Proposed System Research Articles

A Novel Approach to the Technique of Lung Region Segmentation Based on a Deep Learning Model to Diagnose COVID-19 X-ray Images.

The novel coronavirus pandemic has caused a global health crisis, placing immense strain on healthcare systems worldwide. Chest X-ray technology has emerged as a critical tool for the diagnosis and treatment of COVID-19. However, the manual interpretation of chest X-ray films has proven to be inefficient and time-consuming, necessitating the development of an automated classification system. In response to the challenges posed by the COVID-19 pandemic, we aimed to develop a deep learning model that accurately classifies chest X-ray images, specifically focusing on lung regions, to enhance the efficiency and accuracy of COVID-19 and pneumonia diagnosis. We have proposed a novel deep network called "FocusNet" for precise segmentation of lung regions in chest radiographs. This segmentation allows for the accurate extraction of lung contours from chest X-ray images, which are then input into the classification network, ResNet18. By training the model on these segmented lung datasets, we sought to improve the accuracy of classification. The performance of our proposed system was evaluated on three types of lung regions in normal individuals, COVID-19 patients, and those with pneumonia. The average accuracy of the segmentation model (FocusNet) in segmenting lung regions was found to be above 90%. After reclassification of the segmented lung images, the specificities and sensitivities for normal, COVID-19, and pneumonia were excellent, with values of 98.00%, 99.00%, 99.50%, and 98.50%, 100.00%, and 99.00%, respectively. ResNet18 achieved impressive area under the curve (AUC) values of 0.99, 1.00, and 0.99 for classifying normal, COVID-19, and pneumonia, respectively, on the segmented lung datasets. Moreover, the AUC values of the three groups increased by 0.02, 0.02, and 0.06, respectively, when compared to the direct classification of unsegmented original images. Overall, the accuracy of lung region classification after processing the datasets was 99.3%. Our deep learning-based automated chest X-ray classification system, incorporating lung region segmentation using FocusNet and subsequent classification with ResNet18, has significantly improved the accuracy of diagnosing respiratory lung diseases, including COVID-19. The proposed approach has great potential to revolutionize the diagnosis of COVID-19 and other respiratory lung diseases, offering a valuable tool to support healthcare professionals during health crises.

Web Page Ranking Based on Text Content and Link Information Using Data Mining Techniques

Thanks to the rapid expansion of the Internet, anyone can now access a vast array of information online. However, as the volume of web content continues to grow exponentially, search engines face challenges in delivering relevant results. Early search engines primarily relied on the words or phrases found within web pages to index and rank them. While this approach had its merits, it often resulted in irrelevant or inaccurate results. To address this issue, more advanced search engines began incorporating the hyperlink structures of web pages to help determine their relevance. While this method improved retrieval accuracy to some extent, it still had limitations, as it did not consider the actual content of web pages. The objective of the work is to enhance Web Information Retrieval methods by leveraging three key components: text content analysis, link analysis, and log file analysis. By integrating insights from these multiple data sources, the goal is to achieve a more accurate and effective ranking of relevant web pages in the retrieved document set, ultimately enhancing the user experience and delivering more precise search results the proposed system was tested with both multi-word and single-word queries, and the results were evaluated using metrics such as relative recall, precision, and F-measure. When compared to Google’s PageRank algorithm, the proposed system demonstrated superior performance, achieving an 81% mean average precision, 56% average relative recall, and a 66% F-measure.

Beneficial Effects of Self-Motion for the Continuous Phase Analysis of Ac-Coupled Doppler Radars

This paper analyzes the beneficial effects on phase detection arising from the motion of an ac-coupled Doppler radar. Indeed, although the presence of an ac coupling stage suppresses the dc offset after the receiver RF output, due to the coupling capacitor, a high-pass behavior is introduced; the presence of a high-pass behavior leads to signal distortion, particularly for low Doppler frequencies, which are typical in many biomedical or industrial applications. Since the target displacement is usually extracted from the phase history, this effect might, in turn, worsen the overall accuracy of the system. Moreover, if the target alternates stationary and moving time intervals, the phase detection step becomes challenging. Indeed, during the stationary time, the output of the RF front-end shows only noise fluctuations that, in turn, result in uncorrelated phases which might be confused with the real target displacement. This negative effect might be avoided by keeping the radar continuously moving, thus exploiting what is usually considered a state that is negative and worthy of attention. In this contribution, this effect is addressed from a different perspective, and ad hoc experimental case studies are shown to demonstrate the effectiveness of the proposed system. This task has been accomplished through theoretical analysis and related experimental activity.

Digital Platform for Confidential Complaint Resolution: Empowering Anonymity in Online Reporting

This report explores an innovative anonymous authentication system designed for academic services within the university environment. The primary objective of this proposed system is to safeguard students' identities when reporting instances of academic misconduct within the university system. The system comprises two fundamental sub- systems: one dedicated to user management and the other focusing on file management for feedback submitted by students. The "User Management" system facilitates tasks such as new enrollment, registration verification, and key distribution to system users. Meanwhile, the "Student Feedback" system assists users in providing feedback, encrypting and describing files, and submitting feedback. The file encryption process employs the Attribute-Based Encryption (ABE) algorithm. Both systems are interconnected through a confidential API. In the testing phase of this study, data from Pune University's community service participants in the year 2023 were utilized. The tests covered processes such as anonymous authentication, encryption, file description, key distribution, feedback submission, and feedback status. The evaluation produced positive results, indicating that the user authentication process takes only 4.6 seconds, while sending feedback requires 6.4 seconds. Key Words: Anonymous Authentication, Student Feedback, Academic Service, Distributed System,MD5, online , complaint , management , respond.

Dynamical Analysis of Prey Refuge Effects on the Stability of Holling Type III Four-species Predator-Prey System

Models involving four species with prey refuge and type I responses have been studied with recommendations on their extension to include either type II or type III responses. However, models with Holling type II responses are de-stabilizing according to most studies. In this paper, therefore, a multi-species ecological system that includes a prey refuge and a Holling type III functional response is analyzed, to study the effect of reserved zones in enhancing the dynamical stability of the proposed system. The Routh-Hurwitz (RH) criterion and the eigenvalue technique are used to study the local stabilities. On the other hand, global stabilities have been studied using the Lyapunov technique. Numerical simulations have been carried out using the Matlab ode45 solver software to verify the analytical results. The findings show that refuge plays a significant part in improving the dynamic stability of the system.

Mn-L-based multifunctional molecular spintronic device: A first-principles investigation

A transition metal coordination complex Mn-L-based multifunctional molecular spintronic device is proposed and its spin-polarized transport properties in two magnetic configurations (parallel and anti-parallel alignments) are evaluated using the first-principles density functional theory and non-equilibrium Green’s function method. Four high-performance physical effects, including spin-filtering, spin-rectifying, giant magnetoresistance and negative differential resistance, are revealed in the proposed device. The physical mechanisms are analyzed through the spin-resolved bias-dependent transmission spectra, and projected device density of states spectra. These results demonstrate that the proposed system holds great promise in developing the multifunctional molecular spintronic devices.

Techno-Economic and Environmental Analysis of a Sewage Sludge Alternative Treatment Combining Chemical Looping Combustion and a Power-to-Methane System

An innovative process layout for sludge waste management based on chemical looping combustion and flue gas methanation is analyzed in this work. The technical performance of the system was assessed by considering that the flue gas is first purified and then mixed with a pure hydrogen stream sourced from an array of electrolysis cells to produce methane. The life cycle assessment (LCA) and life cycle cost (LCC) methodologies were applied to quantify the environmental and economic performances of the proposed process, and a hotspot analysis was carried out to recognize its most critical steps. The proposed system was then compared with a reference system that includes both the conventional waste management pathways for the Italian context and methane production. Finally, to account for the variability in the future economic climate, the effects of changes in landfill storage costs on sewage end-of-life costs for both the proposed and reference systems were evaluated. With respect to 1 kg/h of sewage sludge with 10%wt of humidity, the analysis shows that the proposed system (i) reduces landfill wastes by about 68%, (ii) has an end-of-life cost of 1.75 EUR × kg−1, and (iii) is environmentally preferable to conventional sewage sludge treatment technologies with respect to several impact categories.

Emergency power supply enabling solar PV integration with battery storage and wireless interface

ABSTRACT This paper presents a detailed investigation of an emergency power supply that enables solar photovoltaic (PV) power integration with a battery energy storage system (BESS) and a wireless interface. Through the utilisation of solar PV-based generation and BESS with wireless/contactless power transmission, the proposed method offers an easy-to-setup and flexible alternative solution for the emergency power supply (EPS) for household appliances and wireless electric vehicle (EV) charging for all weather conditions. During bad weather conditions, the battery acts as the main power supply and can be charged from the solar PV panel and during rainy days, it can be charged from the grid by the proposed wireless interface for emergency use. The proposed system is analysed by mathematical modelling, focusing on the interface of the solar PV, BESS, and load, with inductive power transfer (IPT) as a wireless power transfer (WPT) method. The simulation and a scaled-down experimental prototype are built to demonstrate that the proposed system enables wireless power transfer with PV and BESS, and easy installation can be achieved by just placing the primary charging coil of the proposed power supply close to the wireless charging pad that is available in the existing system for e.g. EV charging. The results show that the load voltage is kept constant at 48 V against the varying input characteristic of the solar panel which is the solar irradiance ranging from 20% to 100% and output characteristics of the load of the proposed integrated system with variation of 20–50%, respectively. Moreover, the wireless power transfer efficiency across the inductive coils is maintained at about 97% under these variations.

An exploration system to effectively analyze the visual metaphor used in sentiment visualization

In the field of data visualization, there has been a recent trend of using a complex type of visualization with a multidimensional structure or using several visualizations in parallel when summarizing the results of sentiment analysis. Although this trend may be useful for sophisticated sentiment analysis, such analysis is difficult for the general public and novice researchers. To address this issue, there has recently been a trend of visualizing sentiments using visual metaphors. To facilitate the understanding of related cases, it is necessary to have a systematic means of grasping the sentiment target, the purpose and motivation of research, and the representations used as substitutes for visual metaphors. Therefore, the objective of the present study was to develop an exploration system that can analyze the visual metaphors used in the case of sentiment visualization. For this study, (1) sentiment visualization cases in which visual metaphors are used were collected. (2) After a taxonomy composed of the categories of “target, intermediation, representation, visual variable, and visualization technique” was constructed, it was used to analyze sentences of visual metaphors appearing in sentiment visualization cases. (3) An exploration system capable of grasping the semantic relationships of sub-elements within the five categories of the taxonomy and intuitively interpreting visual metaphors was developed so that appropriate cases can be recommended to sentiment visualization researchers. (4) The approach and usefulness of the exploration system were explained using user scenarios. (5) A case study was conducted to show that the provided system can be analyzed from various perspectives. (6) The usability of the exploration system was demonstrated through a verification targeting experts. The proposed system allows researchers and analysts to intuitively grasp “what types of visual metaphor method and idea should be equipped to visualize sentiment data in an easier way to understand.”

Bottom Deposition Enables Stable All-Solid-State Batteries with Ultrathin Lithium Metal Anode.

Modern strides in energy storage underscore the significance of all-solid-state batteries (ASSBs) predicated on solid electrolytes and lithium (Li) metal anodes in response to the demand for safer batteries. Nonetheless, ASSBs are often beleaguered by non-uniform Li deposition during cycling, leading to compromised cell performance from internal short circuits and hindered charge transfer. In this study, the concept of "bottom deposition" is introduced to stabilize metal deposition based on the lithiophilic current collector and a protective layer composed of a polymeric binder and carbon black. The bottom deposition, wherein Li plating ensues between the protective layer and the current collector, circumvents internal short circuits and facilitates uniform volumetric changes of Li. The prepared functional binder for the protective layer presents outstanding mechanical robustness and adhesive properties, which can withstand the volume expansion caused by metal growth. Furthermore, its excellent ion transfer properties promote uniform Li bottom deposition even under a current density of 6 mA·cm-2. Also, scanning electron microscopy analysis reveals a consistent plating/stripping morphology of Li after cycling. Consequently, the proposed system exhibits enhanced electrochemical performance when assessed within the ASSB framework, operating under a configuration marked by a high Li utilization rate reliant on an ultrathin Li.

Vehicle Speed Detection using Haar Cascade Classifier and Correlation Tracking

Objectives: The aim of this study is to develop an efficient and cost-effective solution for predicting vehicle speeds using recorded video data. Methods: The proposed system employs a combination of image processing techniques and computer vision to calibrate cameras for traffic simulation, enabling the extraction of information on average vehicle speeds. It utilizes the Haar Cascade Classifier for object detection, followed by a correlation tracker for vehicle tracking. Speed estimation is achieved through the frame differencing method. The dataset comprises 90 minutes of recorded data from highway cameras, showcasing diverse traffic scenarios with various vehicle types (trucks, trailers, cars, buses, and bikes) at varying speeds. Predicted values are compared with ground truth data obtained from a GPS-equipped car, using Mean Absolute Error (MAE) as the evaluation metric. Findings: The algorithm's performance is evaluated, resulting in an average error rate of 1.72 km/h (2.07%). These findings are compared with state-of-the-art data. Novelty: This study introduces a novel system that combines the Haar Cascade Classifier, correlation tracker, and frame differencing method to track vehicle positions, incorporating bike detection into the analysis, and calculate their moving speeds. A relative analysis underscores the system's performance, emphasizing its effectiveness in real-world applications and demonstrating refinement in accuracy assessment. Keywords: Image processing, Vehicle speed estimation, Haar Cascade Classifier, Correlation tracker, Error rate calculation, Computer vision

Elements of the modern system of domestic financial control

Subject. This article deals with the theoretical and practical issues related to the construction of a domestic financial control system and the formation of its elements (components). Objectives. The article aims to determine the main features of the functioning and development of the domestic financial control system, identify the elements (components) of the proposed system with the justification for classifying them as types of control, develop a scheme of the financial control system pointing out input and output values, the target function, as well as determine the goals, objectives and principles of building a domestic financial control system. Methods. For the study, we used the systems and logical approaches, and the general scientific cognition methods. Results. On the basis of the categorical analysis, the article clarifies the main elements (components) of the modern system of domestic financial control, its goals, objectives and principles of construction. It also formulates the need to build a modern system of domestic financial control, determines the main features of its functioning and development, identifies the elements (components) of the proposed system with the justification for classifying them as types of control, offers an author-developed scheme of the financial control system pointing out input and output values, and the target function. Relevance. The results obtained can be used in the activities of control and audit offices, forensic units, audit organizations and tax authorities during examination, control and audit.

METHODICAL SYSTEM FOR THE PEDAGOGICAL EXCELLENCE DEVELOPMENT OF GENERAL EDUCATION TEACHERS OF VOCATIONAL EDUCATION INSTITUTIONS

The relevance. The issue of developing teacher's pedagogical excellence is one of the most significant in pedagogical theory and practice, especially at the current stage of overcoming the consequences of the pandemic and martial law. The article proves the importance of process studying of the pedagogical excellence developing of general education teachers of vocational education institutions as a goal-oriented, dynamic system. The aim is to substantiate the methodical system for the development of pedagogical excellence of general education teachers of vocational education institutions. Methods: the study used theoretical methods, which provided an opportunity to examine the state of the research problem in scientific sources, compare different views, analyse the process of development of pedagogical excellence of general education teachers of vocational education institutions as a methodical system. This study used the methods of analysis and synthesis, systematisation, abstraction, generalisation, and modelling, which resulted in building a structure of functional components of the system of teachers' pedagogical excellence development and clarifying the interconnection of the selected components. Results: the article deals with the concept of "system" as a structure that combines naturally arranged elements, components grouped by some common feature; the principles of functioning and development of pedagogical systems are highlighted, the types and structural components of pedagogical systems are analysed; the design of a system for the development of pedagogical excellence of general education teachers as a certain type of pedagogical system is considered. The main structural components of the designed system (target, conceptual and methodological, subjective, content, technological, diagnostic and resultant) are characterised; the functional components of the system for developing pedagogical excellence of general education teachers in a vocational education institution are allocated; the place of the proposed system for developing pedagogical excellence of general education teachers among the systems of vocational education is determined. Conclusions: the designed system for the development of pedagogical excellence of general education teachers in a vocational education institution, as a certain type of pedagogical system, combines certain structural components (target, conceptual and methodological, subjective, content, technological, diagnostic and resultant, and functional components: design, constructive, gnostic, prognostic, organisational, communicative, evaluative and corrective); the general pedagogical system of a vocational education institution includes a system of pedagogical staff professional development, which in turn combines the system of pedagogical staff advanced training and the pedagogical excellence development system of general education teachers of a vocational education institution as a subsystem; prospects for further research will be devoted to substantiating the goals and designing the content of the development of pedagogical excellence of general education teachers.

Comparison of Machine Learning Methods for Calories Burn Prediction

This paper focuses on the prediction of calories burned during exercise using machine learning techniques. Due to a growing number of obesity and overweight people, a healthy lifestyle must be adopted and maintained. This study explores and compares several machine learning regression models namely LightGBM, XGBoost, Random Forest, Ridge, Linear, Lasso, and Logistic to assess their calories burned prediction performance that can be used in systems such as fitness recommender systems supporting a healthy lifestyle. Our findings show that the LightGBM for predicting calorie burn has a good accuracy of 1.27 mean absolute error, giving users reliable recommendations. The proposed system has a good potential in assisting users in reaching their fitness objectives by offering precise and tailored advice.

Zero energy building optimization for a residential complex with a new optimized cogeneration system for electricity, cooling, heating and freshwater production

In this research, a multi-generation system was proposed to meet the energy consumption of a 120-unit zero-energy residential complex with 100 m2 and 2 bedrooms in Sydney, Australia. To meet the heating and cooling demand of the residential complex, a compression chiller was used, and electrical energy was provided using the system's production power. The main goal of this research is to define a zero-energy building by providing the energy consumption of a residential complex with a solar system. Building Energy Optimization (BEopt) Software has been used for the process of simulating the residential complex and extracting the required load of the residential complex. The design of the load supply system of the residential complex was done using EES thermodynamic software. In this research, a large number of inputs have been used in different scenarios to optimize the residential complex, and the tool used in this research by all the modes introduced as inputs turns the problem into a multi-objective optimization problem. The results of the investigation of the residential complex showed that the amount of electricity consumption, heating, and cooling of the residential complex during the year is 36728, 133123, and 17,902 kWh, respectively. Also, by optimizing the energy consumption of the residential complex, it is possible to reduce CO2 emissions and prevent the increase in environmental pollution. The optimal results showed that the system in its most optimal state can reach an exergy efficiency of 25.18 % and a system cost rate of 46.6 $/h. The performance of the system in the city of Sydney showed that the system can produce 166,164 kWh of electricity, 467,945 kWh of heating, and 183,370 kWh of cooling in one year. Comparing the results of residential complex consumption load and system production showed that the proposed system can easily provide the residential complex consumption load throughout the year.

Seismic improvement of structures using hybrid self-centring dampers and rocking core

Extensive research on seismic-resistant systems aims to lower earthquake repair costs and downtime. This article presents a novel low-damage self-centring system that aims to improve the seismic resilience of buildings by enabling controlled rocking motion while reducing the risk of overturning. Significantly, the rotational movement can be entirely restored. This system combines a rocking core with damping devices, including disc springs and peripheral slit dampers. In this hybrid system, two damping devices are mounted to both first-floor columns of the rocking bays and the rigid movement of the rocking core triggers the dampers. Firstly, a comparative investigation was conducted to assess the efficacy of the endorsed hybrid system. The cyclic pushover results showed that the hybrid system is superior to other typical bracing systems in energy dissipation, with a five times higher margin. In addition, by employing the suggested system, the frame's drift ratio profile under nonlinear time history analysis (NTH) remained consistent throughout the structure, indicating its ability to withstand the soft storey mechanism. Secondly, a comprehensive assessment was conducted on the proposed system's collapse using incremental dynamic analysis (IDA) took into account various seismic intensities and engineering demand parameters for buildings with different bay lengths and included low, medium, and high-rise structures. The system's flag-shaped behavior and self-centring capability were confirmed by the average residual drift ratio of approximately 0.03% (almost zero) for all examined structures. Also, the collapse performance of the system exhibited a safety margin against collapse, with an average of 1.9 across all buildings and seismic intensities.

Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical Imaging

This paper presents a pioneering study in the application of real-time surface landmine detection using a combination of robotics and deep learning. We introduce a novel system integrated within a demining robot, capable of detecting landmines in real time with high recall. Utilizing YOLOv8 models, we leverage both optical imaging and artificial intelligence to identify two common types of surface landmines: PFM-1 (butterfly) and PMA-2 (starfish with tripwire). Our system runs at 2 FPS on a mobile device missing at most 1.6% of targets. It demonstrates significant advancements in operational speed and autonomy, surpassing conventional methods while being compatible with other approaches like UAV. In addition to the proposed system, we release two datasets with remarkable differences in landmine and background colors, built to train and test the model performances.

A combined deep CNN with a chimp optimization approach for lung cancer diagnosis

The most common type of disease that is normal among women is lung cancer. It is one of the main reasons among women, despite great efforts to prevent it through trackers. An automatic disease detection system helps doctors identify and provide accurate results, thus minimizing the mortality rate. Computer Aided Diagnosis (CAD) has minimal human intervention and produces more accurate results than humans. It will be a difficult and lengthy task that depends on the experience of the pathologists. Deep learning methods have been shown to give better results when correlated with ML and extract the best highlights from images. The main objective of this article is to propose a deep learning technique in combination with a convolution neural network (CNN) with a chimpanzee optimization algorithm to diagnose lung cancer. Here, CNN is used for feature extraction and used for extracted feature detection. Experimental results show that the proposed system achieves 100% accuracy, 99% sensitivity, 99% recall, and 98% F1 score compared to other traditional models. As the system achieved correct results, it can help doctors easily investigate lung cancer.

Privacy-preserving intrusion detection in Internet of medical things neural networks using a novel recurrent U-Net auto-encoder algorithm for biomedical applications

The rapid development of Internet of Things (IoT) technology has enabled the emergence of the Internet of Medical Things (IoMT), especially in body area network applications. To protect sensitive medical data, it is essential to ensure privacy preservation and detect intrusions in this context. This study proposes a novel intrusion detection system that protects the privacy of IoMT networks, specifically in the context of body area networks. For feature extraction, the system employs a recurrent U-Net autoencoder algorithm, which effectively captures temporal dependencies in IoMT data. In addition, privacy is protected through the combination of data anonymization techniques and data classification using Principal Component Analysis (PCA). Combining the recurrent U-Net autoencoder algorithm, privacy preservation mechanisms, and PCA-based data classification, the proposed system architecture comprises the U-Net autoencoder algorithm. The proposed method is superior to existing approaches in terms of accuracy, precision, recall, F-measure, and classification loss, as demonstrated by experimental evaluations. This research contributes to the field of privacy protection and intrusion detection in IoMT, specifically in body area network applications.

Analysis of hybrid integrated-alternate mark inversion (I-AMI) modulation and symmetrical-symmetrical-post (SSP) dispersion compensation technique in single-tone radio over fiber (RoF) system

Abstract This paper presents the high capacity single-tone Radio over Fiber (RoF) system utilizing advanced modulation technique in conjunction with optimized dispersion compensation scheme to support the high data rate transmission upto 120 Gbps. The proposed system incorporates the Integrated-Alternate Mark Inversion (I-AMI) modulation and Symmetrical-Symmetrical-Post (SSP) compensation technique to enhance the spectral efficiency as well as to reduce the chromatic dispersion effects in long-haul RoF link. The proposed system i.e., I-AMI with SSP has been examined at 60, 80, 100 and 120 Gbps bit rate for various transmission distance. The simulation results obtained in terms of Q-factor, Bit Error Rate (BER) and eye diagram reveal that the proposed system provides wider eye-opening, low BER and high Q-factor as compared to the existing system. The proposed I-AMI with SSP provides the Q-factor of 30.47 whereas the I-AMI with PSP and AMI with SSP provides the Q-factor of 18.77 and 18.40 respectively at 80 Gbps bit rate over a transmission distance of 800 km.