Proposed System Research Articles

Exploring the Conceptual Framework of Mobile Learning Technology

The idea of introducing new technologies to education is not new, and as a result, research on this topic has been carried out for years. However, the rate of introduction of new technologies in higher education in Uzbekistan is very slow. The purpose of this article is to propose a conceptual framework for the development of mobile learning, in particular, the introduction of mobile applications into the educational process in primary education. This goal is achieved by analyzing the existing measures and the existing foundations of mobile education, which are used to improve the quality of education through the active use of information and communication technologies. The proposed system includes three main elements: applied computing, information systems, and theory of computation. It is important that each of them is done correctly and that there is continuity between them.

Performance evaluation and optimization of a multigeneration hybrid system for power, hydrogen, cooling, and freshwater production from renewable sources

Performance evaluation and optimization of a multigeneration hybrid system for power, hydrogen, cooling, and freshwater production from renewable sources

FruitScan Pro : Fruit Disease and Quality Detection Application

This research paper proposes a revolutionary approach to agriculture by combining the latest image processing technologies with machine learning. The agricultural industry, which is vital for India’s economy, needs efficient methods for monitoring fruit health. Manual monitoring is time consuming and often inadequate. In this paper, we propose a smart farming approach using image processing techniques. This approach improves image quality, segmenting images, extracting features, and classifying diseases and their cures. Fruit diseases are a major economic challenge around the world. Therefore, it is necessary to identify them quickly. Our proposed system offers fast and automatic disease detection. Preprocessing, extracting features, classifying, training/testing. The results prove its effectiveness for accurate disease detection and solutions. As the demand for agricultural productivity increases, this method will be a valuable tool for increasing fruit production with minimum human intervention. Image processing for effective implementation. Key Words: Fruit Disease, Solution, Image Preprocessing, Feature Extraction, Classification.

Bi-level Analysis of Computed Tomography Images of Malignant Pleural Mesothelioma: Deep Learning-Based Classification and Subsequent Three-Dimensional Reconstruction

Abstract A bi-level analysis of computed tomography (CT) images of malignant pleural mesothelioma (MPM) is presented in this paper, starting with a deep learning-based system for classification, followed by a three-dimensional (3D) reconstruction method. MPM is a highly aggressive cancer caused by asbestos exposure, and accurate diagnosis and determination of the tumor’s volume are crucial for effective treatment. The proposed system employs a bi-level approach, utilizing machine learning and deep learning techniques to classify CT lung images and subsequently calculate the tumor’s volume. The study addresses challenges related to deep neural networks, such as the requirement for large and diverse datasets, hyperparameter optimization, and potential data bias. To evaluate performance, two convolutional neural network (CNN) architectures, Inception-v3 and ResNet-50, were compared in terms of their features and performance. Based on CT images, the second stage incorporates 3D volume reconstruction. The process is carried out by cropping, registering, filtering, and segmenting images. This study demonstrated the efficacy of the developed system by combining CNN optimizations with 3D image reconstruction. It is intended to improve the accuracy of MPM diagnosis and to assist in the determination of chemotherapy doses, both of which may result in improved outcomes for patients.

Design and Implementation of Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that uses MRI technology to quantify brain activity by detecting changes in blood flow and applying the Blood-oxygen-level dependent (BOLD) contrast. Current neuroscience research suggests a modular brain structure. To understand the complex patterns of interaction between brain areas. The proposed system makes use of the CNN algorithm, which is an efficient method for dividing segmentation. A brain area is defined as a collection of subjects who exhibit a similar pattern of interaction between their brain areas. A thorough experimental examination utilizing benchmark data demonstrates that our technique is both effective and efficient. Two real-world fMRI studies found that the rifleman technique has the potential to improve comprehension of both normal brain activity and the abnormalities associated with psychiatric diseases. That is, mental diseases are typically defined by How an individual feels, acts, thinks, or perceives events. This is typically linked to specific areas or functions of the brain or the rest of the nervous system. This is unacceptable for a variety of time series sectors. As a result, we provide the statistical region merging approach, which is applied to picture segmentation. The method evaluates the values inside a regional range and combines them using the merging criteria, resulting in a smaller list. . As a result, we gather additional information and compare the findings to those already in the database. As a result, we decide if a person is normal or aberrant, as well as whether they are unwell.

A multi-platform Guidance, Navigation and Control system for the autosub family of Autonomous Underwater Vehicles

Over the last decade, there has been a proliferation of marine robots such as Unmanned Surface Vehicles (USVs), Autonomous Underwater Vehicles (AUVs) and underwater gliders. The variety of vehicles’ capabilities and styles has led to numerous strategies to address the Guidance, Navigation and Control (GNC) tasks. In particular, a recent trend in the advancements of GNC systems is to develop modular, multi-platform solutions. This paper illustrates the approach followed in the development of the GNC system for the different types of AUV of the Autosub family. The presented system is decomposed into several interconnected components, each implemented as a standalone software module. Each component is highly-reconfigurable and easily extensible, and the interactions between the modules rely on generic-purpose interfaces. Such a design enables the execution of a broad range of autonomous missions, as well as the integration and deployment of the system on marine platforms with very diverse characteristics, not limited to the vehicles of the Autosub class. Experimental results obtained in real-time during in water campaigns are provided to demonstrate the effectiveness of the proposed system and its versatility on a wide range of platforms and missions.

FabricFolding: learning efficient fabric folding without expert demonstrations

Abstract Autonomous fabric manipulation is a challenging task due to complex dynamics and potential self-occlusion during fabric handling. An intuitive method of fabric-folding manipulation first involves obtaining a smooth and unfolded fabric configuration before the folding process begins. However, the combination of quasi-static actions like pick & place and dynamic action like fling proves inadequate in effectively unfolding long-sleeved T-shirts with sleeves mostly tucked inside the garment. To address this limitation, this paper introduces an enhanced quasi-static action called pick & drag, specifically designed to handle this type of fabric configuration. Additionally, an efficient dual-arm manipulation system is designed in this paper, which combines quasi-static (including pick & place and pick & drag) and dynamic fling actions to flexibly manipulate fabrics into unfolded and smooth configurations. Subsequently, once it is confirmed that the fabric is sufficiently unfolded and all fabric keypoints are detected, the keypoint-based heuristic folding algorithm is employed for the fabric-folding process. To address the scarcity of publicly available keypoint detection datasets for real fabric, we gathered images of various fabric configurations and types in real scenes to create a comprehensive keypoint dataset for fabric folding. This dataset aims to enhance the success rate of keypoint detection. Moreover, we evaluate the effectiveness of our proposed system in real-world settings, where it consistently and reliably unfolds and folds various types of fabrics, including challenging situations such as long-sleeved T-shirts with most parts of sleeves tucked inside the garment. Specifically, our method achieves a coverage rate of 0.822 and a success rate of 0.88 for long-sleeved T-shirts folding. Supplemental materials and dataset are available on our project webpage at https://sites.google.com/view/fabricfolding.

Social Media Monitoring Of Airbnb Reviews Using AI: A Sentiment Analysis Approach For Immigrant Perspectives In The UK

This paper presents a novel approach for monitoring social media content related to Airbnb reviews, explicitly focusing on the sentiments expressed by immigrants in the United Kingdom. The proposed system, a Quick Search System, leverages machine learning techniques to perform sentiment analysis on many Airbnb reviews. The system aims to provide timely and insightful information about the experiences and sentiments of immigrants in the UK, as reflected in their Airbnb reviews. By employing state-of-the-art machine learning algorithms, the system enables efficient and accurate sentiment classification, allowing for the identification of key themes and sentiments expressed by immigrant users. The study demonstrates the potential of this approach in gaining a deeper understanding of immigrant perspectives within the context of peer-to-peer accommodation, and its implications for social media monitoring and customer satisfaction management.This present study has conducted a critical analysis utilizing efficient feature extraction techniques, including N-grams and TF-IDF, to optimize identifying positive, neutral, and negative feedback. Furthermore, five different models were utilized, and the training and testing processes were accompanied by parameter tuning. Ultimately, the study concluded that the Random Forest (RF) classifier performed exceptionally well, achieving a 95% accuracy rate.

Fast DC-link voltage control based on power flow management using linear ADRC combined with hybrid salp particle swarm algorithm for PV/wind energy conversion system

Fast DC-link voltage control based on power flow management using linear ADRC combined with hybrid salp particle swarm algorithm for PV/wind energy conversion system

Numerical simulation and experimental investigation on Phase Change Materials based energy storage system for cooling the water in process industries towards water conservation and environmental sustainability

Numerical simulation and experimental investigation on Phase Change Materials based energy storage system for cooling the water in process industries towards water conservation and environmental sustainability

An All-Terrain Intelligent Autonomous Vehicle with Sensor Fusion Based Navigation Capabilities

An automated defense robot building is planned, that has a laser gun attached, which is utilized for pointing laser rays to destroy the target object. One of the most important things about these robots is that they have the capability to perform missions remotely in the field, without any actual danger to human lives. In the proposed system, a robot is developed that is controlled through Microcontroller. For the aiming purpose and to view the road and the surroundings in which the robot is travelling, wireless cameras are installed. Radio frequency can be used to control the robot. These defense robots used in military are usually employed with the integrated system including gripper, cameras and sensors. This is specially designed for defense robotic system to save human life and protect the country from enemies. In the existing systems, personal computer using ZigBee protocol is used to monitor the robot.

A novel UEBASC‐qZSI‐based BLDC motor drive with modified DTC for EV application

SummaryAt present taking into account the situation of the environment and the growing demand of fossil fuels, electric vehicles (EVs) are a highly promising option for the transportation system. Many manufacturers of electric vehicles employ BLDC motors as the vehicle's driving component. In contrast to the many advantages that BLDC motors offer, one of the major drawbacks of employing these motors is the production of relatively high distortion in electromagnetic torque that is created by commutation. This distortion in torque has an effect on the vehicle's performance. In this proposed work a BLDC motor is powered by a cutting‐edge, highly gained q‐ZSI Ultra‐Extended Boost Active Switch Controlled Based Quasi Z‐source Inverter, or UEBASC‐qZSI. In the aforementioned drive, the torque distortion is minimized with the use of a modified direct torque control approach. With the aid of this UEBASC‐qZSI, the efficiency of EVs can be improved, and the overall safety of the driving cycle is also increased. Any vehicle's efficiency can be enhanced with the help of this UEBASC‐qZSI, and the overall safety of the overall system can also be improved. To validate the efficacy of the system, a Simulink model is developed in the MATLAB platform. Also, a hardware prototype model is developed for experimental authentication. The modeling and investigational findings demonstrate that the proposed system has significantly reduced the torque ripple while obtaining the necessary input dc bus voltage.

Waste oxy-fuel combustion integrated with supercritical CO2 cycle and high-temperature electrolysis technologies for e-methanol production: A feasibility analysis

Waste oxy-fuel combustion integrated with supercritical CO2 cycle and high-temperature electrolysis technologies for e-methanol production: A feasibility analysis

Design and Development of Automated Control of Restaurant Robot

This paper presents a novel voice-controlled restaurant robot system designed to revolutionize the dining experience by seamlessly integrating advanced robotics and natural language processing technologies. The proposed system aims to enhance customer service, operational efficiency, and overall dining satisfaction by providing a user-friendly and interactive platform. .The restaurant robot is equipped with a comprehensive set of capabilities, including, voice recognition and manipulation. By leveraging voice control, users can interact with the robot using natural language commands, enabling seamless communication and intuitive control. This allows customers to effortlessly place orders The system's is comprises a mobile robotic platform, bluetooth modules, a cloud-based natural language processing, The voice control functionality is implemented through a sophisticated speech recognition system, enabling the robot to accurately understand and interpret user commands. .In addition to customer-facing interactions, the restaurant robot also assists restaurant staff in various tasks. Deliver food and beverages to tables, handle payment transactions, and perform routine cleaning tasks. By automating these processes, the system improves operational efficiency, reduces labor costs, and allows human staff to focus on highervalue activities such as customer engagement and culinary expertise. .To evaluate the performance and usability of the voice-controlled restaurant robot, a series of experiments were conducted in a real-world restaurant environment. The results demonstrated the system's high recognition accuracy, robustness in noisy environments, and its ability to effectively handle a wide range of user requests. .The proposed voice-controlled restaurant robot presents a promising solution for transforming the dining experience. It combines the convenience of voice control with advanced robotics, enabling seamless and interactive interactions between customers, staff, and the robotic platform. The system's ability to enhance customer service, operational efficiency, and overall dining satisfaction positions it as a significant advancement in the hospitality industry.

IoT Structure based Supervisor and Enquired the Greenhouse Parameters

The world on the beginning of a new period of regeneration and developing with the increase of Internet and also Internet of Thing (IoT) in the market commercial application. where a new revolution can be made by characterizing its important. The rapprochement of the universal system and the power of sophisticated calculating, sensing with low cost and add create of communication supported by the Internet. In this paper, the green house system is consisting of two sections they are the software and hardware sections. Hardware has three types of sensors for natural environment measuring and gateway (router). Software consists of monitoring and controlling sub system, and IoT cloud, vb.Net software is used to write the programs codes for the proposed system. The environmental parameters of the greenhouse are continuously recorded by various sensors, and the collected data is displayed on a customized website. The system software has the ability to monitor and control the green house environment variables like humidity, temperature and water level on the greenhouse. Thus, the greenhouse can be monitored and controlling from anywhere and at any time. The fundamental aims of this paper have been achieved with the help of all systems parts. The green house environment has been displayed using the IoT cloud and the control is done over the internet connection. The system is implemented in practices. The system result gives an accurate indication that the system is reliable and work completely.

Cognitive routing in software defined networks using learning models with latency and throughput constraints

<p>To address latency and throughput challenges in software defined networks (SDNs), the research investigates cognitive routing's revolutionary implications. In today's data-driven world, network performance optimisation is crucial. Cognitive routing is a dynamic and potentially disruptive network management technology. Cognitive routing, strengthened by reinforcement learning and adaptive decision-making, is crucial to network efficiency and responsiveness, according to our study. The results show that cognitive routing optimises performance by limiting delay and maximising throughput. SDN application cognitive routing engine (CRE) driving forces, design, and preliminary assessment are described in this article. The CRE finds almost optimal paths for a user's quality of service (QoS) need while minimising monitoring overhead. Instead of global monitoring to find optimal paths, local monitoring achieves this. In ad-hoc networks, finding a trustworthy path reduces latency and ensures network stability. The proposed system was simulated utilising many parameters. Compared to previous SDN-based systems, end-to-end latency and ping round-trip time were better.</p>

Improvised direct torque control of a permanent magnet synchronous motor

Permanent magnet synchronous motor (PMSM) is widely used in different applications because of their different key operation features. But despite having important key features, PMSM suffers from torque ripple when simple control techniques like direct torque control (DTC) are employed. Several approaches have been raised by many researchers to lessen the ripple when DTC is used for controlling the PMSM drive. In this work, duty ratio-optimized DTC control, which has the same simplicity as conventional DTC (CDTC), and delay compensation for input signal prediction are proposed. In the proposed system, to determine the duty ratio, slope manipulation was made by considering the minimization of torque error and forcing the developed torque to be equal to the reference torque at the end of the span. An online slope determination approach was employed to compute the duty ratio. MATLAB 2021b is used for simulation purposes. The dynamics and steady-state performance of the proposed scheme were tested for both variable-load and variable-speed operations. In addition, the harmonic performance and ratio of harmonic power loss to total active power loss of the motor were evaluated. In general, the proposed scheme performs effectively in reducing steady-state ripple and harmonic.

Enhancing music recognition using deep learning-powered source separation technology for cochlear implant users.

Cochlear implant (CI) is currently the vital technological device for assisting deaf patients in hearing sounds and greatly enhances their sound listening appreciation. Unfortunately, it performs poorly for music listening because of the insufficient number of electrodes and inaccurate identification of music features. Therefore, this study applied source separation technology with a self-adjustment function to enhance the music listening benefits for CI users. In the objective analysis method, this study showed that the results of the source-to-distortion, source-to-interference, and source-to-artifact ratios were 4.88, 5.92, and 15.28 dB, respectively, and significantly better than the Demucs baseline model. For the subjective analysis method, it scored higher than the traditional baseline method VIR6 (vocal to instrument ratio, 6 dB) by approximately 28.1 and 26.4 (out of 100) in the multi-stimulus test with hidden reference and anchor test, respectively. The experimental results showed that the proposed method can benefit CI users in identifying music in a live concert, and the personal self-fitting signal separation method had better results than any other default baselines (vocal to instrument ratio of 6 dB or vocal to instrument ratio of 0 dB) did. This finding suggests that the proposed system is a potential method for enhancing the music listening benefits for CI users.

Modelling and performance analysis of free body dynamics of electric vehicles

The modelling and performance analysis of free body dynamics in electric vehicles (EVs) plays a crucial role in understanding and optimizing the vehicle's behavior and performance. This research focuses on accurately modelling the forces and motions acting on the vehicle body, excluding the powertrain components. By considering factors such as vehicle weight, suspension characteristics, tire properties, and aerodynamic forces, a comprehensive mathematical model is developed. This model enables the simulation and analysis of the vehicle's behavior under various operating conditions. The performance analysis involves evaluating key metrics such as vehicle response, stability limits, and ride comfort. The proposed system is compared with the existing electric vehicle in the market. The findings from this research contribute to the design and development of EVs with improved handling, stability, and energy efficiency. Additionally, they inform the development of advanced driver-assistance systems (ADAS) and autonomous driving technologies. Overall, the modelling and performance analysis of free body dynamics in EVs supports the advancement of sustainable and efficient transportation systems.

BISINDO (Bahasa Isyarat Indonesia) Sign Language Recognition Using Deep Learning

<p>Sign language recognition plays a crucial role in<br />facilitating communication for individuals with hearing<br />impairments. This paper presents a deep learning-based<br />approach for recognizing Bahasa Isyarat Indonesia (BISINDO),<br />the sign language used in Indonesia. The proposed system<br />employs convolutional neural networks (CNNs) and recurrent<br />neural networks (RNNs) to automatically extract features from<br />sign language gestures and classify them into corresponding<br />linguistic units. The dataset used for training and evaluation<br />consists of annotated BISINDO sign language videos.<br />Preprocessing techniques such as normalization and<br />augmentation are applied to enhance the robustness of the<br />model. Experimental results demonstrate the effectiveness of the<br />proposed approach in accurately recognizing BISINDO sign<br />language gestures, achieving state-of-the-art performance<br />compared to existing methods. The developed system shows<br />promising potential for real-world applications in enhancing<br />communication accessibility for the hearing-impaired<br />community in Indonesia.</p>