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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Maria Fasli Amani Yousef Owda Tufail Abbasi Majdi Owda Lampros Stergioulas Bhanu Neupane University of Essex School of Computer Science and Electronic Engineering Colchester UK Faculty of Graduate Studies Arab American University Ramallah West Bank Palestine Faculty of IT & Design The Hague University of Applied Sciences The Hague The Netherlands Faculty of Data Science Arab American University Ramallah West Bank Palestine UNESCO Information and ICT & Sciences Paris France

In the last few years, we have seen the production of data via companies, organizations, or even directly by citizens/consumers increase exponentially. In this new era where almost every aspect of human activity entails the production and consumption of data, Governments play a key role in leading the way to opening up data to stimulate innovation, but also to facilitate transparency and accountability. In this paper, we advocate for this critical role that Governments have to play and the need for a concerted effort in providing a guiding framework to support the opening up of Government data for the wider benefit of society, but in particular to support the Sustainable Development Goals (SDGs). The SDGs, a universal call to action to end poverty, safeguard the planet, and promote prosperity for everyone, are inextricably linked to the appropriate use and dissemination of data. By using the potential of accessible government data, we not only advance the SDGs but also create a more fair and sustainable future for all.

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A Review of Algorithms, datasets, and Criteria in Word Sense Disambiguation with a View to its Use in Islamic Texts 8

A Review of Algorithms, Datasets, and Criteria in Word Sense...

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8th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2020

作者： Farahani, Yasamin Vasheghani Janfada, Behrooz Bidgoli, Behrooz Minaei University of Science and Technology Data Mining Lab Faculty of Computer Engineering Tehran Iran

ISBN: (数字)9781728173016

ISBN: (纸本)9781728173016

Most of the words in a natural language are polysemous;it means, they have multiple senses. Word sense disambiguation is the process of determining the exact sense of the polysemous word by analyzing the context in which the word occurs. Word sense disambiguation is one of the fundamental tasks in the field of natural language processing. However, There have been few studies in word sense disambiguation in Persian and Arabic languages. For the above reason and considering the abundance of ambiguous words and phrases in Islamic texts, the need for a special algorithm to help the disambiguation of such texts is felt. The present study attempted to have an overall review of word sense disambiguation approaches, strategies and issues, in order to open the way for future work on the presentation of similar algorithms for Islamic texts. We review approaches such as semi-supervised, unsupervised, supervised and knowledge-based approaches. The best strategy will be introduced, and the state-of-the-art systems related to it will be compared. © 2020 IEEE.

关键词： Knowledge based systems

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The Effect of Mechanical Stress on the Sensitivity of Fiberoptic Polarization Temperature Sensor

The Effect of Mechanical Stress on the Sensitivity of Fibero...

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New Trends in Signal Processing (NTSP)

作者： Zdenek Vylezich Martin Kyselak Karel Slavicek David Grenar Zuzana Chalk Department of Electrical Engineerings Faculty of Military Technlogy Brno Czech Republic Institute of Computer Science Data and Technical Networks Brno Czech Republic Department of Telecommunication Faculty of Electrical Engineering and Communication Brno Czech Republic Department of Communication Technology Electronic Warfare and Radiolocation Faculty of Military Technology Brno Czech Republic

ISBN: (数字)9788080406370

ISBN: (纸本)9798350362688

The article analyses enhancing the sensitivity of fiber-optic polarization sensors to temperature changes via additional longitudinal strain. This multifunctional sensor is suitable for application as a sensing element for security purposes and has many possible biomedical applications. The testing method consists of stretching the fiber on a proof tester where it is possible to get an exact setting of the operating force, and then applying a container of water at a different temperature. Changes were captured by a laser power meter and plotted into graphs describing several sets of measurements to keep independence on manufacturing. The sensor sensitivity was increased and the proposed solution appeared to be suitable for in-position sensors with minimum additional outlays.

关键词： Temperature sensors Temperature measurement Optical fiber polarization Mechanical sensors Optical fiber sensors Sensitivity Force Sensor phenomena and characterization Optical fiber testing Stress

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MLCardiovascularPrediction: Machine Learning-Based Cardiovascular Disease Prediction

MLCardiovascularPrediction: Machine Learning-Based Cardiovas...

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

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-elghany Zeid Ibrahim Abd Elatif Gomaa 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

Like most diseases that affect a human's life, car-diovascular disease is dangerous and unwanted for anyone who has heard of it as it brings a high risk of death. In today's world, most deaths are caused by heart disease as it could come from age, heritage, smoking, and many factors that could seem normal, and it doesn't matter what part of the world you are in. There must be a way to know or predict if this person is at high risk of cardiovascular disease. This paper and project talk about six algorithms: K-Nearest-Neighbour (KNN), Decision Tree (DT), Naive Bayes, Neural Network, Logistic Regression, and Support Vector Machine (SVM). The results came between 60% to 70% in one dataset and from 80 % to 90 % in the second. Still, they mostly favor the Neural Network algorithm as it can learn and model around complex relationships. The SVM showed different outputs between the two datasets as one is significantly larger than the other, possibly due to the outliers in both datasets being different and more complex in the larger one. Cardiovascular disease can be very dangerous, but finding and discovering it soon can be a lifesaver. These algorithms also can help find and predict it, as it was shown with not just decent, but good and respectable success rates for each one.

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Teeth Reconstruction Using Artificial Intelligence: Trends, Perspectives, and Prospects

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Journal of the California Dental Association 2023年第1期51卷

作者： Chau, Reinhard Chun Wang Thu, Khaing Myat Hsung, Richard Tai Chiu Lam, Walter Yu Hang Faculty of Dentistry The University of Hong Kong Hong Kong Department of Computer Science Hong Kong Chu Hai College Hong Kong Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong

Background: Artificial intelligence (AI) applications in dental restorative procedures have significantly developed over recent years. However, there is a lack of documentation regarding the types of AI used in tooth reconstruction. Types of Studies Reviewed: Studies using AI in tooth reconstruction were electronically searched over three databases PubMed, Cochrane library, and Google Scholar. The relevancy of theme tooth reconstruction was prioritized in searching. Only original research was included without the restriction of publication time or any filters. Results: A total of 18 studies were included in this review: 5 reported programmable AI systems based on mathematical models such as statistical estimation, 9 studies used biogeneric tooth libraries, and 4 presented the deep learning models. Practical Implications: AI has gained significant progress over the past two decades as a powerful tool for automated tooth reconstruction. However, further studies are required to compare different forms of AIs and to assess their clinical performance in the reconstruction of occlusal surfaces. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： Artificial intelligence computer neural networks Dentistry 4.0 prosthodontics restorative dentistry

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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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

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%; Naive Bayes: 83%.

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ML_BrainDetection: An Intelligent Model for Brain Tumor Identification Using Machine Learning

ML_BrainDetection: An Intelligent Model for Brain Tumor Iden...

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

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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p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets

arXiv

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arXiv 2022年

作者： Munteanu, Alexander Omlor, Simon Peters, Christian Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany

We study the p-generalized probit regression model, which is a generalized linear model for binary responses. It extends the standard probit model by replacing its link function, the standard normal cdf, by a p-generalized normal distribution for p ∈ [1, ∞). The p-generalized normal distributions (Subbotin, 1923) are of special interest in statistical modeling because they fit much more flexibly to data. Their tail behavior can be controlled by choice of the parameter p, which influences the model’s sensitivity to outliers. Special cases include the Laplace, the Gaussian, and the uniform distributions. We further show how the maximum likelihood estimator for p-generalized probit regression can be approximated efficiently up to a factor of (1 + Ε) on large data by combining sketching techniques with importance subsampling to obtain a small data summary called coreset. Copyright © 2022, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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An Energy-Efficient Event Reliability Protocol for Wireless Communication Networks

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Mobile Information Systems 2022年第1期2022卷

作者： Khan, Gulista Kumar Agarwal, Ambuj Khapre, Shailesh Arunkumar, G. Shankar, Achyut Khosravi, Mohammad R. Faculty of Engineering Teerthanker Mahaveer University Moradabad India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Data Science and Artificial Intelligence IIIT-Naya Raipur Chhattisgarh Raipur India School of Computer Science and Engineering VIT University Vellore India Department of Computer Science and Engineering Amity University Noida India Faculty of Engineering and Built Environment University of Johannesburg Johannesburg South Africa Department of Computer Engineering Persian Gulf University Bushehr Iran

Nowadays, WSN (wireless sensor networks) are used in every aspect of life. It consists of tiny sensor nodes that are capable of sensing, aggregating, dispensation, and sending data for end-user queries. Two major constraints in WSN are power consumption and reliability. As we know, power is limited per sensor node, so efficient utilization of power is needed all the way. Another constraint is reliability. Both constraints are in conflict with each other. If we try to increase reliability by sending the same packet on a different path, then it will increase power consumption. Many algorithms have been defined in the past to reduce power consumption and have reliable transmission. Basically, there are two types of reliability mechanisms: packet reliability and occurrence reliability. Package reliability explains about all packages must be reached at the destination. Event reliability ensures that at least one event should be successful in a specific region. This paper proposes an analysis model to evaluate the reliability of wireless sensor networks. In addition, this paper has proposed a reliability technique for events. The proposed algorithm has been simulated, analyzed, and compared with the already-published algorithms, namely BaseModel, SWIA, and ERP techniques. © 2022 Gulista Khan et al.

关键词： Electric power utilization

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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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