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714 research outputs found
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Facilitating Digital Experience Sharing Among Vehicles through Utilisation of Pre-existing Communication Infrastructure
No full textVehicular communication applications are expanding quickly because of the approach of related technologies, such as vehicular cloud and the Internet of Vehicles (IoV). The combination of the Internet of Things (IoT) and smart transportation is the Internet of vehicles. Data related to infotainment, safety, and effectiveness with different vehicles and the Sustainable framework of vehicles can be exchanged. However, after the appearance of such empowering advancements, still a huge number of ideas need research. Data sharing (related trips and navigation) of new/old models and other new/old model vehicles with the owner's agreement should be taken care of. This paper proposes a novel technique which is a digital experience-sharing system. With the proposed system, vehicles can share their experience with different vehicles depending on the owner's authorizations. The technique of digital experience sharing will give vehicles the ability to share and reestablish past information and data (related trips and navigation). A traffic trace containing the information of the vehicle: longitude, latitude, trip information, time, and location. Open street map (OSM) and simulation of urban mobility (SUMO) tool have been used for the simulation of the proposed technique. Further, the structure of the message, for productive communication is provided with implementation details in this work. Additionally, the application is used in the vehicle, and the information related to the start, stay, and end points of the journey is stored on a cloud. After some time, the same place is visited by the same vehicle, and a notification about previous visit information is displayed by the application
Developing A Predictive Model for Football Players’ Market Value Using Machine Learning
No full textFootball is the world’s most popular sport, and evaluating the market value of players is crucial for clubs and managers in making informed decisions regarding transfers, contracts, and financial planning. This study aims to develop a predictive model to estimate the market value of football players using machine learning (ML) algorithms and real-life statistics performance data from the top five European leagues such as English Premier League, Italian Serie A, Spanish La Liga, German Bundesliga, and French Ligue 1 between the 2017/18 and 2019/20 seasons. By reviewing past research, various ML methods such as Random Forest, LightGBM, XGBoost, and Gradient Boosting Decision Tree (GBDT) are developed. Data preprocessing techniques, including data cleaning, feature selection, feature encoding, splitting, and standardization, are applied to ensure data quality and consistency. To tune the hyperparameter of the models, RandomizedSearchCV is applied alongside cross validation. The model evaluation is conducted using regression metrics such as mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R²), to determine the most accurate model. The best-performing model is further utilised to analyse the correlation between the features and market value, offering insights into the key features that significantly impact the market value for each position
Addressing IoT Security Challenges through Advanced Machine Learning and Encryption
No full textThe rapid growth of Internet of Things (IoT) devices, including smartwatches, home assistants, and connected appliances, has brought significant convenience to daily life, but it has also introduced serious security challenges. These devices often transmit sensitive data, making them vulnerable to theft, misuse, and unauthorized access. Current security measures are insufficient to address the complex and evolving nature of IoT systems, leaving many of them exposed to potential breaches and cyberattacks. This review explores recent developments in IoT security, focusing on how advanced technologies, such as machine learning, can be utilized to enhance the protection of IoT systems. The main objective of this paper is to examine potential solutions to the security problems that arise in IoT environments. It includes a thorough analysis of recent research and technological innovations in the field, with a particular emphasis on how different security methods are applied across IoT systems. By identifying the most common security vulnerabilities and outlining their impact on IoT networks, the review suggests improved methods to safeguard IoT data and ensure privacy. The findings aim to support researchers, developers, and businesses in designing more secure IoT solutions, and contribute to the establishment of stronger data protection policies. Ultimately, the review serves as a resource for those seeking to enhance the security of IoT devices and systems in an increasingly interconnected world
Simulation based Analysis of Encoder Resolution on Differential Drive AMR Odometry
No full textThis research explores the impact of encoder resolution on the odometry accuracy and navigational performance of a differential-drive Autonomous Mobile Robot (AMR), using the Automated Trash Mobile Robot (ALTO) as a test platform. Encoder pulse-per-revolution (PPR) values ranging from 40 to 4096 were simulated in Gazebo. A custom encoder and odometry simulation algorithm were developed and integrated into the ROS1-based navigation stack. Controlled experiments—including straight-line, rotational, and dynamic path tests—were conducted in virtual environments to compare positional accuracy using /odom, /amcl_pose, /global_pose, and /world_pose. Results showed that higher PPR values improved odometry precision, particularly in orientation estimation, but had limited influence on global pose accuracy under AMCL-based sensor fusion. While lower resolutions caused noticeable drift, AMCL maintained robust localization. The findings offer practical guidance for optimizing encoder selection, balancing cost and performance in industrial AMR deployments.
Manuscript received: 30 Jun 2025 | Revised: 8 Aug 2025 | Accepted: 16 Aug 2025 | Published: 30 Nov 202
Forecasting High-Risk Traffic Zones Using Machine Learning for Enhanced Road Safety
No full textRoad traffic accidents continue to pose serious global public health and economic challenges. In Malaysia alone, traffic-related incidents caused an estimated RM25 billion in losses in 2023. This study presents a two-part machine learning framework: Part A focuses on predicting accident severity, while Part B uses these predictions to forecast high-risk traffic zones through spatial and temporal analysis. Accident data from 2023 was selected from the UK Road Safety dataset to reflect current traffic patterns, infrastructure, and enforcement efforts. Five classifiers, Logistic Regression, Decision Tree, Random Forest, XGBoost, and K-Nearest Neighbors, were trained and evaluated. A stacking ensemble combining the top three models was constructed to enhance predictive accuracy. The models were assessed using accuracy, precision, recall, and F1-score, with results showing that the ensemble method outperformed individual classifiers. The findings demonstrate the potential of ensemble learning in identifying high-risk zones and supporting proactive road safety planning.
Manuscript received: 3 Aug 2025 | Revised: 21 Sep 2025 | Accepted: 28 Sep 2025 | Published: 30 Nov 202
Hybrid Phishing Detection Model: Integrating BERT with TF-IDF for Enhanced Email Security
No full textPhishing emails remain a major cybersecurity problem because they cleverly exploit our natural trust by impersonating real messages. While standard NLP methods like TF-IDF and FastText are efficient, they often miss the subtle, contextual tricks found in today's sophisticated phishing attempts. On the other hand, advanced deep learning models like BERT are fantastic at understanding context, but they require a lot of computational power. In this paper, we suggest a hybrid solution. We merge the lightweight, statistical strengths of TF-IDF with the deep contextual power of BERT's embeddings to create a more robust phishing detection system. To test this, we ran experiments on datasets of 1,000, 5,000, and 10,000 emails, putting five different models head-to-head. Our results were clear: the hybrid models consistently beat the single-method ones. Interestingly, the TF-IDF + BERT combo was the most accurate on the smaller dataset (1,000 samples). However, for larger datasets (5,000 and 10,000 samples), TF-IDF + FastText offered the best balance of accuracy and speed. While the BERT hybrid was slightly more accurate, its slower processing time is a real hurdle for scaling up. We believe our proposed framework offers a practical and effective tool for real-world cybersecurity teams.
Manuscript received: 3 Jul 2025 | Revised: 25 Aug 2025 | Accepted: 7 Sep 2025 | Published: 30 Nov 202
Improve Exercise Movement: Detecting Mistakes on Yoga with Mediapipe and MLP
No full textYoga is known as a comprehensive practice for maintaining physical and mental health. However, improper execution of yoga postures can cause injury, hinder progress, and potentially damage health. To overcome this problem, this research utilizes Mediapipe as a data preprocessing tool to identify yoga poses, which are then classified using the Multi-Layer Perceptron (MLP) algorithm. In the process, data normalization is carried out to increase prediction accuracy. This research uses a dataset consisting of six classes of yoga poses, namely tree, downdog, goddess, warrior, and plank. Experimental results show that the model achieved 98% accuracy during training, but accuracy during testing decreased to 95%. This shows an indication of overfitting, where the model adapts too much to the training data and is less able to generalize to the test data. This study makes an important contribution to the development of a safer and more accurate yoga pose classification system, which can be applied to practice yoga properly and prevent injuries.
Manuscript received: 15 Oct 2024 | Revised: 30 Jan 2025 | Accepted: 11 Feb 2025 | Published: 31 Mar 202
A Review of Camouflage Object Detection Techniques
No full textCamouflage Object Detection (COD) is a constantly evolving field that deals with the difficulties of locating items hidden in intricate settings. This review examines the progression of COD techniques, from classical human methods to physical component-based methods such as infrared, LIDAR, multispectral and hyperspectral detection. Key applications of COD span from military reconnaissance to wildlife monitoring, medical imaging, and disaster response, where the ability to detect concealed objects has transformative implications. Future research should prioritize integrating diverse data sources, refining machine learning algorithms, and overcoming deployment constraints to advance the field further.
Manuscript received: 30 Dec 2024 | Revised: 30 Jan 2025 | Accepted: 17 Feb 2025 | Published: 31 Mar 202
Real-Time Emotion Detection Using Artificial Intelligence: A Review
No full textThe integration of artificial intelligence (AI) in emotion recognition has significantly transformed human-computer interaction and revolutionized fields such as medicine, education, and entertainment. This paper reviews 30 papers on the detection of emotional signs through various biometric inputs, including electroencephalography (EEG), electrocardiography (ECG), facial expressions, and speech patterns. Despite advancements in AI-driven emotion recognition systems, challenges persist, particularly in data variability, computational inefficiency, and ethical dilemmas associated with privacy, security, and algorithmic bias. Recent innovations in feature extraction techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have enhanced the precision of emotional state recognition across multiple input channels. The transition to edge computing has further enabled real-time processing with low latency, facilitating integration into wearable devices and IoT ecosystems. Multimodal systems, which leverage data sources such as physiological signals, facial expressions, and speech, show great promise but face challenges related to inclusivity and system fragility. To address these issues, the study recommends for robust training datasets, ethical guidelines, and hardware optimizations. Incorporating contextual information and accounting for individual differences can improve recognition accuracy and user trust. However, ethical concerns remain critical, emphasizing the need for strict standards of privacy, security, and equitable access to ensure AI emotion recognition systems are trustworthy and inclusive. Overall, this paper highlights the potential of AI-driven emotion recognition systems while underscoring the importance of continuous research to address technical and ethical challenges, paving the way for broader applications in pattern recognition, cognitive studies, and specialized tools.
Manuscript received:4 Jan 2025 | Revised: 13 Feb 2025 | Accepted: 27 Feb 2025 | Published: 31 Mar 202
A Comprehensive Review on Machine Learning-Based Job Recommendation Systems
No full textA dynamic, constantly shifting labor market creates enormous job postings, overwhelming candidates and making it difficult for businesses to find quality candidates. It is also hard for job seekers to find suitable jobs. Addressing these issues, machine learning-driven job recommender systems have recently become an essential tool using predictive models to improve the match between jobs and candidates. A hybrid design that combines collaborative filtering with content-based filtering and adds contextual information like geographic location, industry trends, and user behavioural data can enhance the accuracy and relevance of recommendations. This paper reviews and critically analyzes contemporary job recommender system techniques. The focus is on hybrid recommendation models and the integration of algorithmic approaches, indicating their strengths and weaknesses. This review also looks into the evaluation metrics like precision, recall, normalized discounted cumulative gain (NDCG), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). To provide an overall perspective of the various approaches employed and the performance trade-offs inherent therein, this paper hopes to shed some light on the optimization of job recommendation systems for better effectiveness and user satisfaction.
Manuscript received:19 Mar 2025 | Revised: 29 Apr 2025 | Accepted: 10 May 2025 | Published: 30 Jul 202