International Journal of Innovations in Science & Technology
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Classification of Amputee EMG Signals Using Machine Learning Techniques
Get PDFIn the field of prosthetics and assistive technology, the accurate classification of EMG signals from amputees is of paramount importance. These signals provide insights into the intended movements of the user and are essential for designing intuitive and responsive prosthetic devices. This research is primarily centered on the meticulous classification of EMG signals using advanced machine-learning techniques. This research contributes by achieving high accuracy (95.77%, 97.36%, and 95.77%) using SVM, ANN, and CNN, respectively, on EMG signals from 11 amputees in the Ninapro database, offering an innovative approach to improve amputee assistance. We employed SVM, ANN, and CNN algorithms to classify EMG signals from 11 amputees in the Ninapro database, utilizing a robust methodology. This research yielded impressive accuracy rates of 95.77%, 97.36%, and 95.77% for SVM, ANN, and CNN, respectively, demonstrating the effectiveness of machine-learning techniques in amputee EMG signal classification. The discussion highlights the potential implications for improving prosthetic control and rehabilitation. This research presents promising results and highlights the potential of machine learning for advancing amputee assistance, opening new avenues for research and application
Applications of Artificial Intelligence in Various Traits of Life
Get PDFKnowledge Administration (KA) is the method by which an organization creates, shares, applies, and manages its information and knowledge. Although conventional KA has evolved throughout the years, documentation remains its bedrock principle. The considerable shift towards remote and hybrid working, however, has shown the limitations of conventional practices. Artificial intelligence (AI) will close these knowledge gaps and alter the ways in which KA is converted and knowledge is managed. This article reviews research on artificial intelligence (AI) and Knowledge Administration (KA), focusing on how AI can help to improve their KA strategies. In light of the existing literature critical review analyses the most up-to-date methods by analyzing both theoretical and applied works. In addition, the analytical framework presented below is useful for imagining new lines of inquiry and ways to enhance the quality of existing ones
Pragmatic Evidence on Android Malware Analysis Techniques: A Systematic Literature Review
Get PDFA large number of studies including research articles and surveys on android malware detection and analysis techniques have been presented during the last one and a half decades. The authors proposed different systems and frameworks to identify malware from software applications. However, there is no recent and comprehensive systematic literature review on the detection and analysis of android malware methods, systems, and frameworks. We present a systematic review of literature on android malware detection and analysis techniques and tools by following standard guidelines for Systematic Literature Review methodology from 2010 to 2021. We selected 75 most relevant studies out of 3343 published studies. We found that the prominent malicious datasets are Genome (39%) and Drebin (36%) used by different researchers for the detection of malware. The static, dynamic, and hybrid source code analysis methods are applied by android malware detection techniques. We also identified the limitations and future research directions of existing techniques as research gaps for the community. Based on the pragmatic evidence of this research, we have proposed a hybrid analysis-based multiple feature analysis framework. This framework will not only address the limitations of static and dynamic-based approaches, but it also analyzes evolving android malware datasets using deep neural network and machine learning techniques and improve the accuracy of evolving malware samples
Assessing the Impact of Air Pollution on Peri-Urban Agriculture in Lahore City, Pakistan
Get PDFAgriculture is a sector that is particularly susceptible to the negative impacts of air pollution due to the nature of the industry. Air pollution is a continuous global concern that significantly affects numerous sectors. In direct reaction to the increasing worries about the potential adverse impacts of air pollution on agricultural activities, we conducted an extensive research project that centered on eight specific plant varieties, namely cabbage, brinjal, bitter gourd, round gourd, okra, zinnia flower, vinca rosea, and red sunflower. The research leverages the capabilities of remote sensing to collect and analyze aerial and satellite imagery, providing a comprehensive view of agricultural areas and their interaction with air quality. To conduct an in-depth analysis of the effects that air pollution has on the expansion and maturation of plant life, these plants were grown in two distinct habitats (polluted and non-polluted) for a period of 21 and 42 days. The results of this study showed that contaminated environment cause plants to experience serious morphological and physiological disruptions. These disturbances included inhibited growth, disrupted photosynthesis, and modifications in leaf characteristics such as leaf area, leaf area index, and CF. All these factors have major consequences for food security and ecological balance. These measurements were employed to assess the impact of air pollution on the well-being of plants and their potential productivity. The outcomes of this study highlight how important it is to adopt comprehensive measures to reduce air pollution immediately. It highlights the significance of strong emissions controls, sustainable urban planning, and measures to reforest the earth. By integrating remote sensing technology, we can effectively monitor land use patterns, detect changes in vegetation health, and evaluate the spatial distribution of air pollutants. By addressing the root causes of air pollution and devising solutions, we can safeguard the agricultural sector, enhance environmental health, and ensure a sustainable future for ecosystems and human well-being
Mindfulness Based Intervention for 21-Year-Old with Substance Use
Get PDFSubstance Use Disorders (SUDs) are a global psychiatric problem associated with a high mortality and morbidity rate. Consequently, contemporary advances in addiction have generated the potential for assessing the efficacy of Mindfulness Based Interventions (MBIs) for treating those with SUDs and their Relapse Prevention. The current case study focuses on a 21-year-old married man referred with the presenting complaints of intake and withdrawal of Heroin. His symptoms fulfilled the criteria of Heroin Withdrawal Disorder and he is currently in a controlled environment. The assessment was carried out through a clinical interview with the client, behavioral observation, Mental Status Examination (MSE) and the subjective rating of symptoms. Formal assessments were also carried out. The management plan was devised to build and maintain an excellent therapeutic alliance. The psychotherapeutic intervention was applied, primarily focusing on the use of MBI that improved symptoms
Usability Evaluation of Facebook and Instagram by Visually Impaired People
Get PDFIntroduction/Importance of Study:
Social networking websites have become the main medium for communication, information sharing, entertainment, buying/selling, and various other purposes. People of every age use social networking websites, and their usage is increasing daily, especially in current circumstances. People with different abilities also used social networking websites, but each set of users had their requirements for using these websites. Visually impaired people use computers and the web with the help of screen reading tools e. g.; jaws, and NVDA. Screen reading tools read a web page sequentially, which was a time-consuming process. The major problem with screen reading tools came while reading visual content. Screen reading tools only read the alternate texts of non-visual content behind their tag. This research focuses on the usability of social networking websites for visually impaired people. Two of the most commonly used social networking websites, Facebook and Instagram, were selected for the usability evaluation. Accessibility, efficiency, and effectiveness were the metrics of usability, which were evaluated in this study.
Novelty statement:
A consolidated set of guidelines specific to social networking websites were presented in which some new guidelines were also proposed for Facebook. A mock interface was developed based on the proposed guidelines for Facebook.
Material and Method:
`For the evaluation of usability, a controlled experiment was conducted with 28 visually impaired people in which 16 participants evaluated Facebook and 12 evaluated Instagram to find the usability problems faced by visually impaired people.
Result and Discussion:
Results show that Instagram was as easy to use as compared to Facebook when used by visually impaired people with the help of screen reading tools.
Concluding Remarks:
Results showed that Facebook was difficult to use in comparison to Instagram. Thus, new guidelines were proposed for Facebook, and based on the guidelines, a prototype was proposed
An Automated Framework for Corona Virus Severity Detection using Combination of AlexNet and Faster RCNN
Get PDFCoronavirus has affected daily lives of people all around the globe. Lungs being the respiratory organ are the most affected by such a virus. Alternative techniques for diagnosing the coronavirus involving X-rays and CT scans of the chest have been proposed. The severity of the disease, on the other hand, is a crucial component in the patient\u27s treatment. As a consequence, an automated approach to ascertain the severity of the coronavirus on the lungs is designed to decrease the impacts of the coronavirus on the lungs and practice the right treatment. In this manuscript, we proposed a deep learning-based model for identifying the severity level of coronavirus on the lungs which is further categorized in high, moderate, and low. We employed AlexNet for the disease detection and Faster RCNN for the severity level prediction based on the affected area of the lungs. The evaluation is assessed using X-rays and CT scans of the lungs. Total 1400 images have been employed for the training and performance evaluation of the proposed system. The metrics that we considered for the performance evaluation are accuracy, precision, recall, error rate, and time. The results showed that our proposed model attained about 98.4% accuracy and 98.15% precision.
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Detection of Coronary Artery Using Novel Optimized Grid Search-based MLP
Get PDFIn recent years, we have witnessed a rapid rise in the mortality rate of people of every age due to cardiac diseases. The diagnosis of heart disease has become a challenging task in present medical research, and it depends upon the history of patients. Rapid advancements in the field of deep learning. Therefore, it is a need to develop an automated system that assists medical experts in their decision-making process. In this work, we proposed a novel optimized grid search-based multi-layer perceptron method to effectively detect heart disease patients earlier and accurately. We evaluated the performance of our method on a dataset named Public Health dataset for heart diseases. More specifically, our method obtained an accuracy of 95.12%, precision of 95.32%, recall of 95.32%, and F1-score of 95.32%. We made a comparison of our method with existing methods to check superiority and robustness of our system to detect heart disease patients. Experimental results along with comprehensive comparison with other methods illustrate that our technique has superior performance and is robust to detect heart disease patients. From the results, we can conclude that our method is reliable to be used in hospitals for the early detection of heart disease patients.
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IMU Aided GPS Based Navigation of Ackermann Steered Rover
Get PDFGPS signal loss is a major issue when the navigation system of rovers is based solely on GPS for outdoor navigation rendering the rover stuck in the mid of the road in case of signal loss. In this study, a low-cost IMU aided GPS-based navigation system for Ackermann Steered mobile robots is presented and tested to cater to the issue of GPS signal loss along. GPS path is selected and fed using the android application which provides real-time location tracking of the rover on the map embedded into the application. System utilizes Arduino along with the node MCU, compass, IMU, Rotary encoders, and an Ackermann steered rover. Contorller processes the path file, compares its current position with the path coordinates and navigates using inertial sensor aided navigation algorithm, avoiding obstacles to reach its destination. IMU measures the distance traveled from each path point, and in case of signal loss, it makes the rover move for the remaining distance in the direction of destination point. Rover faced a sinusoidal motion due to the steering, so PID was implemented. The system was successfully tested on the IST premises and finds its application in the delivery trolley, institutional delivery carts, and related applications
Analysis of Job Failure Prediction in a Cloud Environment by Applying Machine Learning Techniques
Get PDFCloud Services are the on-demand availability of resources like storage, data, and compute power. Nowadays, cloud computing and storage systems are continuing to expand, there is an imperative requirement for CSP (cloud service providers) to ensure a reliable and consistent supply of resources to users and businesses in case of any failure. Consequently, the large cloud service providers are concentrating on mitigating any failures that transpire in a cloud system environment. In this research work, we examined the bit brains dataset for the job failure prediction which keeps traces of 3 years of cloud system VMs. The dataset contains data about the resources used in a cloud environment. We proposed the performance of two machine learning algorithms which are Logistic-Regression and KNN. The performance of these ML algorithms has been assessed using cross-validation. KNN and Logistic Regression give the optimal results with an accuracy of 99% and 95%. Our research study shows that using KNN and Logistic Regression increases the detection accuracy of job failures and will relieve cloud-service providers from diminishing future failures in cloud resources. Thus, we believe our approach is feasible and can be transformed to apply in an existing cloud environment