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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Kusuma Atmaja, Wahyu Haris Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department West Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Engineering Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect Internet connectivity in clusters of West Java, Indonesia. It also aims to evaluate the results of models that forecast how these internal factors will affect internet connectivity. According to the research findings, the FTTH Cluster has eight significant Internet predictor connectivity out of 18 elements. Activation delay and Administration Documentation of Activation with Connectivity were the two major parameters modeled out of the 18 components. Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) models were used to predict Internet Broadband Connectivity Classification based on the two most important variables. According to the study, ANN is the best model, with 99% accuracy and 99.6% precision when compared to SVM and DT. Furthermore, it exceeds previous studies that used the ANN model and achieved 97.92% accuracy. © 2023 IEEE.

关键词： Prediction models

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A Hybrid Inference Engine to Predict Uncertain Sugar Content for Sugar Industry 9th

A Hybrid Inference Engine to Predict Uncertain Sugar Content...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Asrol, Muhammad Napthaleni Utama, Ditdit Nugeraha Suharjito Harito, Christian Syafrudin, Muhammad Industrial Engineering Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul05006 Korea Republic of

The sugar industry is facing challenges in increasing productivity to meet consumer demand. One opportunity for productivity improvement lies in ensuring sugar content. This study proposes a hybrid model to predict sugar content by considering uncertainty factors. A hybrid model combining fuzzy subtractive clustering, and a fuzzy inference system is proposed to predict sugar content. The clustering results using silhouette and fuzzy subtractive clustering successfully identified 6 cluster centres from 2225 datasets collected in a sugar industry in East Java Province. The hybrid inference engine model is designed with fuzzy rules derived from the clustered data. Two inference models are developed: triangular and Gaussian fuzzy numbers. The testing results indicate that the hybrid model with triangular fuzzy numbers shows the smallest error with an R2 value of 0.95. This model is possible to applied in the sugar industry for decision makers in improving productivity with taking attention into uncertain factors influencing sugar content. © 2024 The Authors.

关键词： Prediction models

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Light Fidelity and Internet of Things for Medical Applications 4

Light Fidelity and Internet of Things for Medical Applicatio...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Adi, Puput Dani Prasetyo Sariningrum, Ros Rahmatullah, Rizky Novita, Hessy Situngkir, Tiar Lina Adji, Priyo Hartono Lusiastuti, Angela Mariana Wibowo, Antonia Rahayu Rosaria Prastiyono, Yudi Adiprabowo, Tjahjo Yassir, Muhammad Maulana, Fairuz Iqbal Department of Telecommunication Bandung Indonesia Veterinary Research Center Bandung Indonesia Singaperbangsa University Faculty of Economics Department of Management Karawang Indonesia Bandung Indonesia Langlangbuana University Faculty of Engineering Department of Electrical Engineering Bandung Indonesia Institut Teknologi Dan Bisnis Nobel Department of System and Information System Makassar Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

This study focuses on transmitting data utilizing medical sensors that are vital for patients in hospitals and other settings. In this situation, Light Fidelity refers to transmitting medical sensor data or other data comprising the Transmitter (Tx) and the Receiver (Rx). If the Light Fidelity Principle is used, the Transmitter transmits LEDs or light to the Receiver. At the same time, the Receiver contains a receiver or light detection component known as the Light Dependent Resistor (LDR);using programming and this basic working principle, data can be sent from the LED Transmitter to the LDR Receiver using C++ programming and its Library to send Medical sensor data. Sensors can include pulse sensors, like in prior research, and additional sensors such as SPO2 or blood pressure sensors. For medical data, a modest bit rate is sufficient, but LiFi applications can be created in such a way that they can be hybridized with WiFi and other RF devices such as LoRa and the Internet of Things. © 2024 IEEE.

关键词： C++ (programming language)

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The effect of the cyclic curricular design of modelling-based instruction with virtual labs

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International Journal of science Education 2024年第1期46卷 46-72页

作者： Chang, Hsin-Yi Liu, Chen-Chung Wen, Chi-Ting Chang, Ming-Hua Fan Chiang, Shih-Hsun Hwang, Fu-Kwun Program of Learning Sciences and Institute for Research Excellence in Learning Sciences National Taiwan Normal University Taipei Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Department of Computer Science and Information Management Soochow University Taipei Taiwan Department of Physics National Taiwan Normal University Taipei Taiwan

One issue facing teachers who implement model-based instruction within the school system is the tight curricular schedule. The ‘lesson objective tension’ calls for investigations to address instructional issues such as how to organise modelling-based instruction at the curriculum level which is across multiple units. We investigated the effect of the cyclic curricular design spacing out three modelling-based instruction units with virtual labs over three semesters, each involving 73, 181, and 150 junior high school students, respectively. Results indicate that, overall, the approach of modelling with virtual labs better promoted students’ scientific literacy than the traditional instruction did. Specifically, students with low and moderate school science achievements benefitted more as shown in their scientific literacy scores than did the high achievement students. Furthermore, this study found that receiving one unit of modelling-based instruction may not be sufficient to promote students’ scientific literacy, whereas the cyclic approach that suggests students receive two or more units of modelling-based instruction with virtual labs spaced over consecutive semesters had significant effects on promoting students’ scientific literacy. The results provide insights into when and how modelling-based learning with virtual labs can promote scientific literacy at a curricular level. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： lesson objective tension Modelling virtual labs

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Evolution of Artificial Intelligence and Machine Learning in Betterment of Healthcare Services 1

Evolution of Artificial Intelligence and Machine Learning in...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Abu Eid, Aws Ismail Maula, Shabana Wagh, Aparna Vyas, Vibha Patil, Prakash Sridhar, P. Arab Open University Faculty of Computing Studies Amman Jordan Jaywant College of Engineering and Polytechnic Department of Computer Science Engineering Karad India Jaywant College of Management Karad India College of Engineering Pune Department of Electronics and Telecommunication Pune India Department of Radio-Diagnosis Karad India Institute of Aeronautical Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350360707

Artificial Intelligence usually comprises a system that is composed of hardware and software. Machine learning is a subset of artificial intelligence (AI). The study discovered that the environment and contemporary technology on the current healthcare services have created new demands, which has led to the formation of these five categories of research. By matching eligible patients with clinical trials, AI technologies in the healthcare industry may also be beneficial for medical research. There are numerous other fascinating uses for AI in the biomedical field. It is evident that AI is becoming more and more relevant in the field of biomedicine. AI technologies may be able to assist in addressing significant health issues, but its potential may be constrained by the caliber of the health data that is now available and by the fact that AI cannot exhibit certain human traits, like empathy. Numerous ethical and societal concerns are brought up by the use of AI, many of which are also brought up by the use of data and healthcare technologies more generally. © 2024 IEEE.

关键词： Adversarial machine learning

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Automatic face recognition based on enhanced Vggface- 16 model in an unconstrained environment using transfer learning

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Multimedia Tools and Applications 2025年 1-23页

作者： Rathod, Vinod Motiram Patil, Amit Madhukar Motekar, Harish Sadashiv Usmani, MAhmer Solavande, Vivek Dadasaheb Rathod, Santosh Baburao Department of Computer Science and Engineering Bharati Vidyapeeth Deemed University DET Navi Mumbai Sector 3 Belpada Kharghar Maharashtra Navi Mumbai410210 India Department of Electronics and Telecommunication Bharati Vidyapeeth Deemed University DET Navi Mumbai Sector 3 Belpada Kharghar Maharashtra Navi Mumbai410210 India Vishwakarma University Maharashtra Pune411048 India

Facial recognition (FR) has seen significant advancements due to deep learning (DL) techniques, which offer discriminative face representation and improved performance for various applications. However, traditional FR systems still struggle with lighting variations, facial expression changes, pose fluctuations, occlusions, and overfitting, which reduce accuracy and reliability. Illumination inconsistencies affect feature extraction, leading to misclassification, while facial expressions and pose variations distort facial landmarks, making recognition less effective. Occlusions, such as accessories or partial face coverage, obscure critical features, causing false detections. Additionally, many models suffer from overfitting due to limited training diversity, reducing their generalization capability. The automatic face recognition system proposed in this research uses transfer learning in unconstrained situations and is based on an enhanced VGGFace- 16 model. The three steps of the approach are data augmentation, face detection, and face recognition. The training dataset is more diverse and less prone to overfitting using data augmentation techniques such as random rotation, shifts, shear, resizing, contrast enhancement, rescaling, and flips. Robust face detection is achieved by applying the Histogram of Orientated Gradients (HOG) technique. A modified VGGFace- 16 model is used for face recognition to enhance performance, including layers such as Global Average Pooling and four thick layers with PReLu activation. The system's effectiveness is demonstrated through its high accuracy, precision, recall, and F1-score of 99.9% on the LFW and VGGFace2 datasets, thereby overcoming traditional FR problems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Face recognition

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NeuroSonic: An Innovative Neuroacoustic Therapy to Enhance Mental Wellness with EEG Brain Signal

NeuroSonic: An Innovative Neuroacoustic Therapy to Enhance M...

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2024 IEEE Calcutta Conference, CALCON 2024

作者： Paul, Trisha Das, Susmita Chowdhuri, Swati Chanda, Ankur Neogi, Biswarup Chaudhuri, Sheli Sinha Jadavpur University Electronics and Telecommunication Engineering Department Kolkata700032 India Narula Institute of Technology Department of Electronics and Computer Science Kolkata700109 India Electronics and Electrical Engineering Department Institute of Engineering and Management Kolkata700091 India Kolkata700091 India R&DJIS College of Engineering Kolkata741235 India

ISBN: (纸本)9798331516666

NeuroSonic' presents an innovative EEGbased music treatment system painstakingly designed to improve mental wellness. NeuroSonic is a program that uses state-of-the-art algorithms to evaluate brainwave data from the frontal and temporal lobes within a particular frequency band. Leveraging electroencephalography (EEG) data obtained from participants engaged in to solve mathematical problem statements. The research aims to elucidate the distinct patterns of neural activation across different brain lobes. Through meticulous statistical analysis of EEG signals, the underlying mechanisms governing brain activity before and after listening the healing sounds and these conditions are explored using EMOTIVE. The study employs advanced signal processing techniques and statistical analysis to discern temporal and spatial dynamics in neural responses. We have collected EEG data from 20 no. of subjects both male and female. ANOVA analysis is used for statistical representation and SVM algorithm is used for data modelling process. We got 85 percent accuracy in data modelling. The challenging job is the mapping of EEG data and integrating it into the proposed device. This method utilizes EEG data and frequency analysis to create customized sonic settings for Autism patients, enhancing their mental health and endurance through therapeutic auditory experiences. © 2024 IEEE.

关键词： mHealth

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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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Improving Supplier Evaluation Model using Ensemble Method-Machine Learning for Food Industry 8

Improving Supplier Evaluation Model using Ensemble Method-Ma...

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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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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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