bluuebunny/crossref_metadata_2025 huggingface dataset:
Energy saving through load balancing of 3-wire loads
Authors: Obais Abdulkareem Mokif , Mukheef Ali Abdulkareem
Published: April 2023

In this paper, static var compensators (SVCs) and many load compensation techniques are reviewed. A continuously and linearly controlled compensating susceptance is devised from a switched capacitor bank and a switched reactor bank. The switched capacitor bank is built of four binary weighted thyristor switched capacitors, while the switched reactor bank is built of three binary weighted thyristor switched reactors. Although few switched capacitors and reactor are used, their binary weighted values beside their control scheme make them respond as a continuously and linearly controlled reactive device in capacitive and inductive modes of operation. A load balancing system is constructed of three identical devised compensating susceptances connected in delta-form. It is designed for balancing an 11 kV 50 Hz distribution station. The proposed system is designed and tested on PSpice which is a computer program equivalent in performance to real hardware design. The simulation results of the proposed system have showed significant treatment of severe imbalance conditions.
Automated home waste segregation and management system
Authors: Murugan Nandhini , Sivathanu Anithakumari , Vaidyanathan Krithika , Tiwari Anupam , Varma Armaan
Published: April 2023

Waste management is a massive issue in India, most of the present systems cannot manage waste on a scalable level, thus creating pressure on the ecosystem. Before the elimination of waste, segregation needs to be done to manage individual types of waste. Hence taken the same approach to solving the problem, which most of the present-day systems fail to do. The goal is to segregate the garbage generated in individual households into solid, liquid, biodegradable, non-biodegradable, combustible, and non-combustible, using many subsystems that involve electro pneumatics, compression, and storage. Image processing techniques will further advocate the process. The desired system will further reduce the waste of an in-built pulverizer. After conducting in-depth research on the present solutions for the urban waste processing chain, the level of complexity increases as the waste goes further along the chain and, in the end, the only option left is incineration was figured out. The solution allows endpoints of the chain to process different types of garbage in a more organized fashion. Municipal solid waste (MSW) is solid waste that results from municipal community, commercial, institutional, and recreational activities. This paper aims to segregate the MSW generated by households into biodegradable, non-biodegradable, combustible, and non-combustible.
Infinite horizon fuzzy optimal control: optimality does not imply asymptotic stability
Authors: Teniou Samir , Belarbi Khaled
Published: April 2023

In this paper, novel stability result for discrete-time infinite horizon optimal control using fuzzy objective functions is presented. For this class of control, the fuzzy goals and the fuzzy constraints introduced in the fuzzy objective function handle the constraints placed on both the state and the control vectors. We analyze the asymptotic stability of the equilibrium for the infinite horizon fuzzy optimal control law using the minimum aggregation operator. We show that the infinite horizon control with the minimum aggregation operator does not guarantee the asymptotic stability of the equilibrium in general. This is done by deriving an analytical solution of the control law for a simple linear system using a fuzzy dynamic programming approach. An example that shows the novel asymptotic stability result of the equilibrium for discrete-time infinite horizon optimal control with fuzzy objective function problem is given.
Realtime face matching and gender prediction based on deep learning
Authors: Surinwarangkoon Thongchai , Hoang Vinh Truong , Vafaei-Zadeh Ali , Hendi Hayder Ibrahim , Meethongjan Kittikhun
Published: April 2023

Face analysis is an essential topic in computer vision that dealing with human faces for recognition or prediction tasks. The face is one of the easiest ways to distinguish the identity people. Face recognition is a type of personal identification system that employs a person’s personal traits to determine their identity. Human face recognition scheme generally consists of four steps, namely face detection, alignment, representation, and verification. In this paper, we propose to extract information from human face for several tasks based on recent advanced deep learning framework. The proposed approach outperforms the results in the state-of-the-art.
Reconstructing 3D model of accident scene using drone image processing
Authors: Norahim Mohamad Norsyafiq Iman , Tahar Khairul Nizam , Maharjan Gyanu Raja , Matos Jose C.
Published: April 2023

At the current stage, an investigation technique on the accident takes a longer time and this causes longer traffic congestion. The aim of this study is to reconstruct a 3D model of an accident scene using an unmanned aerial vehicle (UAV). The flight parameters that have been chosen are the circular method, the double grid method, and the single grid method. All these designs can produce a good 3D model to achieve the study’s objective. The methodology in this study is divided into 4 phases which are preliminary work, data acquisition, data processing, and data analysis. The main results of this study are the 3D model of the accident scene, an orthophoto map layout, and an accuracy assessment of a 3D model of reconstructed accident scene. All these parameters will be tested on accuracy based on the root mean square error (RMSE) value, comparing the UAV data and site measurement data. This objective has been tested for 10 different types of processing and different types of flight parameters. The best result among all the methods is the circular method 5 meters with ground control point (GCP) since this method has the least RMSE value which is 0.047 m. UAVs can replace the site measurement to reconstruct the accident scene.
Personal innovativeness and facilitating conditions in shaping the outlooks toward m-banking adoption among generation Y in Malaysia
Authors: Lim Foo-Wah , Fakhrorazi Ahmad , Ikhsan Ridho Bramulya , Silitonga Karina , Loke Wei-Kit , Abdullah Nik
Published: April 2023

The study investigated the determinants of m-banking adoption among generation Y (Gen-Y) in Malaysia. The study underpinned the technology acceptance model as the main guideline or a blueprint for analyzing the research model. The study applied a survey research design and investigated structural equation modeling (SEM) under the partial least-square SEM (PLS-SEM) technique using SmartPLS 2.0. A total 358 m-banking users in Malaysia were exerted as respondents who were randomly selected and then investigated. The results of the analysis conducted with PLS-SEM reveal the existence of usage convenience, facilitating conditions, and personal innovation in information technology that has been remarkably influenced by the outlooks that have adopted m-banking in the country of Malaysia. In addition, the usage convenience that has been felt and facilitated by the conditions found can affect the appraised practicality of m-banking remarkably. Finally, the usage convenience of m-banking that is felt among Gen-Y in Malaysia is influenced by the remarkable activation of personal conditions and innovations exerted in information technology.
Seawater salinity modelling based on electromagnetic wave characterization
Authors: Elmutasim Imadeldin Elsayed , Mohamed Izzeldin Ibrahim , Bilal Khalid Hamid
Published: April 2023

Wireless communications have experienced tremendous growth, and improving their performance based on specific parameters requires an accurate model. Salt seawater, being an abundant resource, could play a crucial role in various applications such as enhancing electrical conductivity, monitoring security, improving battery power efficiency, and creating liquid antennas. Salinity is an essential factor to consider when developing these applications. This paper focused on investigating the electromagnetic properties of seawater salinity in the context of marine wireless communications. The results of the study showed that salinity has a significant impact on the Fresnel reflection coefficient in terms of magnitude, phase shift, and polarization, and can either constructively or destructively affect it. The new model paved the way for the development of an integrated salt seawater model that addressed the complex salinity issues involved in these applications.
Pattern reconfigurable dielectric resonator antenna using capacitor loading for internet of things applications
Authors: Khaleel Aymen Dheyaa , Ghazali Osman , Jawad Aqeel Mahmood , Ibrahim Ayman Mohammed , Mahmuddin Massudi , Al-Gburi Ahmed Jamal Abdullah , Mahdi Mohammed Najah
Published: April 2023

This research study presents a cube dielectric resonator antenna (DRA) with four different radiation patterns for internet of things (IoT) applications. The various radiation patterns are determined by the grounded capacitor loading to reduce interference. The DRA is constructed of ceramic material with a dielectric constant of 30 and is fed via a coaxial probe located in the antenna’s center. Capacitors are used to load the four parasitic microstrip feed lines. Each pattern of radiation is adjustable by adjusting the capacitors loading on the feed line. The proposed antenna works at 3.5 GHz with -10 narrow impedance bandwidth of 74 MHz.
Design of time division multiplexing/wavelength division multiplexing passive optical network system for high-capacity network
Authors: Azmi Farid Hazwan , Naim Nani Fadzlina , Ya'acob Norsuzila , Sarnin Suzi Seroja , Supian Latifah Sarah
Published: April 2023

This paper presents the design of time division multiplexing-wavelength division multiplexing-passive optical network (TDM-WDM PON). In this design, the current TDM PON is incorporated with the proposed WDM-PON in order to design a high-capacity network with lower loss requirements. The design has been simulated using OptiSystem software. The upstream wavelength for WDM is between 1,530.334 to 1,542.142 nm while for TDM is 1,310 nm. The downstream wavelength for WDM is from 1,569.865 to 1,581.973 nm, while for TDM is 1,490 nm. Based on the result, it is found that the proposed network is capable to support up to 64 customers with a bit rate of 2.5 Gbps.
Downlink massive full dimension-multiple input multiple output downlink beamforming analysis at 3.5 GHz using coordinated ON-OFF switching
Authors: Nishanthi Chilukuri , Ramamurthy Nallagarla
Published: April 2023

The long-term evolution and advancement (LTE-A) of the 5G wireless network depends critically on energy consumption. Many existing solutions focus on limiting power constraints and consequently system coverage. So, improving the antenna array elements of the base station (BS) can solve this issue. In this paper, introduce a coordinated ON-OFF switching method in the massive full dimensional multiple input multiple output (massive-FD-MIMO) system. It enhances the radiation pattern of the antenna array element by adjusting the angular power spectra at the BS. By the way, it allows to select the minimum number of antennas for effective beamforming toward specific user equipment’s (UEs). In this context, part of antenna element should be active mode and remining should be sleep mode at the time of signal beamforming. The multipath spatial profiles are decided the beamforming frequency band with minimize energy consumption. As part of the method, we used a conjugated beamforming with power optimization scheme to determine the individual antenna potential and fading channel condition, power optimization is performed. This method quality of service, reliability, energy consumption and data rate can all be evaluated by experimenting with different-sized antenna arrays such as 16×16, 32×32, 64×64 and 128×128.
Investigation of the performance of multi-input multi-output detectors based on deep learning in non-Gaussian environments
Authors: Pourmir Mohammad Reza , Monsefi Reza , Hodtani Ghosheh Abed
Published: April 2023

The next generation of wireless cellular communication networks must be energy efficient, extremely reliable, and have low latency, leading to the necessity of using algorithms based on deep neural networks (DNN) which have better bit error rate (BER) or symbol error rate (SER) performance than traditional complex multi-antenna or multi-input multi-output (MIMO) detectors. This paper examines deep neural networks and deep iterative detectors such as OAMP-Net based on information theory criteria such as maximum correntropy criterion (MCC) for the implementation of MIMO detectors in non-Gaussian environments, and the results illustrate that the proposed method has better BER or SER performance.
Modeling and simulation of the two-tank system within a hybrid framework
Authors: Yaakoubi Hanen , Haggège Joseph
Published: April 2023

Most real-world dynamical systems are often involving continuous behaviors and discrete events, in this case, they are called hybrid dynamical systems (HDSs). To properly model this kind of systems, it is necessary to consider both the continuous and the discrete aspects of its dynamics. In this paper, a modeling framework based on the hybrid automata (HA) approach is proposed. This hybrid modeling framework allows combining the multi-state models of the system, described by nonlinear diﬀerential equations, with the system’s discrete dynamics described by ﬁnite state machines. To attest to the efficiency of the proposed modeling framework, its application to a two-tank hybrid system (TTHS) is presented. The TTHS studied is a typical benchmark for HDSs with four operating modes. The MATLAB Simulink and Stateflow tools are used to implement and simulate the hybrid model of the TTHS. Different simulations results demonstrate the efficiency of the proposed modeling framework, which allows us to appropriately have a complete model of an HDS.
Rhizostoma optimization algorithm and its application in different real-world optimization problems
Authors: Mahareek Esraa A. , Akif Cifci Mehmet , El-Zohni Habiba A. , Desuky Abeer S.
Published: April 2023

In last decade, numerous meta-heuristic algorithms have been proposed for dealing the complexity and difficulty of numerical optimization problems in the realworld which is growing continuously recently, but only a few algorithms have caught researchers’ attention. In this study, a new swarm-based meta-heuristic algorithm called Rhizostoma optimization algorithm (ROA) is proposed for solving the optimization problems based on simulating the social movement of Rhizostoma octopus (barrel jellyfish) in the ocean. ROA is intended to mitigate the two optimization problems of trapping in local optima and slow convergence. ROA is proposed with three different movement strategies (simulated annealing (SA), fast simulated annealing (FSA), and Levy walk (LW)) and tested with 23 standard mathematical benchmark functions, two classical engineering problems, and various real-world datasets including three widely used datasets to predict the students’ performance. Comparing the ROA algorithm with the latest meta-heuristic optimization algorithms and a recent published research proves that ROA is a very competitive algorithm with a high ability in optimization performance with respect to local optima avoidance, the speed of convergence and the exploration/exploitation balance rate, as it is effectively applicable for performing optimization tasks.
An advance extended binomial GLMBoost ensemble method with synthetic minority over-sampling technique for handling imbalanced datasets
Authors: Rout Neelam , Mishra Debahuti , Mallick Manas Kumar
Published: April 2023

Classification is an important activity in a variety of domains. Class imbalance problem have reduced the performance of the traditional classification approaches. An imbalance problem arises when mismatched class distributions are discovered among the instances of class of classification datasets. An advance extended binomial GLMBoost (EBGLMBoost) coupled with synthetic minority over-sampling technique (SMOTE) technique is the proposed model in the study to manage imbalance issues. The SMOTE is used to solve the proposed model, ensuring that the target variable's distribution is balanced, whereas the GLMBoost ensemble techniques are built to deal with imbalanced datasets. For the entire experiment, twenty different datasets are used, and support vector machine (SVM), Nu-SVM, bagging, and AdaBoost classification algorithms are used to compare with the suggested method. The model's sensitivity, specificity, geometric mean (G-mean), precision, recall, and F-measure resulted in percentages for training and testing datasets are 99.37, 66.95, 80.81, 99.21, 99.37, 99.29 and 98.61, 54.78, 69.88, 98.77, 96.61, 98.68, respectively. With the help of the Wilcoxon test, it is determined that the proposed technique performed well on unbalanced data. Finally, the proposed solutions are capable of efficiently dealing with the problem of class imbalance.
Randomness properties of sequence generated using logistic map with novel permutation and substitution techniques
Authors: Gopalakrishna Saraswathy Pushpalatha , Siddaiah Ramesh
Published: April 2023

In this paper, a design of a chaos-based keystream generator (KSG) using a novel permutation technique with various two-dimensional patterns and a substitution technique with Z4 mapping is proposed. Initially, a chaotic function such as a logistic map is used to generate a pseudo-random number. Then these numbers are converted into binary sequences using binary mapping. In order to achieve statistical properties of the resultant binary sequences, a novel method of KSG is developed by considering parameters such as initial value “x0”, system parameter “r”, novel permutation techniques defined by 2-dimensional patterns, and substitution technique defined over Z4 transformation. The binary sequences so obtained are subjected to randomness tests by applying the National Institute of Standards and Technology (NIST) SP-800-22 (Revision 1a) test suite for investigation of its randomness properties to obtain suitable sequences which can be used as a key for cryptographic applications. From the results obtained, it is found that the binary sequences exhibit better randomness properties as per the cryptographic requirements.
Risk management framework in Agile software development methodology
Authors: Zahedi Mohammad Hadi , Rabiei Kashanaki Alireza , Farahani Elham
Published: April 2023

In software projects that use the Agile methodology, the focus is on development in small iterations to allow both frequent changes and client involvement. This methodology affects the risks that may happen in Agile software projects. Hence, these projects need a clear risk management process to reduce risks and address the problems before they arise. Most software production methodologies must use a framework for risk management, but currently, there is no such framework for the Agile methodology. Therefore, we present a risk management framework for projects that use the Agile methodology to help the software development process and increase the likelihood of the project’s success. The proposed framework states the necessary measures for risk management according to the ISO31000 standard at each stage of the Agile methodology. We evaluated the proposed framework in two running software projects with an Agile methodology by a number of expert experts. The results show that using our proposed framework increases the average positive risk reaction score by 49%.
Efficiency analysis trees as a tool to analyze the quality of university education
Authors: Zuluaga-Ortiz Rohemi , Camelo-Guarin Alicia , Delahoz-Domínguez Enrique
Published: April 2023

This research aims to design a quality management tool in education. The methodology comprises two stages: first, the construction of the decision tree model, and second, the efficiency evaluation. For the validation and development of this research, the data modelled corresponds to the standardized exams for higher education in Colombia of ninety industrial engineering degrees. Among the results, the citizenship skills (CC_PRO) generate the most significant contribution to the model. On the other hand, the written communication competence (CE_PRO) generates a minor contribution to the model. In addition, the most relevant result of the research is the design and validation of a tool to estimate educational efficiency using the efficiency analysis tree (EAT) and data enveloping analysis (DEA) models. The proposed tool allows the generation of specific targets to increase the level of efficiency of universities through the nodes of the decision tree, which contributes to the spectrum of knowledge on models for educational management. In conclusion, this research presents a tool for the management of educational processes through the analysis of efficiency using EAT, estimating the efficiency of universities and setting the foundations for forecasting future efficiency scenarios.
Bayes model for assessing the reading difficulty of English text for English education in Jordan
Authors: Qawasmeh Yasser , Al-Radaideh Qasem , AlQuraan Addy , Otoom Ahmed Fawzi
Published: April 2023

Predicting the reading difficulty level of English texts is a critical process for second language education and assessment. Reading difficulty level is concerned with the problem of matching a reader’s proficiency and the appropriate text. The reading difficulty level or readability assessment is the process for predicting the reading grade level required from an input text or document, which corresponds to the reader and to the materials. Students in Jordan at their academic levels find obstacles in finding relevant readable data for any subject at their levels. This paper is intended to introduce a model that foretells the reading difficulty level of a given text in terms of a student's ability to read and understand English as a non-native English speaker in Jordanian schools. In this paper, Jordanian students were classified into four categories according to their knowledge of English. The prediction of the reading difficulty level is achieved by using a modern statistical model that is situated on the Bayes model. The model compares the given text with some standard predefined text that strongly reflects the ability to read and understand English text. The accuracy of the proposed model was tested using the hold-out method. The overall prediction accuracy was 75.9%.
Detection of chest pathologies using autocorrelation functions
Authors: Abdikerimova Gulzira , Shekerbek Ainur , Tulenbayev Murat , Beglerova Svetlana , Zakharevich Elena , Bekmagambetova Gulmira , Manbetova Zhanat , Baibulova Makbal
Published: April 2023

An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction.
Improved ciphertext-policy time using short elliptic curve Diffie–Hellman
Authors: Wuttidittachotti Pongpisit , Natho Parinya
Published: April 2023

Ciphertext-policy attribute-based encryption (CP-ABE) is a suitable solution for the protection of data privacy and security in cloud storage services. In a CP-ABE scheme which provides an access structure with a set of attributes, users can decrypt messages only if they receive a key with the desired attributes. As the number of attributes increases, the security measures are strengthened proportionately, and they can be applied to longer messages as well. The decryption of these ciphertexts also requires a large decryption key which may increase the decryption time. In this paper, we proposed a new method for improving the access time to the CP using a new elliptic curve that enables a short key size to be distributed to the users that allows them to use the defined attributes for encryption and decryption. Each user has a specially created key which uses the defined attributes for encryption and decryption based on the Diffie-Hellman method. After the implement, the results show that this system saves nearly half of the execution time for encryption and decryption compared to previous methods. This proposed system provides guaranteed security by means of the elliptic curve discrete logarithmic problem.
Implementation of recurrent neural network for the forecasting of USD buy rate against IDR
Authors: Moonlight Lady Silk , Trilaksono Bambang Riyanto , Harianto Bambang Bagus , Faizah Fiqqih
Published: April 2023

This study implements a recurrent neural network (RNN) by comparing two RNN network structures, namely Elman and Jordan using the backpropagation through time (BPTT) programming algorithm in the training and forecasting process in foreign exchange forecasting cases. The activation functions used are the linear transfer function, the tan-sigmoid transfer function (Tansig), and the log-sigmoid transfer function (Logsig), which are applied to the hidden and output layers. The application of the activation function results in the log-sigmoid transfer function being the most appropriate activation function for the hidden layer, while the linear transfer function is the most appropriate activation function for the output layer. Based on the results of training and forecasting the USD against IDR currency, the Elman BPTT method is better than the Jordan BPTT method, with the best iteration being the 4000th iteration for both. The lowest root mean square error (RMSE) values for training and forecasting produced by Elman BPTT were 0.073477 and 122.15 the following day, while the Jordan backpropagation RNN method yielded 0.130317 and 222.96 also the following day.
 
Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm
Authors: Rajayyan Sivakani , Mohamed Mustafa Syed Masood
Published: April 2023

Dementia is a brain disease that stays in the seventh position of death rate as per the report of the World Health Organization (WHO). Among the various types of dementia, Alzheimer’s disease has more than 70% of cases of dementia. The objective is to predict dementia disease from the open access series of imaging studies (OASIS) dataset using machine learning techniques. Also, the performance of the machine learning model is analyzed to improve the performance of the model using the cuckoo algorithm. In this paper, feature engineering has been focused and the prediction of dementia has been done using the OASIS dataset with the help of data mining techniques. Feature engineering is followed by prediction using the machine learning model Gaussian naïve Bayes (NB), support vector machine, and linear regression. Also, the best prediction model has been selected and done the validation. The evaluation metrics considered for validating the models are accuracy, precision, recall, and F1-Score and the highest values are 95%, 97%, 95%, and 95%. The Gaussian NB has been given these best results. The accuracy of the machine learning models has been increased by eliminating the factors which affect the performance of the models using the cuckoo algorithm.
A machine learning model for predicting innovation effort of firms
Authors: Rani Ruchi , Kumar Sumit , Patil Rutuja Rajendra , Pippal Sanjeev Kumar
Published: April 2023

Classification and regression tree (CART) data mining models have been used in several scientific fields for building efficient and accurate predictive models. Some of the application areas are prediction of disease, targeted marketing, and fraud detection. In this paper we use CART which widely used machine learning technique for predicting research and development (R&D) intensity or innovation effort of firms using several relevant variables like technical opportunity, knowledge spillover and absorptive capacity. We found that accuracy of CART models is superior to the often-used linear parametric models. The results of this study are considered necessary for both financial analysts and practitioners. In the case of financial analysts, it establishes the power of data-driven prototypes to understand the innovation thinking of employees, whereas in the case of policymakers or business entrepreneurs, who can take advantage of evidence-based tools in the decision-making process.
Towards a new intelligent traffic system based on deep learning and data integration
Authors: Slimani Nadia , Yousfi Siham , Amghar Mustapha , Sbiti Nawal
Published: April 2023

Time series forecasting is an important technique to study the behavior of temporal data in order to forecast the future values, which is widely applied in intelligent traffic systems (ITS). In this paper, several deep learning models were designed to deal with the multivariate time series forecasting problem for the purpose of long-term predicting traffic volume. Simulation results showed that the best forecasts are obtained with the use of two hidden long short-term memory (LSTM) layers: the first with 64 neurons and the second with 32 neurons. Over 93% of the forecasts were made with less than ±2.0% error. The analysis of variances is mainly due to peaks in some extreme conditions. For this purpose, the data was then merged between two different sources: electromagnetic loops and cameras. Data fusion is based on a calibration of the reliability of the sources according to the visibility conditions and time of the day. The integration results were then compared with the real data to prove the improvement of the prediction results in peak periods after the data fusion step.
 
Hyperparameters analysis of long short-term memory architecture for crop classification
Authors: Sher Madiha , Minallah Nasru , Ahmad Tufail , Khan Waleed
Published: April 2023

Deep learning (DL) has seen a massive rise in popularity for remote sensing (RS) based applications over the past few years. However, the performance of DL algorithms is dependent on the optimization of various hyperparameters since the hyperparameters have a huge impact on the performance of deep neural networks. The impact of hyperparameters on the accuracy and reliability of DL models is a significant area for investigation. In this study, the grid Search algorithm is used for hyperparameters optimization of long short-term memory (LSTM) network for the RS-based classification. The hyperparameters considered for this study are, optimizer, activation function, batch size, and the number of LSTM layers. In this study, over 1,000 hyperparameter sets are evaluated and the result of all the sets are analyzed to see the effects of various combinations of hyperparameters as well the individual parameter effect on the performance of the LSTM model. The performance of the LSTM model is evaluated using the performance metric of minimum loss and average loss and it was found that classification can be highly affected by the choice of optimizer; however, other parameters such as the number of LSTM layers have less influence.
Classification of electroencephalography using cooperative learning based on participating client balancing
Authors: Meqdad Maytham N. , Husain Saif O. , Jawad Alyaa Mohammed , Kadry Seifedine , Khekan Ahlam R.
Published: April 2023

Modern technologies are widely used today to diagnose epilepsy, neurological disorders, and brain tumors. Meanwhile, it is not cost-effective in terms of time and money to use a large amount of electroencephalography (EEG) data from different centers and collect them in a central server for processing and analysis. Collecting this data correctly is challenging, and organizations avoid sharing their and client information with others due to data privacy protection. It is difficult to collect these data correctly and it is challenging to transfer them to research centers due to the privacy of the data. In this regard, collaborative learning as an extraordinary approach in this field paves the way for the use of information repositories in research matters without transferring the original data to the centers. This study focuses on the use of a heterogeneous client balancing technique with an interval selection approach and classification of EEG signals with ResNet50 deep architecture. The test results achieved an accuracy of 99.14 compared to similar methods.
EdgeFall: a promising cloud-edge-end architecture for elderly fall care
Authors: Shahiduzzaman Kazi Md , Yusuf Md. Salah Uddin
Published: April 2023

Elder citizens face sudden fall, which can lead to injuries of both destructive and non-virulent. These sudden falls are later more precarious than diseases like heart attack, blood sugar, blood pressure because these can be untreated for a lengthy time which can lead to death. Elder citizen who experiences a precipitous fall, carry out their communal life narrowed. Therefore, a shrewd and adequate anti-fallen system is required for aiding elderly health care, specifically to those who live individually. So, it can identify and anticipate a precipitous fall through appropriate human activity recognition. In this study, we have suggested an end-edge-cloud based wearable EdgeFall architecture for elderly care. We have performed simulation setups to clarify the query of why we need such a strategy, and its validity. We have achieved maximum 91.87% accuracy with 1.6% false alarm rate (FAR). These empirical results indicate the superiority of using tightly couple multiple information for recognizing human activity. We can accomplish a low FAR with an enhanced accuracy. We can observe that our proposed end-edge-cloud based architecture can reduce the execution time to millisecond range (ms) of 14.16 to 15.74. This work serves as the starting mark for future related research activities.
Virtual machine placement in cloud using artificial bee colony and imperialist competitive algorithm
Authors: Javadi-Moghaddam Seyyed-Mohammad , Dehghani Zahra
Published: April 2023

Increasing resource efficiency and reducing energy consumption are significant challenges in cloud environments. Placing virtual machines is essential in improving cloud systems’ performance. This paper presents a hybrid method using the artificial bee colony and imperialist competitive algorithm to reduce provider costs and decrease client expenditure. Implementation of the proposed plan in the CloudSim simulation environment indicates the proposed method performs better than the Monarch butterfly optimization and salp swarm algorithms regarding energy consumption and resource usage. Moreover, average central processing unit (CPU) and random-access memory (RAM) usage and the number of host shutdowns show better results for the proposed model.
Approaches for smart linear regression in a difficult quasi economic dispatch problem
Authors: Gachhayat Susanta Kumar , Dash Saroja Kumar , Deo Banalata Priyadarshani
Published: April 2023

Traditional methods indispensably necessitate monotonically increasing characteristic for fuel cost of generators in a thermal power plant. However, in medium and large thermal power plants, this condition is a dream to accomplish. So, to meet out these exigencies heuristic methods like swarm optimization technique, genetic algorithm technique and bee colony based hybrid solar thermal technology (BHSTT) are used to realize the practical nonlinearities associated with valve point loading emanated out of multi-valving effect, associated with power station. However, the heuristic methods too face challenges arising out of bulky thermal power plants adopting cubic cost functions and possessing stringent non-convex economic dispatch problem following multi-valving and erratic behavior of nonlinear loads at the load center. So, at its favor function evaluation method dealing with cubic cost function is attempted in this dissertation to yield a satisfactory optimal solution for economic dispatch problem. This method deals with the real power generation of producing units as well as the complex power of units, as well as dealing with severe nonlinear stringent fuel cost characteristics that are prevalent in today’s bulky thermal power plants. In comparison to previous approaches, the findings achieved are highly encouraging.
3D human hands rendering by a six degrees of freedom collaborative robot and a single 2D camera
Authors: Boyack Michael , Sices Alexsandra , Jo Bruce Woongyeol
Published: April 2023

Human hands are essential in everyday tasks, mainly manipulating and grasping objects. Thus, accurate and precise three-dimensional (3D) models of digitally reconstructed hands are valuable to the world of ergonomics. A 3D scan-to-render system called the “3D hands model rendering using a 6-degrees of freedom (DoF) collaborative robot” is proposed to ensure that a person receives the best possible outcome for their unique anatomy. The description implies this is using a 6-DoF robot with a two-dimensional (2D) camera sensor that will encompass all forms of the production line in a timely, low-cost, precise, and accurate manner so that an individual can go to and scan their hand and have an actual 3D reconstruction print within the same facility, the same day. It is expected to generate an accurate hand model using structure from motion (SFM) system techniques to create a dense point cloud using photogrammetry. The point cloud is used to develop the tetrahedral mesh of the surface of the hand. This mesh is then refined to filter out the noise of the point cloud. The mesh can produce a precise 3D model that can tailor products to the consumer's needs. The results show the effectiveness of the 3D model of the hand.
Assembling, simulating, and recording robot videos as an effort to motivate middle school students and teachers of Science in Bengkulu Province
Authors: Mayub Afrizal , Fahmizal Fahmizal , Firdaus M. Lutfi , Johan Henny , Ruyani Aceng , Karyadi Bhakti
Published: April 2023

The aim of this study was to determine the motivation of science teachers and students towards science after participating in the activity of assembling, simulating, and recording line follower robots as an effort to motivate middle school students and teachers towards science in Bengkulu Province. The research was done by direct practicing, where 60 students and 15 teachers of three junior high school (SMP): SMP Negeri 06 Seluma, SMP Negeri 02 Kota Bengkulu, and SMP Negeri 8 Rejang Lebong, were involved as the research subjects. The research activity concluded that the schools are ready to prepare simple electronics/robot laboratories for the three research subjects and the science teachers and students were motivated to learn science. It was seen from the score of 3.95 (scale of 1 to 5) for students, and for the science teacher, the score was 3.83 (scale of 1 to 5). The science teachers will follow up on robotics activities so that students will be interested in learning science at home and school.
Direitos humanos e os trabalhos da imaginação: uma etnografia da ordenação da primeira reverenda trans da América Latina
Authors: Barbosa Olívia Alves , Silva Aramis Luis , Nagamine Renata
Published: April 2023

Este texto está articulado em torno da etnografia da ordenação clerical de Alexya Salvador, primeira pastora transgênero latino-americana. Interessada em descrever e refletir sobre possíveis modos contemporâneos de correlacionar as categorias religião e direitos humanos, a análise coloca em foco a produção de cenas icônicas, artefatos de comunicação que inscrevem quadros vivos em redes orientadas a redefinir os sentidos dessas categorias a partir de experiências concretas e comunicáveis. Sob este enquadramento, direitos humanos podem ser compreendidos como uma linguagem em ação que possibilita a agentes que se denominam e são reconhecidos como religiosos dar forma e expressão às suas imaginações, as quais desconhecem fronteiras estabilizadas entre o político, o religioso e, neste caso, o erótico.
Perfil epidemiológico da hepatite B em Alagoas no período de 2010-2020
Authors: Araujo Isabela Macêdo , Anjos Thaynara Melo dos , Lemos Dinário Augusto , Amaral Matheus de Andrade , Tavares Mariana Mendonça de Araújo , Medeiros Alba Letícia Peixoto , Carnaúba Aline Tenório Lins
Published: April 2023

Introdução: Mais de 13.500 pessoas no Brasil foram infectadas com o vírus da Hepatite B em 2019. Sabe-se que sua prevalência não é uniformemente distribuída, variando de acordo com a região do país, nível socioeconômico e vulnerabilidade da população. O estado de Alagoas abrigou cerca de 10,83% dos casos de Hepatite B da região Nordeste durante o período de 2010-2020, porém, a literatura carece de informações acerca do perfil epidemiológico da doença no estado. Objetivos: Avaliar a prevalência e fatores associados à Hepatite B no estado de Alagoas no período de 2010-2020. Métodos: Trata-se de um estudo de corte transversal com análise de dados secundários da Secretaria do Estado de Saúde de Alagoas, no período de 2010-2020. As variáveis analisadas foram: casos notificados e óbitos por hepatite B, sexo, idade, cor, forma de transmissão e nível de escolaridade. Foram calculadas as taxas de prevalência, mortalidade e letalidade. Utilizou-se estatística descritiva, com cálculos de média e desvio padrão, frequências absolutas e relativas. Resultados: 1.420 casos de Hepatite B foram notificados no estado de Alagoas durante o período de 2010 a 2019, tendo como destaque o último ano citado, responsável por 18,23% dos casos. No ano de 2020, foram notificados 102 casos, com o mês de maior notificação sendo fevereiro, com 19,60% dos casos e, os de menor, maio e junho, com 1,96% cada. A população mais afetada foi do sexo feminino, faixa etária de 20-39 anos, nível de escolaridade fundamental incompleto e forma de transmissão sexual. Conclusões: Os casos da Hepatite B em Alagoas cresceram de forma não linear no período de 2010-2019. Entretanto, no ano de 2020, marcado pela pandemia do Sars-Cov-2, houve queda no número de casos, o que pode ter sido causado por melhora nas políticas públicas ou por subnotificação dos casos.
Determinação hemodinâmica do risco de fluxo turbulento sobre o cisalhamento aterosclerótico e endotelial em regimes de estenose de lúmen coronariano
Authors: Ferreira Mario Henrique Quim , Fumagalli Beatriz Cheregati , Teixeira Ana Beatriz , Silveira Neto Aristeu da , Barbosa Leandro Kefalás
Published: April 2023

Introdução: O infarto agudo do miocárdio é considerado como uma das principais condições clínicas causadoras de mortes no mundo. Sua prevalência denota sua importância e diversos motivos podem culminar na obstrução repentina do lúmen coronariano. Objetivo: determinar a influência da tensão de cisalhamento para a detecção precoce de placas de ateroma sujeitas a riscos de ruptura ou erosão, tornando-se possível prever o infarto nestas situações. Metodologia: artigos foram selecionados nos bancos de dados PubMed, Lilacs, Medline, SciELO e Google Scholar, utilizando descritores DeCS e MeSH para homogeneizar os resultados. Por serem recentes as descobertas neste campo de estudo, não foram utilizados filtros para a pesquisa, empregando linearmente os mencionados artigos acima, para embasamento desta revisão. Resultados / discussão: Os modelos trazidos da mecânica dos fluídos associados a medidas químicas/biológicas de detecção por exames laboratoriais e de imagem têm apontado a tensão de cisalhamento patológica como um estímulo para lesão de placas de ateroma e consequente exposição à possibilidade de obstrução abrupta de lúmen coronariano. Conclusão: Muitos dos ensaios atuais vêm apontando para esse possível pilar de investigação, entretanto, diversas variáveis envolvem o tema, ainda sendo necessário um aprofundamento nos estudos teóricos e práticos para fortalecimento e mapeamento dos detalhes deste aspecto mecânico, em relação consequente ao infarto.
Princípios gerais de tratamento da dor músculo-esquelética
Authors: Teixeira Manoel Jacobsen , Teixeira William Gemio Jacobsen , Viveiros Viviane Passadori
Published: April 2023

A dor apresenta várias dimensões que devem ser avaliadas e normalizadas quando se objetiva seu controle. O tratamento da dor deve ser fundamentado na eliminação dos fatores causais e, quando necessário, no uso de procedimentos analgésicos farmacológicos, medicina física, procedimentos psicocomportamentais, procedimentos neuroanestésicos e ou neurocirúrgicos funcionais. As medidas devem ser simples, seguras, de baixo custo operacional e accessíveis. O tratamento farmacológico consiste no uso de analgésicos antiinflamatórios não hormonais, analgésicos opióides e medicação adjuvantes representada por antidepressivos tricíclicos, neurolépticos, anticonvulsivantes, corticosteróides, moduladores da atividade adrenérgica e serotoninérgica, miorrelaxantes etc. Dentre esses, os agentes analgésicos antiinflamatórios e os opióides associados aos psicotrópicos e miorrelaxantes são os mais empregados no tratamento da dor músculo-esquelética. Procedimentos de medicina física são necessários no tratamento da grande maioria das afecções álgicas músculo-esqueléticas. As infiltrações dos pontos gatilhos são procedimentos bastante úteis no tratamento da síndrome dolorosa miofascial. A descompressão de estruturas neurais, o implante de sistemas para a infusão de medicação analgésica no sistema nervoso central, a lesão por radiofreqüência, dos nervos recorrentes posteriores e a coagulação do disco intervertebral são indicados em casos rebeldes.