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714 research outputs found
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Lung Tumor Segmentation in Medical Imaging Using U-NET
No full textTumors are a deadly condition often triggered by a range of abnormal modifications and genetic abnormalities. Early tumor diagnosis is essential due to the highly concerned nature of the disease. Early detection and treatment of tumors can significantly reduce mortality rates. This paper presents a model for tumor segmentation in medical imaging that uses the U-NET architecture to increase precision. The model’s encoding and decoding processes have been applied with skip connections to boost performance while simplifying model training. Images were cropped around the lower abdominal regions, and all images used in the study were then resized to 256*256 pixels for standardization. The proposed model deals with the class imbalance using data augmentation and oversampling. The experiments achieved a dice score of 0.853±0.02; F-score of 0.905±0.02; and a sensitivity of 0.897±0.02, compared with various existing models. As part of the model’s application, the pytorch-lightning library is used to successfully identify lung cancer scans, thereby proving to be a precise and efficient method of tumor identification. Accordingly, the study emphasizes the accuracy and speed of the applied model as a useful instrument for the earliest detection of tumors. The proposed approach helps to achieve more relevant and accurate segmentation and thus provides enhancements in medical images analysis if such challenges as an imbalance data set are well handled
Classification of Smartphone Product Reviews on E-Commerce using the Recurrent Neural Network (RNN) Method
No full textUnderstanding how consumers behave in e-commerce is essential for businesses, especially in today’s digital world where people rely heavily on online shopping platforms. A key part of this understanding comes from sentiment analysis, which looks at customer reviews to find out what buyers really think and feel about products. However, analysing these reviews is not always straightforward. Many people use informal language, slang, or mixed languages, which makes it hard for computers to interpret their opinions accurately. On top of that, there is often an imbalance in the types of data available, particularly in developing countries, where some opinions might be overrepresented while others are missing. In this study, we tackled these challenges by collecting a large number of smartphone reviews from a leading e-commerce site. We used a Recurrent Neural Network (RNN) with a bidirectional Long Short-Term Memory (LSTM) architecture, which is good at understanding the context and meaning in sequences of words. Our approach also involved optimizing the model with the Adam optimizer, using 100-dimensional word embeddings, and applying dropout regularisation to prevent overfitting. For comparison, we tested more traditional techniques, like Support Vector Machine (SVM) and Naïve Bayes, against our RNN model. By balancing the dataset with random oversampling, the RNN achieved an impressive accuracy of 95.13%, outperforming the baseline methods by 7–9%. Overall, our results highlight the potential of advanced neural network models in improving sentiment analysis for e-commerce platforms, especially in challenging environments. This research provides a practical foundation for future work in natural language processing and can help businesses better understand and respond to their customers’ needs
Sentiment Analysis of the 2024 General Election Through Twitter using Long-Short-Term Memory Algorithm
No full textThis study analyses sentiment related to the 2024 Indonesian Presidential Election using the Long Short-Term Memory (LSTM) algorithm. A total of 2,400 tweets in the Indonesian language were gathered, with approximately 400 tweets sampled per week. In the data preparation, lexicon-based sentiment tagging, oversampling for class balance, and the creation and training of an LSTM model are all included in the study approach. The built model consists of embedding layers, Conv1D, and two LSTM layers. The LSTM model was selected due to its ability to capture long-range contextual dependencies in sequential text data like tweets, facilitated by its gate mechanisms (input, forget, output) that regulate information flow. The model achieved 84.3% accuracy in classifying sentiments (positive, neutral, negative), demonstrating its potential for real-time public opinion monitoring. The results provide actionable insights for election organisers and political analysts. For further study, using a wider spectrum of data to supplement model performance will help development. Tweaking hyperparameters and playing with other architectural models like GRUs or Transformers could improve model accuracy. Improved sentiment tagging calls for a more thorough and relevant sentiment vocabulary. The proposed model can be further developed into a real-time sentiment analysis tool to provide insights into public opinion on elections and other concerns
Exploring User Perceptions of Security in Mobile Banking: A Study in Malaysia
No full textMobile banking has become increasingly popular in Malaysia due to its convenience and efficiency. However, persistent security concerns such as vulnerabilities to cyber attacks, malware and phishing affect user trust and impede adoption. This study aims to explore how users perceive and experience the security features of mobile banking applications. In spite of the implementation of security controls, understanding user perceptions and experiences remains essential, as these are core factors which influence mobile adoption. Thus, this study investigates the user experience of mobile banking security features in Malaysia. Specifically, we focus on the users’ perceptions, satisfaction levels and concerns. We employed a hybrid approach consisting of online surveys in-person interviews, and online interview sessions for data collection. This study comprises a detailed assessment of user awareness and behavior regarding mobile banking security, insights into the impact of security features on the users’ trust and recommendations for enhancing the security and usability of mobile banking applications. Our findings revealed that there were significant variations in user experiences across diverse demographic groups, with younger users exhibiting higher security concerns. Furthermore, usability issues associated with confusing navigation and slow response times were reported, negatively affecting the overall user experience. From the analysis, we noticed that while users generally rated the overall Malaysian mobile banking security applications positively, they identified key areas for improvement. These include the need for enhanced access control authentication mechanisms for secured transactions and more intuitive security interfaces for user-friendly navigation and use. In summary, this study highlights that user perceptions and experiences are central to understanding mobile banking security concerns in Malaysia. Hence, user-centric security designs are desired for balanced protection with ease of use
Editorial: AI-Enhanced Computing and Digital Transformation
No full textArtificial intelligence (AI), machine learning (ML), and advanced computing techniques are now central to the digital transformation journey, reshaping industries, academia, and society. This special issue of the Journal of Informatics and Web Engineering on AI-Enhanced Computing and Digital Transformation brings together contributions that reflect both technical innovation and societal applications. The featured articles span optimization methods, software quality improvements, data augmentation techniques, intelligent mobile applications, blockchain-based governance systems, and disaster management platforms. Together, they illustrate how computational advances not only strengthen efficiency and accuracy but also enable resilience in the face of global challenges. From a technical standpoint, metaheuristic algorithms, hybrid learning models, and refactoring strategies are pushing the boundaries of optimization and software reliability. At the data level, challenges such as imbalance, redundancy, and scalability are being addressed through novel augmentation and hybridization techniques, ensuring more robust predictions. Beyond computation, AI-powered applications are transforming healthcare, education, agriculture, and financial governance, while blockchain-based systems enhance transparency and accountability. In addition, disaster management frameworks integrating big data, hydro-informatics, and real-time monitoring are redefining preparedness in flood-prone regions. Collectively, these works showcase the breadth of AI-enhanced computing as a catalyst for systemic digital transformation, shaping a smarter, more sustainable, and interconnected future
Term Standardisation with LDA Model to Detect Service Disruption Events using English and Manglish Tweets
No full textRapid transit is one of Malaysia's most important transportation modes, where commuters use public transportation to travel. Any disruption in the rapid transit service affects their daily routines. Therefore, detecting such service disruption has become fundamental. In this study, the disruption in Malaysia's rapid transit service was assessed using English and Manglish (a combination of English and Malay) tweets through Latent Dirichlet Allocation (LDA). The gathered tweets were classified into event and non-event tweets and LDA was applied to the event tweets. Manglish event tweets were pre-processed using the proposed term standardisation technique. As a result, LDA has proved its efficiency in topic detection for both English and Manglish tweets with better performance for Manglish tweets; The best event detection rate of the LDA_English model was at the likelihood of 80% while the best detection rate of the LDA_Manglish model was at a likelihood of 60%
Implementation of Grover’s Algorithm & Bernstein-Vazirani Algorithm with IBM Qiskit
No full textQuantum logic gates differ from classical logic gates as the former involves quantum operators. The conventional gates such as AND, OR, NOT etc., are generally classified as classical gates, however, some of the quantum gates are known as Pauli gates, Toffoli gates and Hadamard gates, respectively. Normally classical states only involve 0 and 1, whereas quantum states involve the superpositions of 0 and 1. Hence, underlying principles of algorithm implementation for classical logic gate and quantum logic gate are indeed different. In this paper, we introduce significant concepts of quantum computations, analyse the discrepancy between classical and quantum gates, compare quantum algorithms using Qiskit against equivalent classical algorithms and analyse their performance in terms of runtime
Vision-Based Gait Analysis for Neurodegenerative Disorders Detection
No full textParkinson’s Disease (PD) is a debilitating neurodegenerative disorder that affects a significant portion of aging population. Early detection of PD symptoms is crucial to prevent the progression of the disease. Research has revealed that gait attributes can provide valuable insights into PD symptoms. The gait acquisition techniques used in current research can be broadly divided into two categories: vision-based and sensor-based. The markerless vision-based classification model has become a prominent research trend due to its simplicity, low cost and patient comfort. In this study, we propose a novel markerless vision-based approach to obtain gait features from participants' gait videos. A dataset containing gait videos from normal subjects and PD patients were collected, along with a control group of 25 healthy adults. The participants were requested to perform a Timed Up and Go (TUG) test, during which their walking sequences were recorded using two smartphones positioned at different angles, namely side and front. A multi-person pose estimator is used to estimate human skeletal joint points from the collected gait videos. Different gait features associated with PD including stride length, number of steps taken during turn, turning duration, speed and cadence are derived from these key point information to perform PD detection. Experimental results show that the proposed solution achieves an accuracy of 89.39%. The study's findings demonstrate the potential of markerless vision-based gait acquisition techniques for early detection of PD symptoms
Electric Vehicle Health Monitoring with Electric Vehicle Range Prediction and Route Planning
No full textThe automotive industry is experiencing a revolutionary wave due to the rapid spread of electric vehicles (EVs), which is paving the way for a fundamental and long-lasting revolution in the way we approach transportation. The global movement to reduce greenhouse gas emissions and lessen the environmental impact of traditional internal combustion engine vehicles has seen a significant boost in the popularity of electric vehicles as people come together to support environmentally conscious and sustainable mobility solutions. But the ecology surrounding electric vehicles must continue to flourish if the particular problems that EVs present are to be successfully addressed. Chief among these are the formidable foes of range anxiety and battery health management. Range anxiety is a real issue felt by many potential EV owners worry about becoming stuck because their battery has run out before reaching their destination. This psychological barrier is very noticeable and makes present and future EV owners doubtful. In addition, the longevity and health of EV batteries are essential to their continued effectiveness and affordability. The driving range and operating efficiency of the vehicle are directly affected by the gradual degradation of the battery due to several factors like aging, charging patterns, and temperature. This research presents an integrative and holistic approach to address these pressing issues, enhancing and elevating the whole EV ownership experience by combining Electric Vehicle Health Monitoring (EVHM) with Electric Vehicle Range Prediction (EVRP) and Route Planning (EVRP). Combining these three essential elements creates an all-encompassing plan created to not only lessen these enormous obstacles but also accelerate the switch to electric vehicles by giving consumers the knowledge and assurance they require for a smooth, eco-friendly, and sustainable mobility in the future
Review on Automated Storage and Retrieval System for Warehouse
No full textThe swift expansion of e-commerce and supply chain operations has significantly enhanced the efficiency of warehouse management systems, establishing them as vital components in augmenting organizations' competitiveness. This paper delves into warehouse sorting systems to enhance the sorting process, reduce error rates, and simplify internal warehouse procedures. It aims to develop a scalable, adaptable, and efficient warehouse sorting system that can maximize sorting efficiency while effectively responding to changing market demands through the use of advanced automation technologies. The study provides an in-depth review of the literature that has explored the Automated Storage and Retrieval System (ASRS) within the context of warehouse operations. It offers a comprehensive introduction to the operational systems of warehouses, detailing each type of ASRS along with the technologies that can improve the efficiency and accuracy of these systems. Moreover, the paper thoroughly investigates and classifies the ASRS design decision problem and compares multiple types of ASRS. The analysis aims to delineate the distinctions among various ASRS configurations, assessing their scalability, adaptability, and their impact on operational efficacy in warehouse environments Through this comparative review, the paper emphasizes the potential enhancements in sorting processes that modern ASRS can provide, ensuring that warehouse operations can rapidly adapt to market changes and demands. The goal is to highlight best practices and technological innovations that can lead to more responsive and efficient warehouse management systems. This exploration contributes to a better understanding of how cutting-edge automation and adaptable system designs can significantly influence the efficiency of warehouse sorting processes