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IAENG International Journal of Computer Science 2021年第1期48卷 1-15页

作者： Mahdi, Mohammed Najah Ahmad, Abdul Rahim Ismail, Roslan Buhari, Adamu Muhammad Universiti Tenaga Nasional Malaysia Universiti Tenaga Nasional Malaysia Software Engineering Department College of College of Computing and Informatics Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Faculty of Engineering Multimedia University Malaysia

As the web grows, finding information from large data repositories has become increasingly difficult not only due to inadequate number of results that are relevant, but also due to poor sorting of relevant results from those irrelevant. The present search engines (SEs) use the query and response lookup process that does not provide precise results. Thus, researchers have gone beyond the paradigm to explore a new class of method to seek information, which is called exploratory research that is open-ended and its faceted search can improve the overall search process. Besides, many studies have begun tapping into enhancement of web search results relevancy. The web reflects vast heterogeneity, varying structure, and massive in volumes. Therefore, it is rather difficult to seek accurate outcomes as desired. As such, visualisation and interactive graphics have been proposed as methods to manage massive amounts of results and to project essential features for the web pages. Additionally, search engine controls reconstruction and reformulation of queries. As such, a search engine is presented in this study by developing it on the cloud computing platform environment The search engine is based on the idea of improving visual exploratory search (VExSearch) while exploring information in the web. This particular notion reflects the process of seeking and combing through the vast information by using the coordinated visualisation method, apart from minimising the effort spent in seeking information per query. The VExSearch was evaluated for its capability and performance and later compared with IMDb SE and CloudMining SE. The comparative results showed that the VExSearch was 66% more accurate than the other SEs. VExSearch also seemed to provide the most relevant results among all the three SEs, aside from attaining an average improvement of 20% in terms of recall. © 2021, IAENG International Journal of Computer Science. All rights reserved.

关键词： Search engines

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Monitoring Melon Manis Terengganu (MMT) by Implementation of IoT Technology

Monitoring Melon Manis Terengganu (MMT) by Implementation of...

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Future Technologies for Smart Society (ICFTSS), International Conference on

作者： Adriana Balqis Ali Mohd Kamir Yusof Zahrahtul Amani Zakaria Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kampus Besut Besut Terengganu Malaysia

ISBN: (数字)9798350373844

ISBN: (纸本)9798350373851

Melon Manis Terengganu (MMT) is of significant cultural and economic importance to the people of Terengganu and Malaysia as a whole. The demand for melon fruit is also increasing year by year. However, one of the challenges in producing a quality melon is the soil condition. The soil condition must be good to ensure that the melon can get enough water and fertilizer. The objective of this research is to develop a formulation and implement IoT technology to monitor the process of melon growth by analyzing data related to soil conditions. The purpose of Internet of Things (IoT) is to capture data using sensors and send it to a database system. In this method, a formulation is designed to calculate the amount of water and fertilizer based on soil moisture, soil electrical conductivity (EC), NPK levels, pH level, and water electrical conductivity (EC). These sensors will capture the data and send it to a database system. After that, the system will calculate the actual amount of water and fertilizer needed for each melon plant. This approach will help farmers supply the correct water and fertilizer levels for each melon plant. The implementation of IoT technology in agriculture, especially for melons, is expected to promote smooth growth and the production of high-quality melons.

关键词： Soil moisture Production Conductivity Sensor systems Database systems Agriculture Sensors Internet of Things Monitoring Fertilizers

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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Computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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A comparative study of word embedding techniques for SMS spam detection 14

A comparative study of word embedding techniques for SMS spa...

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14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Joseph, Prashob Yerima, Suleiman Y. School Computer Science and Informatics De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom Cyber Technology Institute De Montfort University Faculty of Computing Engineering and Media Leicester United Kingdom

ISBN: (纸本)9781665487719

E-mail and SMS are the most popular communication tools used by businesses, organizations and educational institutions. Every day, people receive hundreds of messages which could be either spam or ham. Spam is any form of unsolicited, unwanted digital communication, usually sent out in bulk. Spam emails and SMS waste resources by unnecessarily flooding network lines and consuming storage space. Therefore, it is important to develop high accuracy spam detection models to effectively block spam messages, so as to optimize resources and protect users. Various word-embedding techniques such as Bag of Words (BOW), N-grams, TF-IDF, Word2Vec and Doc2Vec have been widely applied to NLP problems, however a comparative study of these techniques for SMS spam detection is currently lacking. Hence, in this paper, we provide a comparative analysis of these popular word embedding techniques for SMS spam detection by evaluating their performance on a publicly available ham and spam dataset. We investigate the performance of the word embedding techniques using 5 different machine learning classifiers i.e. Multinomial Naive Bayes (MNB), KNN, SVM, Random Forest and Extra Trees. Based on the dataset employed in the study, N-gram, BOW and TF-IDF with oversampling recorded the highest F1 scores of 0.99 for ham and 0.94 for spam. © 2022 IEEE.

关键词： Embeddings

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Risk Analysis of Water Grid Systems Using Threat Modeling 11

Risk Analysis of Water Grid Systems Using Threat Modeling

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2022 11th International Conference on Information and Electronics Engineering, ICIEE 2022

作者： Rahim, Fiza Abdul Jamil, Norziana Cob, Zaihisma Che Sidek, Lariyah Mohd Sharizan, Nur Izz Insyirah Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Malaysia College of Computing and Informatics Universiti Tenaga Nasional Malaysia Institute of Energy Infrastructure Universiti Tenaga Nasional Malaysia

Critical infrastructure systems consist of physical and cyber assets that are essential to the operation of the economy and the government. As one of the most important critical infrastructures worldwide, the water sector has become vulnerable to new risks in the form of cyber threats that can severely impact public health, and are difficult to detect. A water grid system (WGS) plays an important role in guarding the business processes of the water sector against possible threats and risks. Threat modeling can be used to analyze threats to the WGS. It is applied to identify points of access to the assets and devices of the system, classify threats to them, assess the risks posed by them, and suggest mitigation measures. Each threat is classified based on its type according to the STRIDE methodology, and the results of the threat classification can be used to assess the level of risk by using the DREAD methodology. This yields a risk rating for each threat that can be used to devise mitigation measures to minimize the risk posed by it. Through the threat modeling stage, it is known that the high-risk threats on WGSs are tampering with a risk score of 14, denial of service threats with a risk score of 13, and repudiation threats with a risk score of 12. The results of the ranking are used to formulate recommendations in the form of mitigation controls against these threats. © Published under licence by IOP Publishing Ltd.

关键词： Critical infrastructures

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An Explainable Counterfeit and Genuine Ethiopian Banknote Classification Using Deep Learning

SSRN

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SSRN 2024年

作者： Woldehana, Yared Dereje Yimer, Mohammed Abebe School of Computing and Informatics Dilla University Dilla Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

Counterfeiting is a serious crime with serious impact around the world and in Ethiopia in particular. The national bank of Ethiopia has implemented various countermeasures to combat counterfeiting. The most successful counterfeit banknote detectors in use today are cash counters, which are hardware-based systems that use optical and magnetic sensors to detect and validate banknotes. This technology entails a high cost and low availability for the public and small businesses, where we find the biggest cash circulation outside of banks. Existing countermeasures are insufficient to address this critical issue. Advancements in technology, such as digital printing and sophisticated scanning equipment, have made it easier for counterfeiters to mislead their victims by producing banknotes very similar to genuine ones. Only a handful of studies have been conducted on the current Ethiopian banknotes. This study presents an explainable deep learning-based model for the classification of genuine and counterfeit Ethiopian banknotes. The study used transfer learning with SHAP (Shapley Additive explanations) and TF-EXPLAIN (TensorFlow Explain) explainable artificial intelligence frameworks for a better understanding of the classification prediction behind the models. Experimental results show that DENSE121 achieved the best accuracy of 99.87%, and InceptionV3 achieved a very similar result of 99.50%. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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The Conductivity and the Depolarization Factor of a Spheroid based on its Polarization Tensor and Volume 21

The Conductivity and the Depolarization Factor of a Spheroid...

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21st IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2025

作者： Yunos, Nurhazirah Mohamad Ahmad Khairuddin, Taufiq Khairi Auni Rusli, Siti Nur Universiti Teknologi Malaysia Department of Mathematical Sciences Faculty of Science Johor Malaysia Mathematical Sciences Studies College of Computing Informatics and Mathematics Universiti Teknologi Mara Johor Malaysia

ISBN: (纸本)9798331522193

The polarization tensor can be used to describe an object presented in an electric field. This is useful in getting accurate image about the object with electrical imaging device or increasing the likelihood of detecting the object with metal detector. Given the first order polarization tensor and the volume of a spheroid, some properties of the spheroid, such as its conductivity and depolarization factor, have not yet been investigated. Thus, this study aims to propose a method to determine the conductivity and depolarization factor of the spheroid, based on its first order polarization tensor and volume. In order to achieve this, the system of nonlinear equations is first derived based on the given first order polarization tensor, before being solved by the Newton's method. Numerical examples are then provided to demonstrate and justify the proposed algorithm. The findings show that the error for the computed conductivity and depolarization factor is small, suggesting that the proposed algorithm is reliable. In addition, the relationship between the spheroid's volume and conductivity is also discussed. The results illustrate an inverse relationship between the conductivity and depolarization factor when considering a positive definite first order polarization tensor. In contrast, for a negative definite first order polarization tensor, the findings reveal a positive correlation between spheroid's conductivity and its volume. The results obtained can then be used to characterize the spheroid based on its first order polarization tensor and also its volume. This provides additional information about the spheroid, which may contribute to the improvement of future techniques in electrical imaging and metal detection. © 2025 IEEE.

关键词： Nonlinear equations

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A Simple and Elegant Method for computing the Inverse Z-transform of Rational Functions

A Simple and Elegant Method for Computing the Inverse Z-tran...

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XXII International Symposium on Information, Communication and Automation Technologies, ICAT

作者： Željko Jurić Department of Computing and Informatics Faculty of Electrical Engineering Sarajevo Bosnia & Herzegovina

Finding the inverse z-transform of a rational function is usually considered a completely routine task with nothing special to say about it. However, practically all the procedures described in the engineering literature for doing this task can be very laborious for manual calculation when the function whose inverse z-transform is sought has multiple poles. This is often a problem for students when solving homework and exam tasks. The situation is particularly awkward when the poles are ugly numbers. Additionally, the described procedures are very unsuitable for programming on a computer. This paper proposes a simple, elegant and student-friendly way to calculate the inverse z-transform of a rational function. In addition to the basic variant, which is most suitable for manual calculation when the poles are relatively nice numbers, there are also variants that may be more suitable in other cases, including a variant that is extremely suitable for programming on a computer, even on the programmable calculators.

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Construction of Part of Speech Tagger for Malay Language: A Review

Construction of Part of Speech Tagger for Malay Language: A ...

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Natural Language Processing (ICNLP), International Conference on

作者： Nurulhuda Mohamad Ali Goh Hui Ngo Amy Lim Hui Lan Department Faculty of Computing and Informatics (FCI) Multimedia University Cyberjaya Malaysia

Part-of-Speech (POS) Tagging is one of the fundamental tasks in Natural Language Processing (NLP) in analyzing human languages. It is a process of identifying how words are used in a sentence by assigning the proper POS for each word. Thus far, most well-researched POS tagging is on European languages which are considered rich-resource languages due to the unlimited linguistic resources such as research studies and large standard corpus. However, POS tagging is arduous for low-resource languages due to the limitation of linguistic resources. The Malay language is considered as a low-resource language. Most POS tagging studies for the Malay language are using rule-based and stochastic methods. However, exploration in Deep Learning (DL) for Malay language is limited. Thus, studies with POS tagging methods that implement DL for other low-resource languages within South East Asia are included in this study. Hence, the aim of this study is to identify the state of the art, challenges, and future works of Malay POS tagger. This study provides a review of different methods, datasets, and performance measures used in POS tagging studies.

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New Modified Controlled Bat Algorithm for Numerical Optimization Problem

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Computers, Materials & Continua 2022年第2期70卷 2241-2259页

作者： Waqas Haider Bangyal Abdul Hameed Jamil Ahmad Kashif Nisar Muhammad Reazul Haque Ag.Asri Ag.Ibrahim Joel J.P.C.Rodrigues M.Adil Khan Danda B.Rawat Richard Etengu Department of Computing and Technology Iqra University IslamabadPakistan Computer Science Kohat University of Science and Technology(KUST)KohatPakistan Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Federal University of Piauí(UFPI) Teresina-PLBrazil Instituto de Telecomunicacoes Covilh?6201-001Portugal Department Computer Science and Technology Chang’an UniversityXi’anChina Department of Electrical Engineering and Computer Science Howard UniversityWashingtonDCUSA

Bat algorithm(BA)is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization *** leverages the echolocation feature of bats produced by imitating the bats’searching *** faces premature convergence due to its local search *** of using the standard uniform walk,the Torus walk is viewed as a promising alternative to improve the local search *** this work,we proposed an improved variation of BA by applying torus walk to improve diversity and *** *** Computerized Bat Algorithm(MCBA)approach has been examined for fifteen well-known benchmark test *** finding of our technique shows promising performance as compared to the standard PSO and standard *** proposed MCBA,BPA,Standard PSO,and Standard BA have been examined for well-known benchmark test problems and training of the artificial neural network(ANN).We have performed experiments using eight benchmark datasets applied from the worldwide famous machine-learning(ML)repository of *** results have shown that the training of an ANN with MCBA-NN algorithm tops the list considering exactness,with more superiority compared to the traditional *** MCBA-NN algorithm may be used effectively for data classification and statistical problems in the future.

关键词： Bat algorithm MCBA ANN ML Torus walk

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