bluuebunny/crossref_metadata_2025 huggingface dataset:
Drone’s node placement algorithm with routing protocols to enhance surveillance
Authors: Akut Emmanuel Kasai , Usman Aliyu Danjuma , Abubilal Kabir Ahmad , Bello Habeeb , Salawudeen Ahmed Tijani , Yaro Abdulmalik Shehu
Published: April 2023

Flying ad-hoc network (FANET) is characterized by key component features such as communication scheme, energy awareness, and task distribution. In this research, a surveillance space considering standard petroleum pipe was created with three drones viz: drone 1 (D1), master drone (DM), and drone 2 (D2) to survey as FANET. DM aggregate packets from D1, D2 and communicate with the static ground control station (SGCS). The starting point of the three drones and their trajectories during deployment were calculated and simulated. Selection of DM, D1, and D2 was done using battery level before take-off. Simulation results show take-off time difference which depends on the distance of each drone to the SGCS during deployment. D1 take-off first, while DM and D2 followed after 0.0704 and 0.1314 ms respectively. The position-oriented routing protocols results indicated variation of information flow within time notch due to variation in the density of the transmitted packets. Packets delivery periods are 0.00136×103 sec, 0.00110×103 sec, and 0.00246×103 sec for time notch 1, 2, and aggregating time notch respectively. From the results obtained, two algorithms were used successfully in deploying the drones
Analysis on the performance of reconfigurable intelligent surface-aided free-space optical link under atmospheric turbulence and pointing errors
Authors: Ai Duong Huu , Dang Dai Tho , Anh Quang Nguyen Vu , Nguyen Van Loi
Published: April 2023

Free-space optical (FSO) communication can provide the cost-efficient, secure, high data-rate communication links required for applications. For example, it provides broadband internet access and backhauling for the fifth-generation (5G) and the sixth-generation (6G) communication networks. However, previous solutions to deal with signal loss caused by obstructions and atmospheric turbulence. In these solutions, reconfigurable intelligent surfaces (RISs) are considered hardware technology to improve the performance of optical wireless communication systems. This study investigates the pointing error effects for RIS-aided FSO links under atmospheric turbulence channels. We analyze the performance of RIS-aided FSO links influenced by pointing errors, atmospheric attenuation, and turbulence for the subcarrier quadrature amplitude modulation (SC-QAM) technique. Atmospheric turbulence is modeled using log-normal distribution for weak atmospheric turbulence. Several numerical outcomes obtained for different transmitter beam waist radius and pointing error displacement standard deviation are shown to quantitatively illustrate the average symbol error rate (ASER).
A new contraction based on ℋ-simulation functions in the frame of extended b-metric spaces and application
Authors: Qawasmeh Tariq , Hatamleh Raed
Published: April 2023

The conceptions of b-metric spaces and metric spaces play a remarkable role in proving many theorems of uniqueness and existence solution of such equations as integral or differential equations. The conception of extended b-metric spaces is considered as a generalization concept of b-metric spaces and metric spaces and this concept was employed to unify some new fixed point results in the literature. On the other hand, a new concept of simulation functions founded in 2020 in the name of ℋ-simulation functions and employed this class of functions to unify some fixed point results in the literature. The main objective of this manuscript, is to establish a new contraction namely, (γ, ϕ, θ) ℋ-contraction, this contraction based on the concepts of extended b-metric spaces and the class of functions (ℋ-simulation functions) and the class of functions Θ-functions and the class of functions Φ-functions, We utilize this new contraction to unify the uniqueness and existence of fixed point results in the literature. In addition, some illustrative examples and an interesting application were established to show the novelty of our work.
Realtime human face tracking and recognition system on uncontrolled environment
Authors: Thary Al-Ghrairi Assad H. , Hussein Fallooh Al-Anbari Noor , Zuhair Sameen Esraa
Published: April 2023

Recently, one of the most important biometrics is that automatically recognized human faces are based on dynamic facial images with different rotations and backgrounds. This paper presents a real-time system for human face tracking and recognition with various expressions of the face, poses, and rotations in an uncontrolled environment (dynamic background). Many steps are achieved in this paper to enhance, detect, and recognize the faces from the image frame taken by web-camera. The system has three steps: the first is to detect the face, Viola-Jones algorithm is used to achieve this purpose for frontal and profile face detection. In the second step, the color space algorithm is used to track the detected face from the previous step. The third step, principal component analysis (eigenfaces) algorithm is used to recognize faces. The result shows the effectiveness and robustness depending on the training and testing results. The real-time system result is compared with the results of the previous papers and gives a success, effectiveness, and robustness recognition rate of 91.12% with a low execution time. However, the execution time is not fixed due depending on the frame background and specification of the web camera and computer.
An improved extrinsic monolingual plagiarism detection approach of the Bengali text
Authors: Ahnaf Adil , Hasan Hossain Mohammad Mahmudul , Sworna Nabila Sabrin , Hossain Nahid
Published: April 2023

Plagiarism is an act of literature fraud, which is presenting others’ work or ideas without giving credit to the original work. All published and unpublished written documents are under the cover of this definition. Plagiarism, which increased significantly over the last few years, is a concerning issue for students, academicians, and professionals. Due to this, there are several plagiarism detection tools or software available to detect plagiarism in different languages. Unfortunately, negligible work has been done and no plagiarism detection software available in the Bengali language where Bengali is one of the most spoken languages in the world. In this paper, we have proposed a plagiarism detection tool for the Bengali language that mainly focuses on the educational and newspaper domain. We have collected 82 textbooks from the National Curriculum of Textbooks (NCTB), Bangladesh, scrapped all articles from 12 reputed newspapers and compiled our corpus with more than 10 million sentences. The proposed method on Bengali text corpus shows an accuracy rate of 97.31%
Hypertext transfer protocol performance analysis in traditional and software defined networks during Slowloris attack
Authors: Murthy Anusha A. , Mabel John Prathima , Babu Kasturi Nagappasetty Rama Mohan
Published: April 2023

The extensive use of the internet has resulted in novel technologies and protocol improvisation. Hypertext transfer protocol/1.1 (HTTP/1.1) is widely adapted on the internet. However, HTTP/2 is found to be more efficient over transport control protocol (TCP). The HTTP/2 protocol can withstand the payload overhead when compared to HTTP/1.1 by multiplexing multiple requests. However, both the protocols are highly susceptible to application-level denial of service (DoS) attacks. In this research, a slow-rate DoS attack called Slowloris is detected over Apache2 servers enabled with both versions of HTTP in traditional networks and software defined networks (SDN). Server metrics such as server connection time to the webpage, latency in receiving a response from the server, page load time, response-response gap, and inter-packet arrival time at the server are monitored to analyze attack activity. A Monte Carlo simulation is used to estimate threshold values for server connection time and latency for attack detection. This work is implemented in a lab environment using virtual machines, Ryu controller, zodiac FX OpenFlow switch and Apache2 servers. This study also highlights SDN's security benefits over traditional networks.
A bipartite graph based proportional fair scheduling strategy to improve throughput with multiple resource blocks
Authors: Kesavan Dhivya , Periathambi Ezhumalai , Chockkalingam Arun
Published: April 2023

The fifth-generation wireless communication is expected to provide a huge amount of capacity to cater to the need of an increasing number of mobile consumers, which can be satisfied by device-to-device (D2D) communication. Reusing the cellular user’s resources in an efficient manner helps to increase the spectrum efficiency of the network but it leads to severe interference. The important point in reusing cellular user resources is that D2D communication should not affect the cellular user’s efficiency. After achieving this requirement, the focus is now turned toward the allocation of resources to D2D communication. This resource allocation strategy is to be designed in such a way that it will not affect communication among the cellular user (CU). This scheme improves various performance objectives. This paper aims at designing a proportional fair resource allocation algorithm based on the bipartite graph which maintains the quality of service (QoS) of CUs while providing D2D communication. This algorithm can be merged with any other scheme of resource allocation for improving QoS and adopting changing channels. In this scheme, a D2D pair can be allocated with one or more than one resource blocks. The MATLAB simulations analyze the performance of the proposed scheme.
Effective dashboards for urban water security monitoring and evaluation
Authors: Zainuddin Zada Qusyairin Mohd , Yahya Farashazillah , Gubin Moung Ervin , Mohd Fazli Bashirah , Abdullah Mohammad Fikry
Published: April 2023

This paper reviews the factors affecting effective dashboards for urban water security monitoring and evaluation. Urban water security is a constantly evolving field influenced by several factors, including changes in climate, ecosystems, socio-economic status, and human beings. Although urban water security has been discussed in some parts of the literature, there has been minimal literature review that focused on the factors of urban water security and the effective dashboards for monitoring and evaluation. Using systematic literature review (SLR) and preferred reporting items for systematic reviews and meta-analysis (PRISMA), this paper reviewed 143 articles. The result shows growth in the environmental informatics landscape since the last ten years when the first article on the urban water management dashboard was published. The visual design was the most frequently discussed factor for dashboards, followed by user customization. It also shows that this topic can go deeper to integrate both factors and design an effective environmental dashboard. The discussion identified three potential opportunities for future research in water security and informatics: i) exploring other dimensions of effective dashboards, ii) considering more research on the environmental dashboard, and iii) investigating the real-life application of dashboards in urban water security.
Big data analytics and internet of things for personalised healthcare: opportunities and challenges
Authors: Venkatraman Sitalakshmi , Parvin Sazia , Mansoor Wathiq , Gawanmeh Amjad
Published: April 2023

With the increasing use of technologies and digitally driven healthcare systems worldwide, there will be several opportunities for the use of big data in personalized healthcare. In addition, With the advancements and availability of internet of things (IoT) based point-of-care (POC) technologies, big data analytics and artificial intelligence (AI) can provide useful methods and solutions in monitoring, diagnosis, and self-management of health issues for a better personalized healthcare. In this paper, we identify the current personalized healthcare trends and challenges. Then, propose an architecture to support big data analytics using POC test results of an individual. The proposed architecture can facilitate an integrated and self-managed healthcare as well as remote patient care by adapting three popular machine learning algorithms to leverage the current trends in IoT, big data infrastructures and data analytics for advancing personalized healthcare of the future.
Performance analysis of change detection techniques for land use land cover
Authors: Karandikar Aarti , Agrawal Avinash
Published: April 2023

Remotely sensed satellite images have become essential to observe the spatial and temporal changes occurring due to either natural phenomenon or man-induced changes on the earth’s surface. Real time monitoring of this data provides useful information related to changes in extent of urbanization, environmental changes, water bodies, and forest. Through the use of remote sensing technology and geographic information system tools, it has become easier to monitor changes from past to present. In the present scenario, choosing a suitable change detection method plays a pivotal role in any remote sensing project. Previously, digital change detection was a tedious task. With the advent of machine learning techniques, it has become comparatively easier to detect changes in the digital images. The study gives a brief account of the main techniques of change detection related to land use land cover information. An effort is made to compare widely used change detection methods used to identify changes and discuss the need for development of enhanced change detection methods.
Accurate fashion and accessories detection for mobile application based on deep learning
Authors: Thwe Yamin , Jongsawat Nipat , Tungkasthan Anucha
Published: April 2023

Detection and classification have an essential role in the world of e-commerce applications. The recommendation method that is commonly used is based on information text attached to a product. This results in several recommendation errors caused by invalid text information. In this study, we propose the development of a fashion category (FC-YOLOv4) model in providing category recommendations to sellers based on fashion accessory images. The resulting model was then compared to YOLOv3 and YOLOv4 on mobile devices. The dataset we use is a collection of 13,689, which consists of five fashion categories and five accessories' categories. Accuracy and speed analysis were performed by looking at mean average precision (mAP) values, intersection over union (IoU), model size, loading time, average RAM usage, and maximum RAM usage. From the experimental results, an increase in mAP was obtained by 99.84% and an IoU of 88.49 when compared to YOLOv3 and YOLOv4. Based on these results, it can be seen that the models we propose can accurately identify fashion and accessories categories. The main advantage of this paper lies in i) providing a model with a high level of accuracy and ii) the experimental results presented on a smartphone.
Approximation-based homomorphic encryption for secure and efficient blockchain-driven watermarking service
Authors: Rosiyadi Didi , Basuki Akbari Indra , Ramdhani Taufik Iqbal , Susanto Heru , Siregar Yusnan Hasani
Published: April 2023

Homomorphic encryption has been widely used to preserve the privacy of watermarking process on blockchain-driven watermarking services. It offers transparent and traceable encrypted watermarking without revealing sensitive data such as original images or watermark data to the public. Nevertheless, the existing works suffer from enormous memory storage and extensive computing power. This study proposed an approximation-based homomorphic encryption for resource-efficient encrypted watermarking without sacrificing watermarking quality. We demonstrated the efficiency of the Cheon-Kim-Kim-Son (CKKS) encrypted watermarking process using discrete cosine transform-singular value decomposition (DCT-SVD) embedding. The evaluation results showed that it could preserve the watermarking quality similar to non-encrypted watermark embedding, even after geometrical and filtering attacks. Compared to existing homomorphic encryption, such as Brakerski-Gentry-Vaikuntanathan (BFV) encryption, it has superior performance regarding resource utilization and watermarking quality preservation.
Evaluation of wind-solar hybrid power generation system based on Monte Carlo method
Authors: Niu Yitong , Mohammed Merza Ahmed , Ibraheem Kadhem Suhad , Tawfeq Jamal Fadhil , Soon JosephNg Poh , Gheni Hassan Muwafaq
Published: April 2023

The application of wind-photovoltaic complementary power generation systems is becoming more and more widespread, but its intermittent and fluctuating characteristics may have a certain impact on the system's reliability. To better evaluate the reliability of stand-alone power generation systems with wind and photovoltaic generators, a reliability assessment model for stand-alone power generation systems with wind and photovoltaic generators was developed based on the analysis of the impact of wind and photovoltaic generator outages and derating on reliability. A sequential Monte Carlo method was used to evaluate the impact of the wind turbine, photovoltaic (PV) turbine, wind/photovoltaic complementary system, the randomness of wind turbine/photovoltaic outage status and penetration rate on the reliability of Independent photovoltaic power generation system (IPPS) under the reliability test system (RBTS). The results show that this reliability assessment method can provide some reference for planning the actual IPP system with wind and complementary solar systems.
Internet of things-blockchain lightweight cryptography to data security and integrity for intelligent application
Authors: Parmar Martin , Shah Parth
Published: April 2023

The industrial internet of things (IoT) plays a major role in the growth of automation and increasing digital connectivity for machine-to-machine communication. The research community has extensively investigated the possibility of IoT and blockchain integration for the last couple of years. The major research is focused on the benefits of integrating blockchain with IoT. In this work, we first focus on the issue of integrating IoT nodes with blockchain networks, especially for non-real-time IoT nodes that do not have an in-built clock mechanism. As a result, they cannot establish communication with real-time blockchain networks. Another critical security issue is protecting data coming from IoT devices to blockchain networks. Blockchain is enough mature to protect the data in its ecosystem. However, information coming from outside of the world does not have any guarantee of data integrity and security. This paper first addresses the clock synchronization issue of IoT nodes with blockchain using a network time protocol and then proposes an IoT-blockchain light-weight cryptographic (IBLWC) approach to secure the entire IoT-blockchain ecosystem. This paper also presents the performance analysis of IBLWC as a suitable and cost-effective solution that incurs less processing overhead for IoT-blockchain-based applications.
An efficient ultra-wideband digital transceiver for wireless applications on the field-programmable gate array platform
Authors: Kumar Ramachandragowda Santhosh , Ramakrishna Devaraju , Narendra Rajashree
Published: April 2023

The ultra-wideband (UWB) technology is a promising short-range communication technology for most wireless applications. The UWB works at higher frequencies and is affected by interferences with the same frequency standards. This manuscript has designed an efficient and low-cost implementation of IEEE 802.15.4a-based UWB-digital transceiver (DTR). The design module contains UWB transmitter (TX), channel, and UWB-receiver (RX) units. Convolutional encoding and modulation units like burst position modulation and binary phase-shift keying modulation are used to construct the UWB-TX. The synchronization and Viterbi decoder units are used to recover the original data bits and are affected by noise in UWB-RX. The UWB-DTR is synthesized using Xilinx ISE® environment with Verilog hardware description language (HDL) and implemented on Artix-7 field-programmable gate array (FPGA). The UWB-DTR utilizes less than 2% (slices and look-up table/LUTs), operates at 268 MHz, and consumes 91 mW of total power on FPGA. The transceiver achieves a 6.86 Mbps data rate, which meets the IEEE 802.15.4a standard. The UWB-DTR module obtains the bit error rate (BER) of 2×10-4 by transmitting 105 data bits. The UWB-DTR module is compared with similar physical layer (PHY) transceivers with improvements in chip area (slices), power, data rate, and BER.  
Internal combustion engine gearbox bearing fault prediction using J48 and random forest classifier
Authors: Somehalli Kapanigowda Nithin , Krishna Hemanth , Vasanth Shamanth , Thammaiah Ananthapadmanabha
Published: April 2023

Defective bearings in four-stroke engines can compromise performance and efficiency. Early detection of bearing difficulties in 4-stroke engines is critical. Four-stroke gasoline engines that vibrate or make noise can be used to diagnose issues. Using time, frequency, and time-frequency domain approaches, the vibrational features of healthy and diseased tissues are examined. Problems are only detectable by vibration or sound. The fault is identified through statistical analysis of seismic and audio data using frequency and time-frequency analysis. Vibration must be minimized prior to examination. Adaptive noise cancellation removes unwanted noise from recorded vibration signals, boosting the signal-to-noise ratio (SNR). In the first of the experiment's three phases, vibrational data are collected. To reduce noise and boost SNR, adaptive noise cancellation (ANC) is applied to vibration data from the first stage. In the second stage, ANC-filtered vibration data is subjected to three studies to detect bearing failure using J48 and random forest classifiers for online, real-time monitoring. In this experiment, one healthy and two faulty bearings are used. According to a current study, the internet of things (IoT) is a promising alternative for online monitoring of remote body health.
Server virtualization in higher educational institutions: a case study
Authors: Koratagere Sreelakshmi , Chandrashekarappa Koppal Ravi Kumar , Umesh Iyyanahalli Math
Published: April 2023

Virtualization is a concept in which multiple guest operating systems share a single piece of hardware. Server virtualization is the widely used type of virtualization in which each operating system believes that it has sole control of the underlying hardware. Server virtualization has already got its place in companies. Higher education institutes have also started to migrate to virtualized servers. The motivation for higher education institutes to adopt server virtualization is to reduce the maintenance of the complex information technology (IT) infrastructure. Data security is also one of the parameters considered by higher education institutes to move to virtualization. Virtualization enables organizations to reduce expenditure by avoiding building out more data center space. Server consolidation benefits the educational institutes by reducing energy costs, easing maintenance, optimizing the use of hardware, provisioning the resources for research. As the hybrid mode of learning is gaining momentum, the online mode of teaching and working from home options can be enabled with a strengthened infrastructure. The paper presents activities conducted during server virtualization implementation at RV College of Engineering, Bengaluru, one of the reputed engineering institutes in India. The activities carried out include study of the current scenario, evaluation of new proposals and post-implementation review.
The role of neural network for estimating real estate prices value in post COVID-19: a case of the middle east market
Authors: Khrais Laith T. , Shidwan Osman Saad
Published: April 2023

The main goal of this paper was to explore the use of an artificial neural network (ANN) model in predicting real estate prices in the Middle East market. Although conventional modeling approaches such as regression can be used in prediction, they have a weakness of a predetermined relationship between input and output. In this regard, using the ANN model was expected to reduce the bias and ensure non-linear relationships are also covered in the prediction process for more accurate results. The ANN model was created using Python v.3.10 program. The model exhibited a high correlation between predicted and actual house price data (R=0.658). In this respect, it was realized that the model could be effectively used in appraising real estate by investors. However, a major limitation of the model was realized to be a limited dataset for large and luxurious houses, which were not accurately predicted as data distribution between actual and predicted values became sparse for high house prices. A key recommendation made is that future research should include more variables related to luxurious houses and macroeconomic factors to increase the ANN model accuracy.
Efficient machine learning classifier to detect and monitor COVID-19 cases based on internet of things framework
Authors: Bel Felcia , Selvaraj Sabeen
Published: April 2023

In this research work, coronavirus disease (COVID-19) has been considered to help mankind survive the present-day pandemic. This research is helpful to monitor the patients newly infected by the virus, and patients who have already recovered from the disease, and also to study the flow of virus from similar health issues. In this paper, an Internet of things (IoT) framework has been developed for the early detection of suspected cases. This framework is used for collecting and uploading symptoms (data) through sensor devices to the physician, data analytics center, cloud, and isolation/health centers. The symptoms of the first wave, second wave, and omicron are used to identify the suspects. Five machine learning algorithms which are considered to be the best in the existing literature have been used to find the best machine learning classifier in this research work. The proposed framework is used for the rapid detection of COVID-19 cases from real-world COVID-19 symptoms to mitigate the spread in society. This model also monitors the affected patient who has undergone treatment and recovered. It also collects data for analysis to perform further improvements in algorithms based on daily updated information from patients to provide better solutions to mankind.
Application of improved you only look once model in road traffic monitoring system
Authors: Kamble Shridevi Jeevan , Kounte Manjunath R
Published: April 2023

The present research focuses on developing an intelligent traffic management solution for tracking the vehicles on roads. Our proposed work focuses on a much better you only look once (YOLOv4) traffic monitoring system that uses the CSPDarknet53 architecture as its foundation. Deep-sort learning methodology for vehicle multi-target detection from traffic video is also part of our research study. We have included features like the Kalman filter, which estimates unknown objects and can track moving targets. Hungarian techniques identify the correct frame for the object. We are using enhanced object detection network design and new data augmentation techniques with YOLOv4, which ultimately aids in traffic monitoring. Until recently, object identification models could either perform quickly or draw conclusions quickly. This was a big improvement, as YOLOv4 has an astoundingly good performance for a very high frames per second (FPS). The current study is focused on developing an intelligent video surveillance-based vehicle tracking system that tracks the vehicles using a neural network, image-based tracking, and YOLOv4. Real video sequences of road traffic are used to test the effectiveness of the method that has been suggested in the research. Through simulations, it is demonstrated that the suggested technique significantly increases graphics processing unit (GPU) speed and FSP as compared to baseline algorithms.
Deep learning-based switchable network for in-loop filtering in high efficiency video coding
Authors: Joy Helen K. , Kounte Manjunath R
Published: April 2023

The video codecs are focusing on a smart transition in this era. A future area of research that has not yet been fully investigated is the effect of deep learning on video compression. The paper’s goal is to reduce the ringing and artifacts that loop filtering causes when high-efficiency video compression is used. Even though there is a lot of research being done to lessen this effect, there are still many improvements that can be made. In This paper we have focused on an intelligent solution for improvising in-loop filtering in high efficiency video coding (HEVC) using a deep convolutional neural network (CNN). The paper proposes the design and implementation of deep CNN-based loop filtering using a series of 15 CNN networks followed by a combine and squeeze network that improves feature extraction. The resultant output is free from double enhancement and the peak signal-to-noise ratio is improved by 0.5 dB compared to existing techniques. The experiments then demonstrate that improving the coding efficiency by pipelining this network to the current network and using it for higher quantization parameters (QP) is more effective than using it separately. Coding efficiency is improved by an average of 8.3% with the switching based deep CNN in-loop filtering.
Data driven algorithm selection to predict agriculture commodities price
Authors: Hegde Girish , Hulipalled Vishwanath R. , Simha Jay B.
Published: April 2023

Price prediction and forecasting are common in the agriculture sector. The previous research shows that the advancement in prediction and forecasting algorithms will help farmers to get a better return for their produce. The selection of the best fitting algorithm for the given data set and the commodity is crucial. The historical experimental results show that the performance of the algorithms varies with the input data. Our main objective was to develop a model in which the best-performing prediction algorithm gets selected for the given data set. For the experiment, we have used seasonal autoregressive integrated moving average (SARIMA) stack ensemble and gradient boosting algorithms for the commodities Tomato and Potato with monthly and weekly average prices. The experimental results show that no algorithm is consistent with the given commodities and price data. Using the proposed model for the monthly forecasting and Tomato, stack ensemble is a better choice for Karnataka and Madhya Pradesh states with 59% and 61% accuracy. For Potatoes with the monthly price for Karnataka and Maharashtra, the stack ensemble model gave 60% and 85% accuracy. For weekly prediction, the accuracy of gradient boosting is better compared to other models.
Bankruptcy prediction model using cost-sensitive extreme gradient boosting in the context of imbalanced datasets
Authors: Yotsawat Wirot , Phodong Kanyalag , Promrat Thawatchai , Wattuya Pakaket
Published: April 2023

In the process of bankruptcy prediction models, a class imbalanced problem has occurred which limits the performance of the models. Most prior research addressed the problem by applying resampling methods such as the synthetic minority oversampling technique (SMOTE). However, resampling methods lead to other issues, e.g., increasing noisy data and training time during the process. To improve the bankruptcy prediction model, we propose cost-sensitive extreme gradient boosting (CS-XGB) to address the class imbalanced problem without requiring any resampling method. The proposed method’s effectiveness is evaluated on six real-world datasets, i.e., the LendingClub, and five Polish companies’ bankruptcy. This research compares the performance of CS-XGB with other ensemble methods, including SMOTE-XGB which applies SMOTE to the training set before the learning process. The experimental results show that i) based on LendingClub, the CS-XGB improves the performance of XGBoost and SMOTE-XGB by more than 50% and 33% on bankruptcy detection rate (BDR) and geometric mean (GM), respectively, and ii) the CS-XGB model outperforms random forest (RF), Bagging, AdaBoost, XGBoost, and SMOTE-XGB in terms of BDR, GM, and the area under a receiver operating characteristic curve (AUC) based on the five Polish datasets. Besides, the CS-XGB model achieves good overall prediction results.
Hybrid iterated local search algorithm for optimization route of airplane travel plans
Authors: Muklason Ahmad , Agung Premananda I Gusti
Published: April 2023

The traveling salesman problem (TSP) is a very popular combinatorics problem. This problem has been widely applied to various real problems. The TSP problem has been classified as a Non-deterministic Polynomial Hard (NP-Hard), so a non-deterministic algorithm is needed to solve this problem. However, a non-deterministic algorithm can only produce a fairly good solution but does not guarantee an optimal solution. Therefore, there are still opportunities to develop new algorithms with better optimization results. This research develops a new algorithm by hybridizing three local search algorithms, namely, iterated local search (ILS) with simulated annealing (SA) and hill climbing (HC), to get a better optimization result. This algorithm aimed to solve TSP problems in the transportation sector, using a case study from the Traveling Salesman Challenge 2.0 (TSC 2.0). The test results show that the developed algorithm can optimize better by 15.7% on average and 11.4% based on the best results compared to previous studies using the Tabu-SA algorithm.
Cloud security: literature survey
Authors: Gadad Amruta , Anbusezhiyan Devi
Published: April 2023

Today, the growth of digitalization has made the ease for livelihood for all the organizations. Cloud computing the storage provider for all the computer resources has made it easy for accessing the data from anywhere anytime. But at the same time the security for cloud data storage is the major drawback which is provided by various cryptographic algorithms. These algorithms convert the data into unreadable format, known as cipher text, Rivest, Shamir and Adleman (RSA) one of the most popularly used asymmetric algorithm. This paper gives detailed review about such different cryptographic algorithms used for the cloud data security. The comparison study is also made for the size of data and to analyze the encryption time and decryption time, which concludes that to enhance the cloud data security some addon techniques are to be used along with these cryptographic algorithms. To increase the security level and to increase the transmission speed of plaintext, integrated method will be proposed by encoding the plaintext to intermediate plaintext and then intermediate plaintext will be compressed using any one of the compression techniques to increase the compression ratio, lastly the compressed file is encrypted to further enhance the security level.
Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia
Authors: Alsmadi Mutasem K. , Jaradat Ghaith M. , Abahussain Sami A. , Tayfour Mohammed Fahed , Badawi Usama A. , Alfagham Hayat , Alshabanah Muneerah Ebrahem , Alrajhi Daniah Abdulrahman , ALkhaldi Hanouf Naif , Altuwaijri Njoud Ahmad , ShoShan Hany Answer , Abouelnaga Hayah Mohamed , Mohamed Metwally Ahmed Baz
Published: April 2023

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model.
Epidemiologia clínica da dor músculo-esquelética
Authors: Teixeira Manoel Jacobsen , Teixeira William Gemio Jacobsen , Santos Fábio Pires de Souza , Andrade Daniel Ciampi Araujo de , Bezerra Simone Lopes , Figueiró João Bertuol , Okada Massako
Published: April 2023

Vários estudos demonstram que as mulheres apresentam mais dor do que os homens. Entretanto, os estudos sobrelombalgia e dor torácica não são conclusivos. Em casos de dores articulares e fibromialgia existe aumento da prevalência com o progredir das idades em ambos os sexos, apesar de haver predominio nas mulheres. As síndromes dolorosas decorrentes das disfunções têmporo-mandibulares são mais comuns nas mulheres que nos homens e apresentam pico de ocorrência nas fases reprodutivas. A diferença na ocorrência da dor nos homens e nas mulheres pode ser devida a vários fatores incluindo os constitucionais, os hormonais, os cognitivos e os culturais, entre outros. O aumento da freqüência da dor com o avanço da idade, especialmente das dores articulares e da fibromialgia, sugere haver associação entre as afecções degenerativas ou acúmulo de casos de dor com o progredir da idade. A redução da freqüência de alguns casos de dor nos idosos pode ser devida à melhora das condições causais com o passar do tempo ou ao aumento da incapacidade ou da mortalidade que remove tais indivíduos das comunidades.
The Neurovasculome: Key Roles in Brain Health and Cognitive Impairment: A Scientific Statement From the American Heart Association/American Stroke Association
Authors: Iadecola Costantino , Smith Eric E. , Anrather Josef , Gu Chenghua , Mishra Anusha , Misra Sanjay , Perez-Pinzon Miguel A. , Shih Andy Y. , Sorond Farzaneh A. , van Veluw Susanne J. , Wellington Cheryl L. ,   on behalf of the American Heart Association Stroke Council; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiovascular Radiology and Intervention; Council on Hypertension; and Council on Lifestyle and Cardiometabolic Health
Published: April 2023

Background:

Preservation of brain health has emerged as a leading public health priority for the aging world population. Advances in neurovascular biology have revealed an intricate relationship among brain cells, meninges, and the hematic and lymphatic vasculature (the neurovasculome) that is highly relevant to the maintenance of cognitive function. In this scientific statement, a multidisciplinary team of experts examines these advances, assesses their relevance to brain health and disease, identifies knowledge gaps, and provides future directions.

Methods:

Authors with relevant expertise were selected in accordance with the American Heart Association conflict-of-interest management policy. They were assigned topics pertaining to their areas of expertise, reviewed the literature, and summarized the available data.

Results:

The neurovasculome, composed of extracranial, intracranial, and meningeal vessels, as well as lymphatics and associated cells, subserves critical homeostatic functions vital for brain health. These include delivering O
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and nutrients through blood flow and regulating immune trafficking, as well as clearing pathogenic proteins through perivascular spaces and dural lymphatics. Single-cell omics technologies have unveiled an unprecedented molecular heterogeneity in the cellular components of the neurovasculome and have identified novel reciprocal interactions with brain cells. The evidence suggests a previously unappreciated diversity of the pathogenic mechanisms by which disruption of the neurovasculome contributes to cognitive dysfunction in neurovascular and neurodegenerative diseases, providing new opportunities for the prevention, recognition, and treatment of these conditions.

Conclusions:

These advances shed new light on the symbiotic relationship between the brain and its vessels and promise to provide new diagnostic and therapeutic approaches for brain disorders associated with cognitive dysfunction.
Ecopsychological interactions of young children with other subjects of the social environment
Authors: Lidskaya Eleonora V. , Panov Victor I.
Published: April 2023

Background. Many studies carried out in the context of di\erent approaches have been devoted to the social interactions of young children with the environment. Meanwhile, numerous questions remain unexplored from an ecopsychological point of view. ]ese include how (and whether) the subjective structuring of the surrounding social environment occurs at this age and what types of communicative interactions are most characteristic of children of this age. Objective. Using the ecopsychological typology of subject-environment interactions, the work seeks to analyze social development of young children, to establish whether children of this age are capable of subjective structuring of social environment, as well as to identify on the example of communication and joint activities with other children and adults, what types of interactions are most characteristic of them. Methods. ]e study was carried out with a theoretical analysis of studies on social development of young children described in the works of L.N. Galiguzova (1983), M.S. Kiryushkina (2009), M.I. Lisina (1986), and E.O. Smirnova (2003). ]e ecopsychological typology of subject-environment interactions was applied as a theoretical construct. Results. Social environment is subjectively not homogeneous for young children, being structured and hierarchized by them. A third of children at this age pay no attention to their peers, revealing an object-object relation to them. Some children treat peers as things, demonstrating the subject-object type of interaction. Approximately a third of children observe their peers but do not come into contact with them, which indicates a subject-object attitude towards them. To overcome the fear of peers and evoke the need to interact with them, as well as to establish their interaction with each other, children need the help of an adult in organizing joint actions. ]ese actions require the implementation of subject-joint or subject-generating types of interaction. With the appearance of an adult, the child ^rst tries to attract attention to himself —subject-object and object-subject types of interaction; then he tries to include the adult in a joint action — the subject-generating and the subject-joint types of interaction. Conclusion. ]e presence of an adult is of the greatest importance in forming and satisfying the need for communicative interaction in children of this age. It is followed by toys and, ^nally, by the presence of another child. As the child’s need develops, the types of interactions with subjects of social environment change in the direction from the object-object and subject-object types towards the subject-generating and subject-joint ones. ]e adult’s assistance in shaping the child’s need for communication and joint actions is a necessary condition for such dynamics in types of interaction.
Neuropsychological diagnostics and rehabilitation of patients with disorders of praxis in diseases of the brain of various etiology
Authors: Baulina Maria E. , Varako Natalia A. , Zinchenko Yuri P. , Kovyazina Maria S. , Mikadze Yuri V. , Skvortsov Anatoliy A. , Fufaeva Ekaterina V.
Published: April 2023

Background. Praxis disorders are one of the most common consequences of brain damage. In this regard, neuropsychological diagnosis and rehabilitation of patients with praxis disorders is of great practical importance. Since people with apraxia and their families continue to live in society, they have to deal with issues of social functioning in the context of the patient's limited abilities. As social well-being is impossible without taking into account the psychological and behavioral aspects of the patient and his environment, the modern approach to diagnosis and rehabilitation includes working with all of these aspects. This work is implemented through the organization of a partnership between the patient, his environment and a multidisciplinary team of specialists. Objective. The article aims to analyze and summarize the diagnostic and rehabilitation experience accumulated in neuropsychology, as well as to identify quality criteria for neuropsychological diagnosis and rehabilitation of patients with praxis disorders arising from brain damage. These criteria are recommended for use in the practice of a clinical psychologist. Results. Based on the analysis of applied research and practical guidelines, recommendations on the choice of diagnostic tools for detecting praxis disorders are presented. The main directions and methods of rehabilitation work, which have shown their effectiveness in overcoming kinetic, kinesthetic, spatial and regulatory apraxia, are highlighted. Conclusion. The practical work of a clinical psychologist with patients suffering from praxis disorders should be aimed at maintaining the level of preserved voluntary movements and actions as a “support” for further restoration of motor functions or for maintaining the existing level of motor functioning. Practical work should seek to restore disturbed praxis with the help of internal / external means or special techniques and focus on the organization of the patient's household environment in order to minimize restrictions in functioning.