Journal of Mobile Multimedia

SMART: Secured and Mobility Aware Routing Technique for Opportunistic IoT Network in Smart Cities

S. P. Ajith Kumar, Hardeo Kumar Thakur, Koyel Datta Gupta, Deepak Kumar Sharma — Fri, 29 Mar 2024 00:00:00 +0100

Transferring data between nodes in the Opportunistic Internet of Things (OppIoT) network may lead to the transmission of multiple copies of each message, which can increase communication costs and jeopardise network security. This necessitates a routing method that is effective and can address both problems. To protect transmitted data and reduce communication overhead, this study suggests a Secured and Mobility Aware Routing Method (SMART) routing algorithm for OppIoT networks in smart cities. With a buffer size of 30 MB and an overhead ratio of 27.9, the delivery probability can be increased by more than 50%. The simulation’s findings demonstrate that, in terms of delivery probability, overhead ratio, and reports, the proposed SMART protocol outperforms more traditional routing methods.

Gradient Boosting for Predicting the Relation Between Bio-medical Signals and Seizures Using LGBM and XGBoost

Bhaskar Kapoor, Bharti Nagpal — Fri, 29 Mar 2024 00:00:00 +0100

Background and aim: In recent years, research in the fields of brain-computer interfacing techniques and related areas are developing at a very rapid rate with the help of exploding of Artificial Intelligence, Machine Learning and Deep Learning. A new concept of Gradient Boosting has become popular research area among the researchers related to the field of automatic classification of Electroencephalograph (EEG) signals for predication of mental health issues like seizures.

Methods: However effective feature extraction from EEG and accurately classify them with efficient classifiers is still an important task and attracted wide attention in this area. Therefore in this paper, we presented the detailed mathematical analysis of these methods and ensemble learnings based EEG signals classification method for seizures classification in EEG using Extreme Gradient Boosting Model such as Light Gradient Boosting Machine Learning (LGBM) and XGBoost.

Results: Time-frequency domain based non-linear features are selected from preprocessed EEG Dataset, and PCA (Principal Component Analysis) is used for dimensionality reduction for features engineering, then optimized feature based training and testing is done for two class classification in ensemble learning method i.e. LGBM and XGBoost. Finally, both models are tested with dataset of University of Bonn, Germany to classify the signals.

Conclusions: In addition this paper highlights the Correlation Analysis Methodology to Identify Strong Predictor and Attributes Correlation-based Attribute Ranking for the Feature Engineering which has proved to be more efficient in EEG signals Classification and provide comparative analysis with other existing models for performance evaluation in terms of accuracy which is 87.34 and 92.31 for LBGM and XGBoost, sensitivity of 85.21 and 90.18 and specificity of 83.0 and 90.04 for LBGM and XGBoost.

Reward Based Garbage Monitoring and Collection System Using Sensors

Deepti Aggarwal, Sonu Mittal, Kamal Upreti, Pinki Nayak — Fri, 29 Mar 2024 00:00:00 +0100

Most of the time in our surroundings we come across the overfilled garbage bins near the lakes. When the bins are full, people just throw the waste here and there, which eventually goes into the lakes and pollutes the water bodies. This is because of improper dumping of garbage that is practiced in our society. With the increase in population, this problem is taking really bad shape. The prime need is to maintain a clean and healthy environment with proper disposal of waste. This paper presents a small effort to reduce this garbage problem. An Android app has been created which keeps on checking whether the dustbin is full. Also, the people will be rewarded for throwing waste into the dustbins. A QR code has been attached to the dustbin which will be scanned for rewarding the people. The dustbins use an IR sensor that detects the receiver of waste in bins. Major part of this proposed system includes the proper working of mobile application and proximity sensors. Arduino is used to maintain the proper connection with sensors and application and that is done by Bluetooth sensor. The main objective of this proposed system is to lure people to put waste into the dustbin along with the contribution towards smart city vision. This paper also gives a brief overview of the technologies and work done so far in this field.

Automated License Plate Recognition for Non-Helmeted Motor Riders Using YOLO and OCR

Chunduru Anilkumar, Meesala Shobha Rani, Venkatesh B, G. Srinivasa Rao — Fri, 29 Mar 2024 00:00:00 +0100

One of the leading causes of serious injuries in motorcycle accidents is the failure to wear a helmet, pressing the need for effective measures to encourage riders to use helmets. To stop these frequent violations of business regulations, regular observation is necessary. The proposed system is for detecting traffic violations in India related to riding motorcycles without helmets. The system utilizes deep learning-based object detection using YOLO and OCR techniques to automatically detect non-helmet riders and extract license plate numbers. The proposed system aims to improve efficiency and accuracy in detecting violations by automating the process and reducing the need for manpower. The system involves three levels of object detection: person and motorcycle/moped, helmet, and license plate. OCR is used to extract the license plate number, and all techniques are subject to predefined conditions and constraints. The system is designed to operate on video input to ensure high-speed execution, and it intends to offer a complete solution for both detection of helmet and extracting the license plate number.

A Reliable Framework for Detection of Smart Contract Vulnerabilities for Enhancing Operability in Inter-Organizational Systems

S. Arunprasath, A. Suresh — Fri, 29 Mar 2024 00:00:00 +0100

Information and communication technology based inter-organizational systems enable companies to integrate information and conduct business electronically across different parts of the organization. For organizations embracing blockchain, smart contracts provide automation and operational efficiency for inter-organizational systems. Initially utilised for financial transactions, smart contract are extended beyond banking and deployed in wide number of organizations. Smart contracts are regarded as self-executing type of contract consisting of agreement’s terms embedded directly into the code which plays a vital role in operability for inter-organizational systems, however, smart contract vulnerabilities can arise due to programming errors, leading to security issues. The effects of smart contract vulnerabilities can be significant, including loss of funds, unauthorized access to sensitive information, manipulation of data, and loss of trust in the application leading to catastrophic financial losses followed by legal implications for an organization based on blockchain technology. The goal of smart contracts exploiting vulnerabilities is to discover and eliminate potential security vulnerabilities in smart contract code prior to it being deployed. Detecting vulnerabilities in a timely manner helps to prevent financial losses, unauthorized access, and data manipulation. In order to provide a robust solution to detect vulnerabilities in smart contracts, the proposed methodology presents a novel approach for rapid detection of vulnerabilities by integrating genetic algorithm with isolation forest. Furthermore, enhancing smart contract vulnerability identification with higher accuracy and false-positive rate provides a reliable gateway for organizations to adopt blockchain.

Enhancing Gesture-Controlled Virtual Mouse and Virtual Keyboard Using AI Techniques

Jayasri Kotti, B. Padmaja, D. Deepa — Fri, 29 Mar 2024 00:00:00 +0100

Artificial Intelligence has become an essential part of modern technology. Although computer technology is advanced, it can still be improved to make it more user-friendly. One way to do this is to replace touchscreen desktops with a virtual mouse and keyboard. This can reduce the need for gadgets and enhance human-computer interaction. During the COVID-19 pandemic, reducing human intervention and dependency on devices has been critical in controlling the spread of the virus. This is where a battery-powered or Bluetooth mouse, powered by virtual reality technology, can be helpful. The virtual mouse is created using OpenCV and virtual reality technology, with the proposed system utilizing advanced tools such as MediaPipe and Python. The MediaPipe library is particularly useful in artificial intelligence projects, as it enhances the efficiency of the model. The system is an AI-based mouse and keyboard that can be controlled using hand gestures. The user interacts with the system through the camera output displayed on the screen, while the webcam serves as an input device. Python and OpenCV tools are used for implementation, making it applicable in pandemic situations and smart teaching systems. The proposed system works on Enhancing gesture Controlled Virtual Mouse and Virtual Keyboard through Virtual Assistant using AI Techniques.

Deep Dive Into Diabetic Retinopathy Identification: A Deep Learning Approach with Blood Vessel Segmentation and Lesion Detection

Fri, 29 Mar 2024 00:00:00 +0100

In the landscape of diabetes-related ocular complications, diabetic retinopathy stands as a formidable challenge, reigning as the leading cause of vision impairment worldwide. Despite extensive research, the quest for effective treatments remains an ongoing pursuit. This study explores the burgeoning domain of AI-driven approaches in ocular research, particularly focusing on diabetic retinopathy detection. It delves into various diagnostic methodologies, encompassing the detection of microaneurysms, identification of hemorrhages, and segmentation of blood vessels, primarily utilizing retinal fundus photographs. Our findings juxtapose conventional machine learning techniques against deep neural networks, showcasing the remarkable efficacy of Convolutional neural network (CNN) and Random Forest (RF) in segmenting blood vessels and the robustness of deep learning in lesion identification. As we navigate the quest for clearer vision, artificial intelligence takes center stage, promising a transformative leap forward in the realm of vision care.

Blockchain-based Traceability for Teak Identity: A Transformational Approach

Sai Woon Sheng, Santichai Wicha — Fri, 29 Mar 2024 00:00:00 +0100

The Southern regions of India, Myanmar, Thailand, Laos, and Indonesia are where teak originated. Teak is a high-value wood that used to be an export good for Thailand, bringing in a lot of money. Thailand produced approximately 71,954.53 m³ of teak plantation timber and exported wood products worth 1.1 billion baht overseas in 2018, according to the Forest Industry Organization (FIO). Due to its high demand, there is also a chance that smuggled wood from within the country or wood that has been illegally obtained abroad will enter the supply chain. Encroachment and illegal logging are still major problems in Thailand. Blockchain technology has become extremely popular due to its distinctive immutability and traceability properties, which have the opportunity to overcome a variety of issues. In order to get rid of illegal teak timber and achieve traceable, transparent, and reliable teak data that is moved through the teak supply chain, we present a decentralized application (DApp) based on the Ethereum blockchain that implements a traceability system for teak identity. According to the findings of the experiment, our DApp achieves a good trade-off between the system’s gas cost of 116K (2.53 USD) to store data in the Ethereum blockchain and provide high security, transparency, privacy, resilience, and robustness. We observed that the newly proposed blockchain-based system can reduce illegal logging, the usage of paper-based documentation, and the time needed to validate the documentation in teak supply chain controls when we compared it to the traditional process used in the supply chain.

Online Task-Based Language Learning to Enhance Thai Monks’ Speaking Performance

Sirikanya Dawilai, Natthaphon Santhi, Bhudthree Wetpichetkosol — Fri, 29 Mar 2024 00:00:00 +0100

This research aimed to achieve two main objectives: (1) to assess the learning achievements in online task-based language learning, and (2) to evaluate satisfaction with the utilization of the online task-based language learning model. The study’s population comprised Buddhist monk students from four provinces in Thailand: Chiang Rai, Phrae, Phayao, and Nan. A total of 80 participants took part in a 30-hour English language training program that focused on task-based language teaching (TBLT) implemented through online technology to enhance Thai monks’ speaking performance. Zoom and free online tools such as Pronunciation Checker App were integrated into the TBL learning cycle to deliver this online training. The research process can be summarized four stages consisting of orientation stage, pre-practicing stage, practicing stage, and post-practicing stage.

The comparison of learning outcomes before and after the implementation of the instructional model for monks revealed a significant difference in the average scores. The overall score before training was 38.78 (S.D. = 5.85), while the post-training learning outcomes had a higher average score of 47.34 (S.D. = 4.99). The assessment of satisfaction was divided into four dimensions: (1) content and language usage; (2) English instructional activities for monks; (3) teaching and learning process; and (4) development of English-speaking skills. Overall, participants expressed high levels of satisfaction across all four dimensions of the instructional design approach, with a mean rating of 4.49 and a standard deviation of 0.56.

Leveraging Knowledge Management Techniques for Developing Multimedia Exercise Guides for Elderly Fall Prevention

Suwit Wongsila, Suepphong Chernbumroong, Kritsana Boonprasit, Pradorn Sureephong — Fri, 29 Mar 2024 00:00:00 +0100

In this research study, the primary objective was to develop a comprehensive exercise program specifically designed for elderly individuals. The focus was on evaluating various exercise postures and validating their impact on muscle groups. By integrating knowledge management systems with knowledge engineering methodologies, the aim was to optimize the design of exercise postures and promote optimal health outcomes for the elderly.

Divided into three distinct experiments, the study employed a systematic approach to acquire, represent, and validate knowledge related to exercise postures for the elderly population. The use of knowledge management systems and engineering methodologies facilitated the design of effective exercise postures tailored to meet the unique needs and capabilities of elderly individuals. Experiment I focused on knowledge acquisition through structured interviews with physical therapists. The acquired knowledge was used to screen and prioritize exercise postures suitable for elderly individuals. Expert recommendations and analysis were employed to select a set of exercise postures. Using a matrix combination approach, 189 possible exercise postures were generated by combining aerobic and dance postures. Through a screening process, 52 postures were selected as suitable for elderly individuals. Experiment II utilized kinesthetic representation techniques to visually represent the 52 selected exercise postures for the elderly. Additionally, frame representation was employed to capture muscle specifications associated with each posture. The representation design was validated by physical exercise experts. In the first step of Experiment III, a total of 52 exercise postures were implemented and evaluated with elderly participants. The implementation and validation process aimed to identify the best and most appropriate postures for the elderly, considering factors such as satisfaction levels, difficulty levels, and safety considerations. Through this rigorous evaluation, the initial selection of 52 postures was narrowed down to a final set of 21 suitable postures. The validation results provided valuable insights into the effectiveness of the exercise postures and their impact on the elderly participants. It ensured that the chosen postures were not only effective in promoting optimal health outcomes but also minimized the risk of injury. The iterative assessment and refinement process contributed to the development of an evidence-based exercise program specifically tailored to the unique needs and capabilities of elderly individuals. For the second step of Experiment III, the effects of the selected exercise postures on different muscle groups were validated. Three physical exercise experts evaluated the impact on upper limb, trunk, and lower limb muscles. Specific muscle groups, such as brachioradialis, deltoid, quadriceps, and hamstring, were found to be strongly focused on during the exercises, while trunk muscles were rated as poor overall. The evaluation involved implementing and evaluating a total of 52 exercise postures with elderly participants. The selection of the final 21 postures was based on the evaluation results and provided insights into satisfaction levels, difficulty levels, and safety considerations for each exercise posture. The validation outcomes showed a high level of agreement among the experts, ranging from 79% to 91%.

For conclusion, this research aimed to enhance exercise programs for the elderly by developing more effective methodologies for designing exercise postures. By considering the specific requirements of the elderly population and utilizing knowledge management systems, the study successfully created exercise postures that maximize health benefits and overall well-being. Through the implementation of knowledge engineering methodologies and the utilization of knowledge management systems, the research optimized the design of exercise postures for elderly individuals. The division of the study into three experiments enabled a comprehensive analysis of the acquired knowledge, leading to the development of exercise postures specifically tailored to the needs of the elderly population.