cryptographic applications due to its design simplicity, speed and efficiency. In this paper, we present an approach to decrypt RC4-encrypted data by leveraging Grover’s quantum search algorithm. To the best of our knowledge, this is the first documented attempt to employ Grover’s algorithm in breaking RC4 encryption. We enumerate the quantum resources required to perform exhaustive key search attack on RC4. Our approach demonstrates the potential of quantum computing to revolutionize cryptanalysis.

关键词： Ciphers Costs Quantum computing Software algorithms NIST Software Encryption Optimization Faces Lenses

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