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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Asrol, Muhammad Wahyudi, Sofyan Suharjito Harito, Christian Utama, Ditdit N. Syafrudin, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Artificial Intelligence Sejong University Seoul05006 Korea Republic of

Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performance. Previous research has successfully designed decision tree and neural network models for assessing suppliers in the food industry with accuracies of 84.2% and 92.8% separately. Recognizing the opportunity to improve the model's performance, this study aims to advancing the machine learning models accuracy for analyzing and evaluating suppliers in the food industry. Two main models are proposed to enhance model accuracy: ensemble methods and support vector machine. This research has successfully designed a supplier evaluation model and demonstrated that the ensemble method - gradient boosting model outperforms other ensemble methods and support vector machine which is achieved a accuracy of 93.6% on a cross-validation dataset. The development of a dashboard is required to implement the supplier evaluation model using machine learning, facilitating decision-makers in evaluating and controlling supplier performance. © 2023 The Authors. Published by Elsevier B.V.

关键词： Ensemble Method Food Machine Learning Supply chain Supplier evaluation

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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Rule-Based Dialect of Tulang Bawang Stemmer

Rule-Based Dialect of Tulang Bawang Stemmer

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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Abidin, Zaenal Junaidi, Akmal Wamiliana Lumbanraja, Favorisen Rosyking Kurniasari, Dian Borman, Rohmat Indra Doctoral Program of Mathematics and Natural Sciences Universitas Lampung Bandar Lampung Indonesia Universitas Lampung Department of Computer Science Bandar Lampung Indonesia Universitas Lampung Department of Mathematics Bandar Lampung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandar Lampung Indonesia

ISBN: (纸本)9798331513320

Stemming, an essential procedure in natural language processing (NLP), diminishes words to their base forms, facilitating tasks such as information retrieval and sentiment analysis. Although stemming techniques for highresource languages are well-developed, numerous low-resource languages, including dialect of Tulang Bawang, suffer from inadequate solutions owing to a scarcity of linguistic data and resources. Existing systems, including rule-based stemmers, have demonstrated efficacy in processing low-resource languages such as Indonesian and Javanese by utilizing established morphological rules. Nonetheless, these methods encounter considerable obstacles, such as restricted adaptability, inability to accommodate unusual root structures, and excessive dependence on fixed rules that might result in over- or understemming. Rule-based methodologies frequently misidentify roots when faced with intricate affixes or unconventional word forms. We introduce an improved rule-based Tulang Bawang Stemmer aimed at overcoming these constraints by enhancing current linguistic rules and integrating new patterns specific to the language's morphology. Assessed on 500 test samples and 200 independent test samples, our improved stemmer attained gold standard evaluation metrics of 96.2% and 94%, respectively, surpassing prior implementations in both precision and generalization. The findings demonstrate the potential of enhanced rule-based techniques to improving NLP for lowresource languages. Improved stemming performance enables better downstream applications, promotes more efficient text analysis, and advances research in underrepresented languages. © 2025 IEEE.

关键词： Linguistics

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A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

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computers, Materials & Continua 2023年第2期74卷 2817-2838页

作者： Noureldin Eissa Uswah Khairuddin Rubiyah Yusof Ahmed Madani Centre for Artificial Intelligence&Robotics Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of Computer Engineering Arab Academy for ScienceTechnology and Maritime TransportCairo2033Egypt Department of Mechanical Precision Engineering Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia

Epigenetics is the study of phenotypic variations that do not alter DNA *** epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human *** of these alterations is DNA methylation;an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in ***,studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA ***,microarray technologies;such as Illumina Infinium BeadChip assays;are used to study DNA methylation at an extremely large number of varying *** each DNA methylation site,a beta value(β)is used to reflect the methylation ***,clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers aswell as identify the relevant loci without user *** study proposed a Nested Big Data Clustering Genetic Algorithm(NBDC-GA);a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation *** efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas(TCGA);a cancer genomics program created by the NationalCancer Institute(NCI)and the NationalHuman Genome Research *** performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm(IGA)that was tested using the same data *** NBDC-GA outperformed the IGA in terms of convergence ***,the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximumof 67%and of 94.5%for individual cancer type and collective cancer,*** proposed NBDC-GA was also able to identify two chromosomes with highly contrastingDNAmethylations activities th

关键词： Cancer clustering DNA methylation feature selection metaheuristic technique the cancer genome atlas

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Intelligent Hardware Solutions for COVID -19 and Alike Diagnosis - A survey 1

Intelligent Hardware Solutions for COVID -19 and Alike Diagn...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Aeri, Manisha Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

Globally, 210 nations have been affected by the 2019 Novel Coronavirus (COVID-19), which has been classified as a pandemic. The modern health system, as well as the economic, educational, and social sides of society, have all been severely impacted. While the rate of transmission keeps increasing, several cooperative strategies between stakeholders to create cutting-edge methods of screening and detecting COVID-19 instances among people at a comparable rate have been noticed. Also, the importance of computational models connected to the technologies of the fourth industrial revolution in accomplishing the desired feat has been emphasized. Unfortunately, there is a gap in the precision of COVID-19 case detection, prediction, and contact tracking. For patients with COVID-19 in isolation units, teleultrasound (TUS), particularly with the support of fifth generation (5G) wireless transmission technology, can offer rapid monitoring, quick clinical progress assessment, and assistance with guiding interventional procedures. Also, it helps conserve medical resources like equipment and supplies while lowering the risk of infection among medical personnel. The review of computer models presented in this work can be used to improve the effectiveness of COVID-19 pandemic case detection and prediction. We concentrate on adoptable big data, AI, and nature-inspired computing solutions for the current pandemic. According to the review, models inspired by nature have shown strong performance in feature selection for medical problems. In pandemic-related cases like COVID-19, contact tracing using big data analytics should also be investigated. © 2023 IEEE.

关键词： COVID-19

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Wireless Sensor Networks and Green Networking for 6G communication- An Overview 1

Wireless Sensor Networks and Green Networking for 6G communi...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bordoloi, Dibyahash Yadav, Satya Prakash Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Dehradun Rs Cse Graphic Era University Dept. of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

Wireless sensor networks and green networking have been major research areas in the field of communication technology for the past few decades. In particular, the development of 6G communication systems has brought renewed focus on these areas, as the demands for higher data rates, more diverse applications, and lower energy consumption continue to increase. The use of wireless sensor networks in 6G communication systems has been widely explored in the literature, and has shown great potential for a wide range of applications, including environmental monitoring, industrial process control, and healthcare. However, a key challenge in the deployment of these networks is ensuring their energy efficiency, as the demands on the network will be much higher than in previous generations. To address this challenge, researchers have proposed various green networking techniques, such as energy harvesting and low-power communication protocols, which have been shown to significantly reduce the energy consumption of the sensors. In addition to improving the energy efficiency of wireless sensor networks, researchers have also studied their reliability, scalability, and adaptability. These properties are important in 6G communication systems, as the demands on the network are expected to be much higher than in previous generations. Researchers have proposed various methods to improve the reliability, scalability, and adaptability of wireless sensor networks, including the use of error-correction codes, redundant communication channels, and machine learning algorithms. © 2023 IEEE.

关键词： Wireless sensor networks

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A Comparison of Multi Class Support Vector Machine vs Deep Convolutional Neural Network for Brain Tumor Classification

A Comparison of Multi Class Support Vector Machine vs Deep C...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Cahyo, Nur Ryan Dwi Sari, Christy Atika Rachmawanto, Eko Hari Jatmoko, Cahaya Al-Jawry, Rabei Raad Ali Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Department of Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia Northern Technical University Department Computer Science Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Sciece DhiQar Iraq

ISBN: (纸本)9798350339215

Multi SVM has long been one of the popular methods in classification, while DCNN has recently gained significant attention in image processing and pattern recognition. This research evaluates the effectiveness of Multi Class Support Vector Machine (M-SVM) and Deep Convolutional Neural Network (DCNN) techniques in classifying brain tumors. A dataset of 2660 3D medical images with dimensions 227 x 227 x 3;including Glioma, Meningioma, and Pituitary tumors, has been partitioned into distinct sets for both training and testing purposes. DCNN approach achieves excellent accuracy in identifying tumor names, with a training accuracy of 97.8% and 100% success rate in 9 experiments. The Multi SVM method demonstrates relatively good accuracy, with training accuracies ranging from 70% to 90% based on different kernel functions. These findings provide valuable insights for selecting appropriate methods in brain tumor classification and encourage further exploration of hybrid Multi SVM-DCNN approaches to enhance accuracy and reliability. © 2023 IEEE.

关键词： Convolutional neural networks

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Internet of Intelligent Things (IoT) - An Overview 1

Internet of Intelligent Things (IoT) - An Overview

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

ISBN: (纸本)9798350338027

The basic goal of the IoT is to permit the online connection and communication of everyday physical objects like appliances, vehicles, and other gadgets. This paves the way for effortless and automated data collection and exchange, as well as interaction between the objects themselves and with humans. The Internet of Everything (IoE) is an expansion of the Internet of Things (IoT) that aims to maximize and improve human life by connecting and making the most of all available resources (humans, information, and technology). The transportation, healthcare, agricultural, energy, and manufacturing sectors are all examples. The IoIT aims to incorporate AI and ML within the IoT's interconnected network of physical items. These objects can then independently behave in response to the information they have gathered and the instructions they have been given. This has the potential to lead to smarter, more efficient systems that can react to new information and human input in real time. The Internet of Things, the Internet of Everything, and the Industrial Internet of Things all have the same overarching goal: to pave the way for the development of more efficient and intelligent systems and processes that can enhance a wide range of human endeavors. However, in order to make sure that these technologies are used in a responsible and ethical way, it will be necessary to carefully analyze their consequences and the issues they provide. © 2023 IEEE.

关键词： Internet of things

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Identification of Sounds from Construction Sites: A Machine Learning Approach 4th

Identification of Sounds from Construction Sites: A Machin...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Maji, Sayantan Mukherjee, Himadri Dhar, Ankita Marciano, Matteo Roy, Kaushik Department of Computer Science and Engineering Sister Nivedita University Kolkata India TISA Lab Department of Computer Science West Bengal State University Kolkata India Gazelien Records Lab Department of Arts and Humanities Music Program New York University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (纸本)9789819757022

The rate at which the world is evolving is astonishing with cutting-edge technologies being introduced every day. There have been developments in every field ranging from constructing gigantic architectures to enhancing the ease and comfort with which we can commute daily. Nevertheless, this has given rise to different types of pollution, sound pollution being one of them. Although the impact of sound pollution is not instantaneously noticed, it has a distinct effect on humans and nature which is observed over a period of time. There are multiple sources that cause sound pollution. Sounds from ongoing construction sites are one of them. Such sounds disrupt the daily lifestyle of people, their health, and daily productivity. Mostly, these sounds start very early in the day thereby making it difficult for people to manually take precautions like shutting doors and windows or deploying mobile soundproofing solutions. Machine learning has advanced significantly over the years leading to the refinement of artificial intelligence. This has aided in the development of multifarious solutions to ease and simplify our daily lifestyle. This sets the stage for an autonomous system, that can detect sounds from construction sites very early to automatically deploy precautionary measures so that the effect of these sounds on close by residents can be minimized. In this paper, a system is presented that models audio clips using MFCC-based features for the identification of sounds from construction sites in the Urban environment. Experiments were performed on a dataset of over 6K clips composed of the varied construction sounds as well as ambient urban sounds and a maximum accuracy of 98.02% was achieved using random forest-based classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Automatic Heliostat Directing System Based On Microcontroller ESP32

Automatic Heliostat Directing System Based On Microcontrolle...

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International Conference on Electrical engineering and Informatics, ICEEI

作者： Richard Medyanto Nicholas Sanjaya Suryadiputra Liawatimena Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta

Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of other alternatives. The heliostat is a sunlight directing device with mirrors that can be used in a concentrated solar power system. Current heliostats require high capital investment due to their large frames and expensive components. This research was undertaken to develop a lower cost heliostat using a smaller frame, ESP32 microcontroller, servo motor and low-cost components. The position of the sun can be determined using an algorithm based on the National Oceanic and Atmospheric Administration (NOAA) solar calculator, and the mirror is moved to maintain the sun's reflection on a target. The result of this research is a set of heliostat prototypes consisting of the frame and control system. Tests were carried out to test the performance of the designed heliostat and it was found that the heliostat has an accuracy of about 60 cm and can raise temperatures up to 3.41°C. The conclusion is that the heliostat can be used in a concentrated solar power system to heat boilers in solar power towers.

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