Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outs...
Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, “Smart Shoes for Blind and Visually Impaired People”, and “Human posture monitoring device”. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy.
Since the last few decades, the prey-predator system delivers attractive mathematical models to analyse the dynamics of prey-predator interaction. Due to the lack of precise information about the natural parameters, a...
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The methods of nonlinear adaptation based on an analytical design of aggregated regulators and modal control are discussed for solving the problem of nonlinear control over a robotic arm operating under the conditions...
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Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pai...
Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant.
Let F be a class of group and G a finite group. Then a set Σ of subgroups of G is called a G-covering subgroup system for the class F if G ∈ F whenever Σ ⊆ F. We prove that: If a set of subgroups Σ of G contains a...
—The mathematical model for many problems is arising in different industries of natural science, basically formulated using differential, integral and integro-differential equations. The investigation of these equati...
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The bipolar fuzzy set and interval-valued bipolar fuzzy set efficiently analyse real-world problems where for each input of an object, there has counter information. This study's main objective is to lay a foundat...
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In the era of the knowledge-based economy, the active branch of information technology plays a crucial role. The enterprise administration covers efficient changes, and it has been entered in the age of reasonable man...
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Sleep disorders have a significant impact on physical health, cognitive ability, as well as quality of life. However, they are still underdiagnosed due to the practical limitations of traditional diagnostic methods, s...
Sleep disorders have a significant impact on physical health, cognitive ability, as well as quality of life. However, they are still underdiagnosed due to the practical limitations of traditional diagnostic methods, such as polysomnography. Polysomnography has been accepted as the gold standard with a good accurate diagnostic value. However, it is laborious, time-consuming and frequently unavailable, particularly in the setting with limited resources. To address these challenges, this study aims to improve sleep disorder classification using machine learning (ML) techniques with metaheuristic optimization strategies. The study utilizes the Sleep Health and Lifestyle Dataset, which includes demographic data, sleep parameters, lifestyle factors, and cardiovascular indicators. To improve model efficiency and predictive accuracy, the binary Al-Biruni Earth Radius (bBER) optimizer is applied for feature selection, effectively reducing data dimensionality by eliminating irrelevant and redundant features. Among the evaluated models, the multilayer perceptron (MLP) achieved the strongest baseline performance, reaching an accuracy of 89.92 % and a sensitivity of 91 % after feature selection. Further optimization through the BER algorithm led to the development of the BER-MLP model, which delivered superior classification results, achieving an accuracy of 95.41 % and a sensitivity of 92.45 %. These findings reinforce the effectiveness of integrating metaheuristic optimization with machine learning approaches to enhance diagnostic accuracy and reliability in sleep disorder detection.
The algebraic structures have many applications in coding theory, cryptography, and security networks. In this paper, the notion of hybrid subalgebras of BCH-algebras is introduced and related properties are investiga...
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