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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Mohamed S Farag Ibrahim A Gomaa Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Brain tumors are one of the greatest causes of death worldwide. Due to that, early diagnosis and classification of the tumor would surely help increase the chance of survival for patients worldwide. However, classifying and identifying brain tumors requires certain tools, including using Magnetic Resonance Imaging (MRI) to detect brain tumors. Moreover, many researchers in the past have also built up computer-Aided Diagnosis (CAD) systems to aid radiologists in their purpose of detecting brain abnormalities and tumors effectively. In this research, extracted multi-modal features like texture, morphological, entropy-based, Scale Invariant Features are proposed, and consequently, certain robust machine learning techniques, which are k-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes, as well as the Logistic Regression classifier were used to analyze the two data-sets included in the paper, detect tumors found in the brain, not to mention, examining their results, which have then shown the highest accuracy when using the Logistic regression classifier in both data sets, with the Support Vector Machine (SVM) classifier following it, which further support the argument made in the paper.

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Non-invasive Grading System for Banana Tiers using RGB Imaging and Deep Learning 2021

Non-invasive Grading System for Banana Tiers using RGB Imagi...

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7th International Conference on Computing and Artificial Intelligence, ICCAI 2021

作者： Rivero Mesa, Armacheska Chiang, John Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung804 Taiwan University of the Philippines Mindanao Davao City Philippines Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung804 Taiwan

ISBN: (纸本)9781450389501

Food losses transpire at postharvest and processing operations in developing countries, commonly caused by inaccurate manual classification of horticultural crops. The modernization of agricultural facilities and emerging technologies in agriculture has provided solutions for such losses and even increased productivity in a short duration and with higher precision. A non-invasive classification of bananas is presented in this paper, which grades banana tiers into different categories using digital images of bananas applied with deep learning techniques. The main objective of this paper is to develop a tier-based grading system for clustered fruits such as bananas and classify them in terms of quality (export class, middle class, and reject class), maturity (green, turning yellow, yellow, and overripe), and size (small, medium, and large). The classification models for the different grading parameters are developed using transfer learning and a fine-tuned VGG16 Deep CNN architecture. The system was able to automate the assembly line-like process of classifying bananas with a satisfactory overall accuracy using only a minimal number of image samples. The non-invasive technique will also serve as a paradigm for classifying other clustered fruits or horticultural crops. © 2021 ACM.

关键词： Grading

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(AIAM2019) Artificial Intelligence in Software engineering and inverse: Review

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International Journal of computer Integrated Manufacturing 2020年第10-11期33卷 1129-1144页

作者： Shehab, Mohammad Abualigah, Laith Jarrah, Muath Ibrahim Alomari, Osama Ahmad Daoud, Mohammad Sh. Computer Science Artificial Intelligence Department Aqaba University of Technology Aqaba Jordan Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Department of Computer Engineering Istanbul Gelisim University Istanbul Turkey Faculty of Engineering Al Ain University of Science and Technology Abu Dhabi United Arab Emirates

Artificial Intelligence (AI) and Software engineering are considered as significant fields to solve various problems. However, there are weaknesses in certain problem-solving in each field. Thus, this paper is a broad-based review of using artificial intelligence (AI) to improve software engineering (SE), and vice versa. As well as it intends to review the techniques developed in artificial intelligence from the standpoint of their application in software engineering. The aim of this review is highlighted in how the previous study benefited from incorporating the advantages of both fields. The researchers and practitioners on AI and SE belong to a wide range of audiences from the domains of optimization, engineering, data mining, clustering, etc., who will benefit from this study and areas for potential future research. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Application programs

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SKIN_ML: An Efficient Approach for Skin Cancer Detection Using Soft Computing Methods

SKIN_ML: An Efficient Approach for Skin Cancer Detection Usi...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

In today's environment, cancer is a fatal disease. Skin cancer has become a fairly common malignancy due to the spread of several forms of cancer. Skin cancer is divided into two types: melanoma and non-melanoma. Melanoma is one of the most fatal tumors on the planet, and it can spread to other parts of the body if not diagnosed early enough. Our proposed system uses Five alternative methods to predict a skin lesion's borders, texture, and color: a neural network and four standard machine learning classifiers. To enhance their performance, the outputs of these systems are merged using majority voting. Experiments have demonstrated that combining the five strategies yields the maximum level of accuracy. Pre-processing, Segmentation, Feature Extraction, and Classification are four critical phases in skin cancer identification. Skin lesion images were collected for this research from the International Skin Imaging Collaboration (ISIC), which contains over 3297 photos. The accuracy of the NN classifier is 91.9%, compared to 87.2% for the KNN classifier, 81.5% for the Naive Bayes classifier, 72.5% for the SVM classifier, and 68.3% for the DT classifier.

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Using XGBoost for Cyberattack Detection and Analysis in a Network Log System with ELK Stack 9th

Using XGBoost for Cyberattack Detection and Analysis in a Ne...

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9th International Conference on Frontier Computing, FC 2019

作者： Lai, Cing-Han Yang, Chao-Tung Kristiani, Endah Liu, Jung-Chun Chan, Yu-Wei Department of Computer Science Tunghai University Taichung City Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung City Taiwan Department of Informatics Faculty of Engineering and Computer Science Krida Wacana Christian University Jakarta Indonesia College of Computing and Informatics Providence University Taichung City Taiwan

ISBN: (纸本)9789811532498

Recently, cyberattackers have been developing more sophisticated ways to attack systems. Accordingly, identifying these attacks is getting more complicated in time. On many situations, network administrators were not capable of recognizing these attacks effectively or respond quickly. Whereas, to monitor and analyze the network log data which is very large and complicated is challenging. Therefore, in this case, there is a need to use artificial intelligence and machine learning techniques. In this paper, we develop a monitoring and analysis system for network log data. First, we used Elasticsearch, Logstash, and Kibana (ELK Stack) to monitor the network system. Second, we analyze the network log data use ‘eXtreme Gradient Boosting’ (XGBoost) to build a model of attack event detections. Finally, we use the XGBoost model to do cross-validated with the ELK Stack. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Impact of False Data Injection attacks on Decentralized Electric Vehicle Charging Protocols

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Transportation Research Procedia 2021年 52卷 331-338页

作者： Nikos Piperigkos Aris S. Lalos Industrial Systems Institute ATHENA Research and Innovation Center 265 04 Platani Greece Department of Computer Engineering and Informatics University of Patras 265 04 Patras Greece Department of Electrical and Computer Engineering University of Patras 265 04 Patras Greece

Electric vehicles (EVs) gain great attention nowadays since the electrification of private and public transport has a great potential to reduce greenhouse gas emissions and mitigate oil dependency. However, the influx of a large number of electrical loads without any coordination could have adverse affects to the electrical grid. More importantly, the complexity in the coordination of a large number of EVs, pose critical challenges in ensuring overall system integrity. A typical attack found in the controllers of connected EVs is false data injection (FDI), which can be utilized to distort real energy demand and supply figures. Energy distribution requests may therefore be erroneous, which results in additional costs or more devastating hazards. The lack of a proper coordination scheme, robust to such cyber attacks could cause voltage magnitude drops and unacceptable load peaks. In this work, we study the impact of FDI attacks, on various decentralized charging protocols with reduced computational requirements. The proposed decentralized EV charging algorithms only require from each EV to solve a local problem, hence the proposed implementation require low computational resources. An extensive evaluation study highlights the strengths and weaknesses of the presented solutions which are based on iterative convex optimization solvers.

关键词： Electric Vehicles Coordinated Charging Decentralized Protocols False Data Injection Attacks

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Improving Pseudo Relevance Feedback with Term Relationship using Firefly Algorithm 2

Improving Pseudo Relevance Feedback with Term Relationship u...

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2nd International Conference on Sustainable engineering and Creative Computing, ICSECC 2020

作者： Hasani, Muhammad Fikri Mandala, Rila Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781728175881

When searching for information with an information retrieval (IR) system, sometimes the results of the search documents provided by the system do not match the information needs of the user. Pseudo Relevance Feedback (PRF) based Query expansion (QE) tries to overcome these problems by adding words that are expected to improve retrieval results from top N ranked documents retrieved. The use of firefly algorithm (FA) as one of the optimization methods has been proven by the previous study to improve the performance of the IR system. However, in that study the weighting of words was done using the rocchio function of the Pseudo Relevant Document (PRD), so it is feared that the performance of IR system will be reduced if the number of relevant documents in PRD is little or none at all. Therefore, scoring by term relationship between query and PRD is used in this study combined with rocchio algorithm. The results of the study showed that usage of term relationship word co-occurrence or word similarity can improve the performance of the IRS that was previously built. In addition, word co-occurrence with jaccard have the best performance compared to the previous study and other combinations. FA itself was able to choose the optimal terms, even though the number of top N ranked documents increased. Furthermore, the combination of term relationship and rocchio algorithm can increase the convergence rate than the ones without rocchio algorithm. © 2020 IEEE.

关键词： Information retrieval

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Your Tweets Matter: How Social Media Sentiments Associate with COVID-19 Vaccination Rates in the US

arXiv

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arXiv 2022年

作者： Aleksandric, Ana Obasanya, Mercy Jesuloluwa Melcher, Sarah Nilizadeh, Shirin Wilson, Gabriela Mustata The University of Texas at Arlington Multi-Interprofessional Center for Health Informatics ArlingtonTX United States The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX United States

Objective: The aims of the study were to examine the association between social media sentiments surrounding COVID-19 vaccination and the effects on vaccination rates in the United States (US), as well as other contributing factors to the COVID-19 vaccine hesitancy. Method: The dataset used in this study consists of vaccine-related English tweets collected in real-time from January 4 - May 11, 2021, posted within the US, as well as health literacy (HL), social vulnerability index (SVI), and vaccination rates at the state level. Results: The findings presented in this study demonstrate a significant correlation between the sentiments of the tweets and the vaccination rate in the US. The results also suggest a significant negative association between HL and SVI and that the state demographics correlate with both HL and SVI. Discussion: Social media activity provides insights into public opinion about vaccinations and helps determine the required public health interventions to increase the vaccination rate in the US. Conclusion: Health literacy, social vulnerability index and monitoring of social media sentiments need to be considered in public health interventions as part of vaccination campaigns. © 2022, CC BY.

关键词： Vaccines

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RetinaRegNet: A zero-shot approach for retinal image registration

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computers in Biology and Medicine 2025年 186卷 109645-109645页

作者： Sivaraman, Vishal Balaji Imran, Muhammad Wei, Qingyue Muralidharan, Preethika Tamplin, Michelle R. Grumbach, Isabella M. Kardon, Randy H. Wang, Jui-Kai Zhou, Yuyin Shao, Wei Department of Electrical and Computer Engineering University of Florida GainesvilleFL32610 United States Department of Medicine University of Florida GainesvilleFL32610 United States Department of Computational and Mathematical Engineering Stanford University StanfordCA94305 United States Department of Health Outcomes and Biomedical Informatics University of Florida GainesvilleFL32610 United States Department of Internal Medicine University of Iowa Iowa CityIA52242 United States Iowa City VA Center for the Prevention and Treatment of Visual Loss Iowa CityIA52242 United States Department of Radiation Oncology University of Iowa Iowa CityIA52242 United States Department of Ophthalmology and Visual Sciences University of Iowa Iowa CityIA52242 United States Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Intelligent Clinical Care Center University of Florida GainesvilleFL32610 United States

Retinal image registration is essential for monitoring eye diseases and planning treatments, yet it remains challenging due to large deformations, minimal overlap, and varying image quality. To address these challenges, we propose RetinaRegNet, a multi-stage image registration model with zero-shot generalizability across multiple retinal imaging modalities. RetinaRegNet begins by extracting image features using a pretrained latent diffusion model. Feature points are sampled from the fixed image using a combination of the SIFT algorithm and random sampling. For each sampled point, its corresponding point in the moving image is estimated by cosine similarities between diffusion feature vectors of that point and all pixels in the moving image. Outliers in point correspondences are detected by an inverse consistency constraint, ensuring consistency in both forward and backward directions. Outliers with large distances between true and estimated points are further removed by a transformation-based outlier detector. The resulting point correspondences are then used to estimate a geometric transformation between the two images. We use a two-stage registration framework for robust and accurate alignment: the first stage estimates a homography for global alignment, and the second stage estimates a third-order polynomial transformation to capture local deformations. We evaluated RetinaRegNet on three imaging modalities: color fundus, fluorescein angiography, and laser speckle flowgraphy. Across these datasets, it consistently outperformed state-of-the-art methods, achieving AUC scores of 0.901, 0.868, and 0.861, respectively. RetinaRegNet's zero-shot performance highlights its potential as a valuable tool for tracking disease progression and evaluating treatment efficacy. Our code is publicly available at: https://***/mirthAI/RetinaRegNet. © 2025 The Authors

关键词： Image registration

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System biology approach to identify critical host genes for dengue infection

System biology approach to identify critical host genes for ...

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2020 International Conference on computer, Electrical and Communication engineering, ICCECE 2020

作者： Barman, Ranjan Kumar Sen, Sagnik Mukhopadhyay, Anirban Maulik, Ujjwal Das, Santasabuj ICMR-National Institute of Cholera and Enteric Diseases Biomedical Informatics Centre Kolkata India Jadavpur University Department of Computer Science and Engineering Kolkata India University of Kalyani Department of Computer Science and Engineering Kalyani India

ISBN: (纸本)9781728144757

Dengue is currently the leading mosquito-borne viral disease of the world. Around 40% of the world's population is at risk of dengue infection. This is caused by one of the four serotypes of dengue virus (DENV -1 to -4), which spreads between the humans by Aedes mosquitoes. Severe dengue is a life-threatening disease. Identification of critical human genes related to dengue infection leads to better understanding of the disease mechanisms, improved diagnostics and development of an effective treatment. Here, we introduced a novel systems biology approach to find crucial human genes for dengue infection. We have integrated a large volume of biological information on human genes throughout our analysis steps. We found three critical genes that presumably regulate the outcome of dengue infection from a pool of 244 human genes. Pathway enrichment analysis of these 3 genes also indicates infectious disease specific pathways. The proposed method may be useful for other viral diseases. Finally, the identification of crucial host genes for the virus can help the development of host-dependent antiviral drugs. © 2020 IEEE.

关键词： Gene Ontology

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