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A Novel Approach for Optimal Cluster Identification and N-Order Hesitation Based Time Series Forecasting

SN computer science 2024年第7期5卷 1-14页

作者： Dixit, Ankit Jain, Shikha Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Time series forecasting is an important field of research, especially when the series is completely random, known as a strictly non-stationary time series (NS-TS). To handle the randomness efficiently, the paper presents a novel approach to identify the optimal number of clusters in NS-TS followed by a hesitation-based N-order IFS forecasting algorithm (called IFSH_new). The performance of the proposed approach for determining the optimal number of clusters is demonstrated using four variants of the proposed algorithm, IFSH_new, across five datasets. Furthermore, a comparison of the best variant of the proposed approach with existing state-of-art work based on mean absolute percentage error (MAPE) and root mean square error (RMSE) confirms the superiority of the proposed model over others. Additionally, the graphical representation of actual versus forecasted data points for different datasets shows good improvement at high change points compared to other approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Degree of hesitation Intuitionistic fuzzy set (IFS) N-order forecasting model Non-stationary time series (NS-TS) Time series forecasting model

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A Model to Validate Different Inflections of Assamese Verbs

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Recent Patents on Engineering 2025年第2期19卷 13-25页

作者： Sarma, Parismita Bhuyan, Manash Pratim Kalita, Chandamita Upadhyaya, Varsha Department of Information Technology Gauhati University Assam Guwahati India Department of Computer Science Dibru College Assam Dibrugarh India

A verb in a sentence defines the action to be performed and keeps the subject of the sentence in motion. Assamese is a language spoken by millions of people in Assam, a North Eastern state of India. In the Assamese language, different inflections of verbs express the tense of the action, whether it is present, past or future. Verbs are inflected according to person, number, gender, tense, and voice, thus showing their existence in different situations. In this research work, we have built a tool to check whether an Assamese verb form is valid or invalid. After checking the validity, it also displays the meaning of the inflected form of the root verb in English. The root verbs are conjugated with the inflectional morphemes, and inflected verb forms are recorded. Background: This work is completely new and done by the authors for the Assamese language. To build this tool, a few research papers on this language were studied, and a summary of them is written in the Related Work section. Methods: In this experiment, 72 root verbs and 132 inflections were used. GUI was designed by using Tkinter in Python. The inflected verb form passed through three modules consisting of different searching techniques. Assamese corpus, Google search, Assamese Wikipedia, and Manual evaluation, respectively. Results: Finally, 662 Assamese valid verb forms were found that have meaning and are used in a different context. Corpus search, Google search, Assamese Wikipedia and Manual Evaluation gave 232, 733, 550 and 112 numbers of overall search results accordingly. Novelty: This paper presents a novel validation framework for Assamese language verb inflections and hence it can be defined as a patent work. This work can help the linguistic community better comprehend verb inflection patterns and linguistic diversity. This patent paper explores the inflections of a lesser-known low-resource language that would be beneficial. This study could be considered as innovative and important because it h

关键词： Speech recognition

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Formal verification for security and attacks in IoT physical layer

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Journal of Reliable Intelligent Environments 2024年第1期10卷 73-91页

作者： Toman, Zinah Hussein Hamel, Lazhar Toman, Sarah Hussein Graiet, Mohamed Valadares, Dalton Cézane Gomes Department of Computer Science FSM Al-Monastir University Monastir Tunisia Department of Computer Science Faculty of Computer Science and Information Technology Al-Qadisiyah University Al Diwaniyah Iraq Department of Computer Science ISIMM Al-Monastir University Monastir Tunisia IMT Atlantique LS2N Nantes France Federal Institute of Pernambuco/Federal University of Campina Grande Campina Grande Brazil

IoT devices are more important than ever. In a connected world, IoT devices have many uses. They are no longer merely used at work;they are part of our everyday lives. Security concerns arise if the devices generate, collect, or process sensitive data. Physical layer security controls are the cornerstone once the risk for humans increases when physical security fails. To achieve security in IoT devices, preventing is better than detecting. Formal verification is an important and valuable tool for detecting possible vulnerabilities and ensuring data security. Thus, this paper proposes an Event-B proof-based formal model of IoT physical layer security and attacks from the requirements analysis level to the goal level. Our model is built incrementally using a refining method during design and verification. We present a three-level formal approach: first, the construction of the IoT physical layer;then, we check for IoT physical layer vulnerabilities by processing the lack of some characteristics that cause these vulnerabilities, such as speed, typical bandwidth, and power consumption;lastly, we detect physical layer attacks like jamming and MAC spoofing, which helps to build security proofs. Our approach uses an electrocardiogram (ECG) IoT system as a case study, and as an additional case study to back up the proposed method’s generalizability, we used a fire alarm system. Also, we use the proof obligations and the ProB animator in the Rodin model checking tool to check and validate our approach. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Internet of things

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AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping

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Journal of Ambient Intelligence and Humanized Computing 2024年第7期15卷 3009-3033页

作者： Inani, Hardik Mehta, Veerangi Bhavsar, Drashti Gupta, Rajeev Kumar Jain, Arti Akhtar, Zahid Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Network and Computer Security State University of New York Polytechnic Institute NY United States

Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Chemical activation

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and Engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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An efficient Quasi-Affine Transformation Evolutionary algorithm with fixed dimension updating and its application in UAV 3D path planning

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9755-9781页

作者： Sung, Tien-Wen Zhao, Baohua Zhang, Xin Lee, Chao-Yang Fang, Qingjun Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou China Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm's practicability. © 2024 - IOS Press. All rights reserved.

关键词： Optimization

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Accurate Phase Detection for ZigBee Using Artificial Neural Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 2505-2518页

作者： Ali Alqahtani Abdulaziz A.Alsulami Saeed Alahmari Mesfer Alrizq Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh *** standard uses a physical layer of binary phase-shift keying(BPSK)modulation and can be operated with two frequency bands,868 and 915 *** frequency noise could interfere with the BPSK signal,which causes distortion to the signal before its arrival at ***,filtering the BPSK signal from noise is essential to ensure carrying the signal from the sen-der to the receiver with less ***,removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor ***,researchers have reported a posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ***-while,artificial neural network(ANN)and machine learning(ML)models outper-formed results for predicting signals for detection and classification *** paper develops a neural network predictive detection method to enhance the performance of BPSK ***,a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network(WPAN)***,Gaussian noise was injected into the BPSK simulation *** reduce the noise of BPSK phase signals,a recurrent neural networks(RNN)model is implemented and integrated at the receiver side to esti-mate the BPSK’s phase *** evaluated our predictive-detection RNN model using mean square error(MSE),correlation coefficient,recall,and F1-score *** result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient(R-value)metric for different signal-to-noise(SNR)*** addition,our RNN-based model scored 98.71%and 96.34%based on recall and F1-score,respectively.

关键词： Neural networks RNN BPSK phasedetection WPAN IEEE802.15.4 signal demodulation

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Mitigating Bias and Assessment Inconsistencies with BERT-Based Automated Short Answer Grading for the Indonesian Language

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 533-545页

作者： Amalia, Amalia Lydia, Maya Silvi Muchtar, Muhammad Anggia Manik, Fuzy Yustika Sinu Gunawan, Dani Department of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Information Technology Universitas Sumatera Utara Medan Indonesia

Automating the grading of short answers in Indonesian presents unique challenges, primarily due to the inherent variability in student responses and the limited linguistic resources available for fine-tuning models. This study aims to develop an automated grading system for short answers by employing a modified BERT model tailored explicitly for the Indonesian language. Our methodology involves translating the Stanford Question Answering Dataset (SQuAD 2.0) and augmenting it with domain-specific content from lecture materials, resulting in an additional 1,000 question-answer pairs. Furthermore, we optimize the model using the Optuna library for hyperparameter tuning. Experimental results show a minimum loss of 1.81 for the model, with optimized hyperparameters including a learning rate of 3.36 × 10-5, a training batch size of 8 per device, and two training epochs. The model achieves an F1-score of 69%, demonstrating a satisfactory level of accuracy comparable to or slightly exceeding typical performances on SQuAD 2.0 in English, which averages around 66%. Evaluations were conducted across various scenarios, including short-answer, long-context, and long-answer assessments. The results revealed that short answers achieved the highest cosine similarity and a perfect QWK score of 1. In contrast, long-context and long-answer scenarios yielded a QWK of 0.5, indicating a need for further improvement;however, the model shows promise for long answers with additional enhancements. This study demonstrates BERT’s potential to enhance equity and precision in grading Indonesian short answers, despite limitations in response completeness. © (2025), (International Association of Engineers). All rights reserved.

关键词： Linguistics

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ICRM: An intelligent citation recommendation mechanism based on BERT and weighted BoW models

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10135-10150页

作者： Chang, Chih-Yung Yang, Yu-Ting Zhang, Qiaoyun Lin, Yi-Ti Roy, Diptendu Sinha Department of Computer Science and Information Engineering Tamkang University New Taipei Taiwan Department of English Tamkang University New Taipei Taiwan Department of Computer Science and Engineering National Institute of Technology Shillong India

With the field of technology has witnessed rapid advancements, attracting an ever-growing community of researchers dedicated to developing theories and techniques. This paper proposes an innovative ICRM (Intelligent Citation Recommendation Mechanism), designed to automate the process of suggesting the appropriate number of citations for individual brackets within a document. The proposed ICRM comprises three phases: Coarse-grained Weighted Bag of Word (WCBW), Fine-grained SciBERT (FSB) and Citation Adjustment phases. Firstly, the WCBW phase employs TF-IDF to extract keywords from both target and candidate documents, forming vectors that capture word significance along with metadata like authorship, keywords, and titles. It aims to identify relevant papers from a database, serving as initial candidates for each bracket. Secondly, the FSB phase employs the SciBERT model to assess the similarity between candidate documents and the local context around brackets, enhancing the precision of recommendations. It refines this selection by analyzing candidate-document relationships within the proximity of the brackets. Lastly, the Citation Adjustment phase tackles overlapping citations and ensures that recommended citation numbers align with user-defined criteria, resolving issues of imbalance. The simulation results demonstrate that the proposed ICRM outperforms existing models significantly in terms of precision, recall and F1-score. © 2024 - IOS Press. All rights reserved.

关键词： Artificial intelligence

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Social issues of aging: Immersive technology solutions

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IEEE Potentials 2023年第6期42卷 12-17页

作者： Huang, Xiang-Rui Chen, Liang-Bi National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan

With the advancement of medical care and technology, human life expectancy is increasing, many advanced countries have aging societies, and the elderly have increasing needs for society to address;these have become some of the main problems to be solved in modern society (Chang et al., 2021). To solve these problems, long-term care institutions have become one of the solutions. In addition, dementia is the most important social problem among elderly individuals (Tsang et al., 2020). In most Asian countries, the solution to the problem of dementia in the elderly is to establish long-term care institutions, such as villages for elderly individuals and nursing homes. However, there is still the problem of companionship in an aging society. The needs of the elderly lie in companionship. Through family companionship, the elderly can share the joys of daily life with their families. © 1988-2012 IEEE.

关键词： Medical problems

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