International Journal of Emerging Engineering and Technology

Design and Implementation of Brain Controlled Electric Wheel Chair for Quadriplegic Persons

2023-09-08T10:57:11+00:00

Quadriplegia refers to a condition where the body experiences paralysis below the neck, affecting the trunk, legs, and arms. Globally, the World Health Organization (WHO) reports that around 5.4 million individuals are afflicted by quadriplegia. Offering secure and independent mobility to such a substantial population aligns with the United Nations' (UN) third sustainable development goal of promoting good health and well-being. Addressing this challenge demands innovative, and user-friendly solutions. To tackle this pressing issue, this paper introduces a hardware-based implementation of a brain-controlled electric wheelchair tailored for quadriplegic individuals. The proposed system comprises an electric wheelchair, an electroencephalogram (EEG) headset, a Bluetooth module (HC-05), a controller, and gear motors. The EEG headset captures brain signals, and transmits them to the controller via Bluetooth. The controller communicates with motor drivers, propelling the wheelchair in desired directions: left, right, forward, or backward-based on the extracted brain signal information. For a comprehensive solution catering to a wider audience of individuals with disabilities, the manuscript also uses joystick-controlled wheelchair, a cost-effective option for those with leg impairments. The implemented solution stands out for its speed, user-friendliness, and safety, incorporating a collision avoidance system to ensure user well-being.

Heart Disease Prediction Using Hybrid Random Forest and Linear Model

2023-08-31T14:37:25+00:00

Heart disease remains a significant global health concern, and accurate heart disease risk prediction plays a crucial role in its prevention. In recent years, machine learning techniques have shown promising results in cardiovascular disease prediction. This paper proposes a novel Hybrid Random Forest and Linear Model (HRFLM) that leverages diverse patient attributes to accurately predict the risk of developing heart disease. HRFLM utilizes a combination of feature engineering, feature selection, and an ensemble of machine learning algorithms to effectively capture the complex relationships between patient characteristics and heart disease outcomes. The model is trained and validated on a comprehensive dataset from the Framingham Heart Study, which includes a wide range of demographic, clinical, and lifestyle variables. The experimental results demonstrate that HRFLM
outperforms several state-of-the-art machine learning models like K-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP) and random forest (RF) in terms of accuracy, precision, recall, and F1 score. The proposed model provides valuable insights into the risk factors associated with heart disease and can assist doctors in identifying individuals at high risk for preventive interventions. The proposed model has achieved an accuracy of 88%.

Analysis of Strength and Flight Time for Hexacopter Fire Fighting Drone with Fire Extinguisher Ball

2023-09-25T07:52:36+00:00

This research article is aimed to contribute to the improvement of human life by addressing the growing number of fire incidents caused due to increasing human population and advances in modern technology. This study proposes the use of drone with a hexagonal design configuration for firefighting purpose. Two design concepts for the firefighting drone were proposed. The 3D models were developed for both the designs using SolidWorks. The design 1 was made to carry an FEB of 0.5 Kg and design 2 was made to carry 1.3 Kg FEB. The strength of drone structure was analyzed using Ansys software. The static structural analysis showed the maximum total deformation and stresses on the arms while minimum total deformation and stresses on the top and bottom plates in both design 1 and design 2. The dynamic analysis performed for 0.0002 seconds of flight time. The total deformation was observed to be maximum on the arms and the bottom plate while minimum on the top plate of the drone. However, the stresses were observed to be maximum on the arms and minimum on the top and bottom plates. A prototype was developed based on design 1 for experimental purpose. The drone's flight control and stability were managed by a Pixhawk 2.4.8 flight controller, alongside GPS for precise navigation. A wireless radio telemetry system facilitates communication between the drone and a mobile phone or PC, with power supplied by a 5200mAh capacity battery. To carry out firefighting operations, the drone was equipped with a wooden box that utilized servo motors to drop a fire extinguisher ball accurately from an initial point to a designated target location. In conclusion, the research proposes an efficient and cost-effective solution to combat fire incidents using a drone. By integrating advanced technologies and equipment, the drone demonstrates the potential to protect the environment and preserve life from the destructive effects of fire.

Demand Response-Based Energy Management Strategy for University Micro Grid Using Modified Optimization Algorithm

2023-09-19T08:37:03+00:00

Scheduling university appliances, energy batteries, and distributed generation like PV in a stochastic setting required the development of an optimization model. The research proposes an optimization approach to the micro-grid of the university campus considering various loads. Research considers that the energy management system can work with renewable energy sources like PV and storage. The merged approach of MILP and PSO has been used to solve the optimization problem, and the results have been presented comprehensively. This research investigation explores the process of creating an EMS for a prosumer microgrid with a centralized energy storage system and distributed generation (DG) installed on-site. The proposed EMS optimizes energy flow between μG and the utility network and schedules energy storage charging and discharging to minimize energy costs. The operational optimization of the energy hub has been modelled using MATLAB software, and the approach could be modified to find the optimal solution for various energy hubs with little modifications.

A Feature Fusion Based System for Brain Tumor Classification

2023-08-28T18:12:44+00:00

classifying brain tumors is an exclusive and difficult task in the field of clinical image analysis. Radiologists were able to reliably detect tumors using machine learning algorithms without the need for extensive surgery. However, a number of difficulties arise, including the difficulty in locating a specialist with competence in the field of identifying brain malignancies using images using deep learning models, and the main issue in the erection of the most effective deep learning system for diagnosing tumor cells. We used deep learning and adaptive algorithms to build a sophisticated and incredibly accurate system that uses feature fusion to automatically categorize brain tumors. The proposed framework extracts deep features from CNN architectures with varying depths and designs. The features from highest performing CNN architectures are then fused to form a single vector which is classified using SVM and KNN. The novel vector obtained the highest accuracy of 92% via the feature fusion method. Therefore, the suggested framework can be successfully used in clinical settings to categorize three different forms of tumors, namely gliomas, meningiomas, and pituitary tumors, from medical imaging.

BlockYards: A Blockchain-Powered System for Secure Real Estate Transactions

2023-09-09T08:05:11+00:00

The real estate industry holds a paramount position within Pakistan, propelled by the surging trends of urbanization and globalization. Despite its growth, challenges persist, encompassing property scams, fraud, and unauthorized property claims, transactions, and holdings. Consequently, ensuring the security and credibility of real estate dealings remains pivotal. Various web and app solutions have emerged to market real estate properties, yet none of them has established an online platform for verified transactions. Presently, both sellers and buyers are compelled to navigate cumbersome procedures for property registration and ownership documentation, typically conducted in person, lacking instantaneous and authenticated online validation. Addressing these limitations, BlockYards has devised a solution by immersing comprehensive property information and transaction data within a publicly accessible Ethereum Blockchain. This innovation benefits all stakeholders, including users and law enforcement agencies, facilitating the tracking of an individual's or organization's assets. Public transparency is achieved as a public Blockchain exposes the activity and history of asset transfers associated with a specific hash on the network. Employing a consensus mechanism, this technology safeguards the integrity of asset transactions by ensuring that once a transaction record garners verification from the majority of nodes on the network, it becomes unalterable, resistant to updates or hacking, thus establishing immutable logs. Furthermore, BlockYards integrates a tool fueled by Machine Learning decision models that accurately predict the real value of a property based on comprehensive property attributes. This feature eradicates the practice of inaccurate real estate price estimations, empowering users to confidently list their property values. In essence, BlockYards empowers users to trade properties at their genuine worth once certain conditions are met, normalizing transparency within the online property market through the application of Blockchain's potent capabilities.

Lightweight Message Authentication Protocol for Low-Resource IoT Devices

2023-10-03T08:59:03+00:00

The

Internet of Things (IoT) is a system of physical objects attached to software and other technologies that allow them to connect to and exchange information between devices and systems over the Internet. There is a framework in the Internet of Things (IoT) in which devices are usually outfitted with wireless sensor nodes to connect several physical devices over the medium to obtain data without human intervention. Message Authentication in low-resource devices such as sensors is inefficient with heavy protocols because it quickly drains the battery. In our proposed scheme, we used CRC. We compared it with other hashing protocols to introduce a lightweight message authentication protocol for low-resource devices in various fields, such as healthcare and daily life gadgets.

Enhancement of Embedded Topic Model

2023-09-22T05:30:49+00:00

This comparative study aims to examine the performance of the Embedded Topic Model (ETM) in producing coherent topics when applied to noun-restricted corpora. As nouns in any dataset are the most informative features so involving them will improve the topic quality. To evaluate this hypothesis, we compare the performance of two topic models: ETM (Embedded topic Model) and LDA (Latent Dirichlet allocation) on three variations of the dataset. The first dataset version is the original pre-processed dataset, while the second version consists of the dataset reduced to noun phrases only, the third version represents the dataset reduced to nouns only. To assess the performance of both models, we employ two widely used measures: Topic Coherence (TC) and Topic Diversity (TD). The experimental results revealed that the embedded topic model outperforms LDA across all the variations of the datasets. Remarkably it exhibits exceptional performance for the dataset having only nouns. In addition, the time to train the model is also reduced when the vocabulary is reduced to nouns only. Overall, this paper presents evaluations to show how the Embedded Topic Model significantly improves topic quality, especially in noun-restricted contexts. These findings provide insightful information for researchers and practitioners in the area regarding the possible advantages of using noun-based corpus reduction strategies in topic modeling tasks.

Detecting and Analyzing Deceptive Information in News Articles: A Study Using a Dataset of Misleading Content

2023-09-28T10:36:52+00:00

Due to the widespread growth of social and traditional media in the last decade has led to the huge spread of information. Nowadays these platforms are playing a vital role in everyone’s life. Many people use these platforms to share and discuss different ideas and issues related to almost every field. Through the internet, getting the most recent news at their fingers has become simpler. However, in this dissemination of information, it has been noted that there is some misinformation and fake news circulating with no relevance to reality which can cause serious problems in political and social aspects. The main goal of this study is to find the ideal learning model for achieving high accuracy and performance. In this regard, this study offers a state-of-the-art dataset which is the extension of fake and real news datasets called the Misleading Content dataset. To analyze the performance of the dataset three different machine learning algorithms, Random Forest, Naïve Bayes (NB) and Support Vector Machine (SVM), and three different deep learning, Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) models are being used. BiLSTM performs well and provides promising results along with 88.54% accuracy.

Collision Avoidance of Autonomous Driving at Low Speed in the Near Field of Vehicle

2023-09-14T17:29:59+00:00

This paper aims to propose a new idea for realizing low-power-consumption, real-time, microcontroller-based, redundant embedded collision avoidance systems in autonomous driving applications. When operating a fully automated vehicle, the vehicle generates a driving trajectory based on the global route to the destination. The car must follow the generated driving path. It is essential to ensure the safety of this path by checking that it is collision-free. The goal of our low-level embedded collision avoidance system is to guarantee the safety of the path. After defining the driving path area associated with the generating path and the safe monitoring distance, the system can monitor the vehicle's defined Keep-Out-Area (KOA) by using 3D Light Detection and Ranging (LiDAR) sensor. Considering the relatively limited computing power of the microcontroller, the KOA is calculated offline and stored in a look-up table (LUT). This paper also introduces an experimental hardware platform based on the proposed system concept. This platform can facilitate the testing of various collision avoidance algorithms. Moreover, we also identify the challenges, such as false positives and deviation from the actual driving path.