bluuebunny/crossref_metadata_2025 huggingface dataset:
The role of personality traits and social support in relations of health-related behaviours and depressive symptoms
Authors: Edler Johanna-Sophie , Manz Kristin , Rojas-Perilla Natalia , Baumeister Harald , Cohrdes Caroline
Published: January 2022

Background

Previous evidence has suggested that physically inactive individuals and extensive media users are at high risk for experiencing depressive symptoms. We examined personality traits and perceived social support as potential moderators of this association. Personality and perceived social support were included as two of the most frequently considered variables when determining predispositioning factors for media use phenomena also discussed in relation to physical activity.

Methods

We analysed cross-sectional data from 1402 adults (18–31 years old) who participated in a national health survey in Germany (KiGGS, Study on the health of children and adolescents in Germany, wave 2). The data included one-week accelerometer assessments as objective indicators of physical activity, self-reported media use, depressive symptoms, perceived social support and Big 5 personality traits. An elastic net regression model was fit with depressive symptoms as outcome. Ten-fold cross-validation was implemented.

Results

Amongst the main effects, we found that high media use was positively correlated with depressive symptoms, whereas physical activity was not correlated. Looking at support and individual differences as moderators, revealed that PC use was more strongly correlated with depressive symptoms in cases of low levels of perceived social support. Positive associations of social media use with depressive symptoms were more pronounced, whereas negative associations of moderate to vigorous physical activity with depressive symptoms were less pronounced in extraverts than they were in introverts.

Conclusions

Results highlight the importance of considering individual factors for deriving more valid recommendations on protective health behaviours.
Efficacy of IOTA simple rules, O-RADS, and CA125 to distinguish benign and malignant adnexal masses
Authors: Xie Wen ting , Wang Yao qin , Xiang Zhi sheng , Du Zhong shi , Huang Shi xin , Chen Yi jie , Tang Li na
Published: January 2022

Objective

Ovarian cancer is the most deadly deadliest gynecological tumor in the female reproductive system. Therefore, the present study sought to determine the diagnostic performance of International Ovarian Tumor Analysis Simple Rules (IOTA SR), the Ovarian-Adnexal Reporting and Data System (O-RADS), and Cancer Antigen 125 (CA125) in discriminating benign and malignant ovarian tumors. The study also assessed whether a combination of the two ultrasound categories systems and CA125 can improve the diagnostic performance.

Methods

A total of 453 patients diagnosed with ovarian tumors were retrospectively enrolled from Fujian Cancer Hospital between January 2017 and September 2020. The data collected from patients included age, maximum lesion diameter, location, histopathology, levels of CA125, and detailed ultrasound reports. Additionally, all ultrasound images were independently assessed by two ultrasound physicians with more than 5 years of experience in the field, according to the IOTA simple rules and O-RADS guidelines. Furthermore, the area under the curve (AUC), sensitivity, and specificity of the above mentioned predictors were calculated using the receiver operating characteristic curve.

Results

Out of the 453 patients, 184 had benign lesions, while 269 had malignant ovarian tumors. In addition, the AUCs of IOTA SR, O-RADS, and CA125 in the overall population were 0.831, 0.804, and 0.812, respectively, and the sensitivities of IOTA SR, O-RADS, and CA125 were 94.42, 94.42, and 80.30%, respectively. On the other hand, the AUCs of IOTA SR combined with CA125, O-RADS combined with CA125, and IOTA SR plus O-RADS combined with CA125 were 0.900, 0.891, and 0.909, respectively. The findings also showed that the AUCs of a combination of the three approaches were significantly higher than those of individual strategies (p<0.05) but not significantly higher than the AUC of a combination of two methods (p>0.05).

Conclusion

The findings showed that a combination of IOTA SR or O-RADS in combination with CA125 may improve the ability to distinguish benign from malignant ovarian tumors.
Estonian women’s national volleyball team’s success in 2019 European Championship final tournament
Authors: Stamm Raini , Õun Kevin , Stamm Meelis , Tuula-Fjodorov Reeda
Published: January 2022

The aim of the article was to analyse the game success and tactics of the Estonian team in subgroup C of the 2019 European Championship final tournament in sets won and lost.

The subjects were the players of the Estonian volleyball team in the final tournament of the European Championship in 2019. The paper analyses Estonia’s performance in five games of the European Championship: Hungary – Estonia, Croatia – Estonia, Romania – Estonia, the Netherlands – Estonia and Azerbaijan – Estonia. A total of 19 sets were analysed, four of which the Estonian national team won.

As a result, it was concluded that the Estonian volleyball team had higher efficiency indicators in all the four technical elements in the sets they won than in the sets they lost. Ball reception and serve efficiency differed less in the sets won and lost than attacking and blocking efficiency. Thus, it can be concluded that sets were lost mainly because of attack and block, not serve and reception.

The most efficient receiver (50%) in the Estonian women’s team was K. Nõlvak. The most efficient server (80%) was opposite spiker K. Moor. In attack, the most efficient player was outside hitter A. Ennok (28%). Estonia’s setter J. Mõnnakmäe was the most efficient in blocking (24%). L. Kullerkann also performed at a very good level among middle blockers and collected the greatest number of blocks in the tournament (57). K. Laak scored the greatest number of points in the championship (36 points).
Body composition of male office workers compared by means of infrared reflection measurement, bioimpedance analysis and calipermetry
Authors: Raschka Christoph , Kwon O-Sung , Koch Horst J.
Published: January 2022

There are currently many different anthropometric methods to determine individual body fat percentage, as well as almost as many variants of bioelectric impedance analysis (BIA) and only a single method of infrared reflection measurement (IR) as easily feasible methods of field research. The present study aims at a simultaneous comparison between calipermetry, IR and BIA. In particular, the question which measurement method could be used as an equivalent method in the event of failure of the BIA or IR is investigated. The sample group consisted of 250 male office workers from the Rhine-Main area (average age 31.75 ± 9.16 years, average height 178.5 ± 6.93 cm and average weight 80.61 ± 9.42 kg). The measurements were previously scheduled and mostly carried out during the lunch break. For anthropometry, 13 skin fat folds were measured with the help of the Accu® Measure Caliper, thigh circumference with the help of a measuring tape, height with a height measuring device and body mass on the weight scale. Subsequently, an IR measurement (Futrex®) and a BIA (InBody®) were performed on the same subject. The parameters of body fat percentage and total body water (in litres) were examined. The statistical methods were correlation coefficients, Bland-Altman comparison and paired t-test for equivalence. For the men studied, the highest correlation coefficients were in the comparisons between the formulas according to Parízkova & Buzkova 1 and Parízkova & Buzkova 3 (r = 0.93), as well as for Parízkova & Buzkova 3 and Parízkova & Buzkova 4 (r = 0.96). The correlation coefficient for the IR vs. BIA comparison was only r = 0.56. A key result of the present study was the finding that certain methods cannot be substituted in an equivalent way, in fact only some calipermetric regression equations.
СВЯЗЬ СТЕПЕНИ РИСКА НЕВРОТИЧЕСКИХ РАССТРОЙСТВ С ЛИЧНОСТНЫМИ ОСОБЕННОСТЯМИ
Authors: Khudalova Madina Z.
Published: January 2022

В связи с повышенной стрессовой нагрузкой в обществе происходит неуклон­ный рост частоты нервно-психических заболева­ний. Этим продиктована актуальность изучения психологических факторов пограничных состояний и пси­хогенных расстройств.
Цель: исследование возможной корреляционной связи между уровнем невротизации и психопатизации с индивидуально-личностными особенностями.
Выборку составили 41 человек, жителей республики Южная Осетия, в возрасте от 28 до 47 лет обоих полов.
Методы. Использовались следующие методики: опросник «Уровень невротизации и психопатизации» (И.Б. Ласко и Б.И. Тонконогий), пятифакторный личностный опросник (Р. МакКрае, П. Коста), методика определения типа личности (Олдхэма-Моррис). Использовались статистические методы обработки результатов эмпирического исследования в программе SPSS 22.0: описательная статистика, корреляционный анализ (ранговая корреляция p-Спирмена).
Результаты. Было обнаружено, что рост невротизации и психопатизации сопровождаются ростом импульсивности, снижением воодушевления, дружеского настроя. Чем ниже уровень невротизации и психопатизации, тем больше респондентам свойственны наблюдательность, спокойствие, беспристрастность, реалистичное отношение к действительности, высокая приспособляемость. Также низкий уровень невротизации и психопатизации связны с такими личностными особенностями, как лояльность, сердечность, преданность.
Заключение. Выявлено, что уровни невротизации и психопатизации связаны с некоторыми индивидуально-психологическими особенностями. Среди которых: эмоциональная устойчивость / неустойчивость, экстраверсия/интроверсия, импульсивность/самоконтроль.
Область применения результатов. Результаты исследования могут быть применены в образовательном процессе в вузах на специальности «Медицинская (клиническая) психология», а также в практической деятельности медицинского психолога.
A Novel Approach for Hydraulic Valve Experimental Assessment Under Cavitating Condition
Authors: Cavallo Domenico Mario , Chiavola Ornella , Frattini Edoardo , Palmieri Fulvio
Published: January 2022

The article presents a novel approach for the experimental characterization of hydraulic valves. The proposed methodology allows to capture the key layout of the valve, to study its characteristic flow rate and to visualize the flow that passes through it. In the wake of the experimental technique found in the literature, the novel approach introduces the use of an original test valve, briefly called “prismatic”. It represents an effective alternative to the so-called “Half-Cut Model” (HCM) proposed by Oshima and Ichikawa. The new test valve simplifies the experimental set-up and allows to visualize the whole internal flow field, providing full insight in both the inception and the spatial development of the cavitation. Moving from the HCM, the key features of the prismatic valve are preliminarily investigated and assessed by modelling, trough 3-D CFD simulations within OpenFOAM environment. Once the layout of prismatic valve is defined, the experimental assessment phase is carried out, highlighting its capability in research and development activities.
A Deep Learning Based Social Distance Analyzer with Person Detection and Tracking Using Region Based Convolutional Neural Networks for Novel Coronavirus
Authors: Prabakaran N. , Sai Kumar Suvarna Sree , Kiran P. Kranthi , Supriya Pabbidi
Published: January 2022

With its staggering spread, the continuous novel coronavirus Covid flare-up has caused a worldwide disaster. Populace weakness develops because of an absence of productive helpful prescriptions and a shortage of antibodies against the infection. Since there are no antibodies accessible as of now, social detachment is viewed as a sufficient insurance (standard) against the transmission of the illness. With the ascent in cases, the public authority has ordered a base actual division of 2 meters in all open spaces as a security measure. Utilizing PC vision on video reconnaissance, we made an AI device to forestall the spread of the (novel coronavirus). A social separating analyzer AI apparatus that utilizes video observing from CCTV cameras and robots to control social removing convention. The made AI device was introduced in broad daylight spaces and guaranteed the distance between gatherings of individuals. If the hole was excessively close, the red line showed up, demonstrating a higher danger of being influenced, trailed by the green and yellow light, showing a protected line, and the other, demonstrating a generally safe of being influenced.
An Enhanced Video Inpainting Technique with Grey Wolf Optimization for Object Removal Application
Authors: Rao B. Janardhana , Chakrapani Y. , Kumar S. Srinivas
Published: January 2022

Video inpainting is the most trending research topic from the last decade. Video inpainting is the process of restoring the damaged parts of the vintage video or the filling of the regions by removing the unwanted objects with sophisticated techniques. The video inpainting is achieved by dividing the video into frames and the motion of the moving objects in the frames are tracked by applying the motion tracking method. The existing inpainting method proposed by the Criminisi, neglected the local similarities in the images so it suffered from dropping effect in the priority computation. This paper proposed a new priority computation method by introducing gradient operation with the addition of curvature in the data term and local structure measurement function with structure tensor theory as an additional term. Later, the patch matching is achieved with the Sum of Absolute Difference (SAD) distance method. Further, the optimal patch is selected by applying the Grey Wolf Optimization (GWO) algorithm. The efficiency of the proposed video inpainting technique is evaluated with the performance metrics, viz., Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Edge Similarity (ESIM) executed in MATLAB. The PSNR and SSIM of the proposed method for Fontaine_chatelet video is improved by 18.9% and 4.19% than existing method. The proposed method is compared with other existing methods also and it outperformed the existing methods.
Satellite Image Based Spatio-Temporal Variation Assessment in Captive Limestone Mines for Long-Term Viability
Authors: Sudhakar C. Venkata , Reddy G. Umamaheswara
Published: January 2022

Land use and Land cover mapping are crucial applications for planning and disaster mitigation as well as prevention. Such mapping is possible via satellite imagery. Every pixel in the training set must be labelled in order to employ machine learning techniques combined with image analysis for such land cover mapping. This paper mainly concentrates on the mining sector for assessing sustainable land environmental management plans. Google Earth mapping is one of the most efficient, economical, and reliable methods for better understanding of spatial and temporal changes of limestone mining area with less human efforts. High-resolution Google Earth images are used in assessing the temporal changes of the Limestone mine area in Kadapa district from 2011 to 2020. Google Earth Pro open-source software was used to extract images of captive limestone mines, which were then clipped using a polygon tool to aid in the creation of a land-use region occupied by the active mining area. It was found that the active mining area variation rate is in the Dalmia Cement Limestone mine is 308.49% which is the highest compared to Bharathi Cement Limestone Mine mining area variation rate (240%), which is second, and the lowest mining area variation rate is found in the Niduzuvvi Limestone Block-1 area, which is 11.67%. Based on the overall impact of limestone mining in the study area, it is recommended that all stakeholders especially mine and cement plant owners; pay close attention to the area’s land environmental issues. The proposed technique is used for quality assessment in terms of prediction accuracy. The system so developed is observed to outperform existing systems implemented with the benchmarks counterparts.
Image Link Through Adaptive Encoding Data Base and Optimized GPU Algorithm for Real-time Image Processing of Artificial Intelligence
Authors: An Byoungman , Kim Youngseop
Published: January 2022

This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive ethernet technology. The proposed system provides a design and optimization algorithm of in-vehicle networking technologies related Ethernet Audio Video Bridge (AVB) technology. We present a design of in-vehicle network system as well as the optimization of AVB for automotive. A proposal of Reduced Latency of Machin to Machine (RLMM) plays a significant role in reducing the latency between devices. The approach of RLMM on realistic test cases indicated that there was a latency reduction about 30.41% It is expected that the optimized settings for the actual automotive network environment can greatly shorten the time period in the development and design process. The results achieved from the experiments on the latency present in each function are trustworthy since average values are obtained via repeated tests for several months. It would considerably benefit the industry because analyzing the delay between each function in a short period of time is tremendously significant. In addition, through the proposed real-time camera and video streaming via optimized settings of AVB system, it is expected that AI (Artificial Intelligence) algorithms in autonomous driving will be of great help in understanding and analyzing images in real time.
An Empirical Review of DDOS Attack Mitigation System on SDN Using Cloud Environment
Authors: S. Annie Christila , R Dr. Sivakumar
Published: January 2022

The separation of the control plane and the network data plane from the network defined by the software is making it easier for network management. As a result, SDN can be used in a variety of network settings, including network hiring networks. However, the construction of the novel network raises new security issues. Dedicated Shared Denial (DDoS) is easy to distribute and hard to protect on standard networks; on SDN networks, it can bypass the central controllers and bring the entire network down. Cloud computing has emerged as a modern and exciting computer space over the past decade, providing an affordable and awesome computer space. SDN technology combined with cloud computing highlights the challenges of cloud communication and enhances cloud adaptability, configuration, intelligence, and extreme density. Sensitive features of SDN, such as global network coverage, software-based traffic analysis, integrated network management, etc., greatly enhance the acquisition of DDoS cloud capabilities and scalability capabilities. In this study, a major effort was made to compare the various solutions currently available for programs to reduce DDoS attacks in cloud environments using SDN and provided performance tests such as detection rate, recovery time and False positive Rate.
Prediction Secondary Protein Structure from Images of Amino Acid by Using Harr - like Features in Support Vector Machine
Authors: Jabbar Nahla Ibraheem
Published: January 2022

In this study Haar features are extracted from images of sub-sequences of amino acid and classified by Support Vector Machine (SVM). We apply a novel approach of integration Haar-like features extraction from primary protein structure to predication three states of secondary protein structure. The sequences of primary protein are divided into different slides windows for representation images and then Haar-like feature have been extracted from these images to classify three-category of secondary protein structure helix (H), strand (E) and coil (C). The final prediction results were generated from SVM overall per residue accuracies are: - accuracy of helix(H) reached 83.93%, accuracy of Sheet(E) is 85.15% and accuracy of the Coil (C) is 81.0126 %. Images are scanned from amino acid sequences are specified by the selection window sizes, when the size of window is small the important information of predicting secondary structure relay outside the window. It has taken only local sequence information. When the size of window has been increased the performance has been deteriorated. Haar-like gives a perfect input data of SVM with a huge amount of data, also for improvement of support vector machine are used varying cost parameters.
Presenting a Protocol to Increase IOT-Based Security
Authors: Mohammadiounotikandi Ali
Published: January 2022

The Internet of Things (IoT) has expanded access to information technology by combining both digital and physical fields as an emerging technology. IoT will increasingly overshadow human life as it becomes more pervasive. IoT will be applied to important areas of the national economy, such as health care and medical care, and smart transportation. Hence, data security in IoT must be met with highlights such as distinguishing proof, unwavering quality, integration, and verifiable, and so on. Security within the field of IoT is more vital in terms of openness and reliance. Security is an critical issue for IoT applications and proceeds to confront major challenges. This think about pointed to supply a convention to extend IoT-based security. The method proposed in this paper is to show modern conveyance engineering to extend IoT security. This unused design is based on a blockchain and can be actualized as a dispersed design in all layers of IoT. The findings of recreations and usage in genuine IoT scenarios appear that the execution of the proposed security conventions can be proficient and viable within the productivity and adequacy of distinctive levels of security and IoT accessibility.
Implementation and Comparison of File Security Using AES, DES and RSA and Anomaly Detection in Videos Using Convolutional Auto Encoder
Authors: Alex Sini Anna , A Parkavi , Patil Rakshita N , Kanavalli Anita , Karki Nandish Mahadev
Published: January 2022

Security is the vital part which helps in trying to exchange huge amount of data in the form of valid data from source to destination. Nowadays technology is modifying very fast based on the view on secure information. Every individual who are interacting with social network wishes their communication to be confidential and secured from the access of the unauthenticated users over the internet. Symmetric and Asymmetric Cryptography Techniques play a vital role as the arms of security. Cryptography is the skill of properly modifying the readable plain text data into a not readable cipher form. There are many individual symmetric and asymmetric algorithms that are implemented to secure the files but have their own disadvantages. Proposed technique in this paper is to use a combination of symmetric and asymmetric encryption algorithms and compare the performance with the existing systems. The file is divided into n-parts depending on the size then each part of the file is encrypted with any of the AES, DES, and RSA algorithms. Results of this Hybrid technique provides a stronger security. The comparison is made on the basis of time taken by the proposed and existing systems in milliseconds to encrypt different numbers of bytes of text and image data.
Image Processing for Improvement of Facial Keypoints Detector
Authors: Kusnadi Adhi , . Leondy , Nathania Lianna , Pane Ivransa Zuhdi , Overbeek Marlinda Vasty , Prasetya Syarief Gerald
Published: January 2022

This paper discusses the improvement of facial detection algorithms using the DCT algorithm and image processing. Face key point is very needed in the face recognition system. Some important factors that have effects to detect its result are noise and illuminations. These two factors can be overcome by eliminating some DCT coefficients, both high and low. However, after handling the problem, most likely the image quality will become decrease, which will adversely influence the performance of the feature detector algorithm. Therefore, it is very important to test the performance of the feature detector algorithm on images that are implemented noise and illumination handling and how to improve the quality again. This research implemented Discrete Cosine Transform (DCT), by eliminating the high and low coefficient because there is noise and illumination. However, it is not known at what coefficient level is the most effective, so testing in this study was carried out. Four deblurring algorithms are tested in this research, Blind Deconvolution, Wiener Filter Deconvolution, Lucy-Richardson Deconvolution, and Regularization Deconvolution. And tested the CLAHE algorithm to overcome the effect of removing low coefficient DCT. The best coefficient value to be removed at the DCT frequency is 0.75 with the best SURF algorithm, without the use of other algorithms. Also, the highest F-score is produced by the SURF detector at removing DCT low frequency in combination with the CLAHE algorithm. With the most ideal coefficient of 0.25.
Interaction Analysis of WhatsApp Application Integration in M-Learning
Authors: Khan Raja Muhammad Ishtiaq , Kumar Tribhuwan
Published: January 2022

Interaction analysis (IA) describes and analyzes an instructor's verbal interaction with a student (s). The use of interaction analysis has aided in the monitoring of teacher verbal behavior. WhatsApp is also extensively used in language learning, and it is the most widely used program for this function. WhatsApp Interaction was investigated using Language Related Episodes (LRE) and Interaction Analysis to utilize WhatsApp. Eight pairs of pupils were formed from the sampled students. The 16 students were all enrolled in the public university's PYP section. The investigation took place for four weeks, for three days for the class, and the sampled students collaborated in pairs via WhatsApp. Pearson's correlation method was used to demonstrate the association between two or more groups without any modification. Findings indicated that there was a negative correlation between the time spent on WhatsApp and vocabulary scores.
Introducing Comprehensive Software Quality Model for Evaluating and Development E-Government Websites in Jordan Using Fuzzy Analytical Hierarchy Process
Authors: S Al- Tarawneh Enar , Al-Tarawneh Lana kh
Published: January 2022

E-government is a new principle, mainly employed to improve the processes of government to make it appear more efficient, responsive, and transparent. The processes reflect the way that both enterprises and citizens will ultimately interact with their government units as well as the way that government units effectively cooperate and communicate. Besides, the main goal of this contribution is to build a new quality model dedicated to evaluate E-government websites. In this work a comparative analysis has been done on existing software quality models. In this respect, a good number of quality models for evaluating e-government websites have been published. Each model is mainly intended to embody and represent the quality factors’ totality and issues which are relevant to a particular idea of websites quality. Besides, such quality models may ultimately be used to improve new websites, develop ways to measure the quality of already existing websites, or in a sense, guide the development of existing websites. Thus, desirable mechanisms ultimately exist to choose the right model of highest intrinsic quality as well as of greater relevance. The researcher, in this research, presents mechanisms which are mainly proposed for the comparative assessment of e-government websites quality models. The introduction of model has been further enhanced and intensified through expanding its general quality, and identifying metrics to ultimately measure their attributes. Finally, the Fuzzy Analytical Hierarchy Process (FAHP) has been applied to the model, just as a decision-making tool, in order to mainly execute an empirical study that aims at verifying the ultimate efficiency of such a model.
A Study on Women Empowerment of Privaie Banks at Tirunelveli
Authors: Pandala CR Narayanan , K.A Dr.Janardhanan
Published: January 2022

Women empowerment is giving a particular degree of delegate freedom and commitment in regards to making decisions concerning their specific progressive destinations. The essential inspiration driving this audit is to choose the effect of ladies strengthening philosophy which is completed by the association. Being an elucidating study, the review system was taken from data to discover the components. The expert used this assessment arrangement is to find the truth of respondent's points of view and decisions about ladies strengthening. The goal of the review is to realize the association effectively urges ladies to face challenges and open doors and Women strengthening assists organizations with growing better. Information were inspected by using the Chi-square method and ANOVA in SPSS programming. The review inspected the effects of various methods embraced by the administration for ladies strengthening. The scientist presumes that ladies' strengthening in the association is at a healthy level.
Detection COVID-19 of CT-Scan Image for Hospitalized Iraqi Patients based on Deep Learning
Authors: Ahmed Dalia Shihab , Allah Hanan Abed Alwally Abed , Hussain Samira Abdul-kader , Abbas Ishraq Khudhair
Published: January 2022

Due to the conditions in which countries experienced the outbreak of the Coronavirus and our problem in diagnosing the disease, some of them relied on swabs to know if a person was infected, and also their dependence on symptoms such as temperature, rapid heartbeat, pressure, coughing and other symptoms similar to the normal flu, but this method is failure sometimes, therefore it was the best way for early detection and diagnosis of cases of COVID- 19, as well as the accurate segregation of non-COVID-19 patients at cost and in the early stages of the disease, is a major difficulty in the current COVID-19 pandemic. Although widely used in diagnostic centres, radiation-based diagnostic techniques have drawbacks when it comes to disease newness. As a result, deep learning models are commonly used for X-ray interpretation by medical and computational researchers. Deep learning models can identify COVID-19, a critical task for treatment options based on diagnostic data these days. On the other hand, advances in artificial intelligence, machine learning, deep learning, and medical imaging methods enable outstanding performance, especially in detection, classification, and segmentation issues. These advances have allowed clinicians to more accurately monitor the human body, improving diagnosis and non-surgical patient examination. There are a variety of imaging methods that can be used to identify COVID-19, but we choose to use computerized tomography (CT) because it is the most commonly used. In addition, to detect COVID-19, we use a deep learning model based on a Convolutional Neural Network (CNN). Two samples of the tested data were used, where one of these data was collected from Al-Karkh Hospital in Baghdad, which consisted of 40 people, samples were taken according to their critical condition. The system was trained and tested on the basis of this dataset, where we used CNN three times, once to extract the feature and twice for the classification process. The results showed that the accuracy of the system reaches 100% because this system depends on the Bayes rule and it is not possible error.
Quality Management System Practices Performed in Engineering Educational Institutions: Analysis of Indian Universities
Authors: Kumar Parvesh , Singhal Sandeep , Kansal Jimmy
Published: January 2022

The purpose of this research is to examine the quality management system (QMS) practices in ISO 9001 certified engineering educational institutes (EEIs) in India. According to the literature, QMS in engineering education is primarily concerned with teaching, learning, examinations, student results, and infrastructure. In terms of organizational effectiveness, very few studies mentioned QMS. A QMS analysis was performed on forty-five EEIs located in the Delhi NCR region of India, and two groups of institutions were chosen based on the number of years they had been in operation. Data was collected from forty-five engineering educational institutions using a questionnaire-based instrument. The questionnaire was created with the help of QMS constructs (ten factors), institutions, and quality measures recommended by India's national board of accreditation-NBA. To validate the measuring instrument and determine the student t-test and p-value, the data was analysed using SPSS 26.0 software. The author discovered that group-b institutions received significantly higher scores in variables such as top management commitment, systemic management approach, customer satisfaction, employee engagement, training, teamwork, performance development, corporate social responsibility, academic culture, and knowledge audit when compared to group-a institutions. The study also revealed the institutional QMS's strong and weak points, highlighting the critical need to incorporate ISO 9001:2015-based QMS practises for institutional continuous improvement.
Predicting the Probability of Infected with Lung Failure in Covid-19 Patients Using a Logistic Regression Model
Authors: Jasim Sabah Hasan
Published: January 2022

Logistic regression has increased in social and medical sciences and in education research. A program SPSS version (22) was used to estimate the logistic regression model after entering the required information. Discussion of the results obtained through the use of the outputs the results to analysis and discussion. The model was applied, about 125 samples for Covid-19 patient was entered from people has Covid-19 disease, and all independent variables were entered to infer the adoption of lung infection and its relationship to the variables together. The aims of study were probability of infected with covid-19 disease regarding to use some variables and forecasting probability infected with lung failure in Covid-19 patients which lead to cytokine syndrome and death. Use the logistic regression in medical fields as a statistic analysis tools.
Enhancement of Cognitive Radio Spectrum with Proposed Cascade Filter Model
Authors: Ismaeel Nooruldeen Q. , Ibrahim Sarmad K. , Najah A. Hajer
Published: January 2022

The use of the frequency spectrum has become an urgent necessity, especially in the advanced generations of communications and various types of transmitted data. The radio spectrum is not being exploited effectively, primarily due to the current strict frequency allocation scheme. Only few bands of the spectrum are heavily utilized, such as those used for wireless base stations. Many bands are either not used at all or are only used infrequently. This problem has been solved through cognitive radio (CR) technology. CR technology faces many challenges in finding the unused frequency spectrum, especially the presence of noise in the spectrum analysis. This paper proposes a filter model with two-stages to enhance the cognitive radio performance and perform a comparison between proposed filter with the traditional cognitive radio techniques, by the means of QPSK modulation. The comparison result based on parameters such as the probability of detection as well as the probability of failure and the cumulative error rates. High probability of detection is required to achieve better network performance. A proposed filter model in the CR method has successfully passed all tests and provided better performance than other filters.
Improved Methods for Mammogram Breast Cancer Using by Denoising Filtering
Authors: Burhan Iman Mohammed , Abid Rahman Nahi , Jasim Mustafa Abdalkhudhur , Jaleel Refed Adnan
Published: January 2022

In diagnosing breast cancer, digital mammograms have shown their effectiveness as an appropriate and simple instrument in the early detection of tumor. Mammograms offer helpful cancer symptoms information, including microcalcifications and masses, which are not easy to distinguish because there are some flaws with the mammography images, including low contrast, high noise, fuzzy and blur. Additionally, there is a major problem with mammography because of a high density of the breast which conceals As a result of the mammographic image, it is more difficult to distinguish between the tissues with normal dense and the tissues that are cancerous. Therefore, mammography images need to be improved in order to accurately identify and diagnose breast cancer. The most typical goals of images enhancement are to remove noise and improve image details. With the aid of mammography image processing techniques, a special data including distinctive characteristics of tumors can be differentiated, this could help distinguish between malignant and benign cancers. This work focuses on removing noise of pepper & salt, improving image to increase the quality of mammography and enhance early detection of breast cancer. A specific approach is employed to do this, including of two phases of image denoising base filtration and one phase to improve contrast. The stages of filtering contain the using of wiener and median filters. The contrast enhancement stage utilizes (CLAHE) which is an abbreviation for contrast limited adaptive histogram equalization. Evaluating the performance is done via contrast histogram for the CLAHE and MSE & PSNF for the filters. The results demonstrate that the work technique is doing better when put to comparison with other approaches in term of low MSE (1.1645) and high PSNR (47.4750). The technique will be assessed with additional kinds of noise for future work.
Detection of Diseases in Rice Leaf Using Deep Learning and Machine Learning Techniques
Authors: Ibrahim Dr. Abdul-Wahab Sami , Atya Dr. Baidaa Abdul khaliq
Published: January 2022

Plant diseases have a negative impact on the agricultural sector. The diseases lower the productivity of the production yield and give huge losses to the farmers. For the betterment of agriculture, it is very essential to detect the diseases in the plants to protect the agricultural crop yield while it is also important to reduce the use of pesticides to improve the quality of the agricultural yield. Image processing and data mining algorithms together help analyze and detection of diseases. Using these techniques diseases detection can be done in rice leaves. In this research, the image processing technique is used to extract the feature from the leaf images. Further for the classification of diseases various machine learning algorithm like the random forest, J48 and support vector machine is used and the result is compared among different machine learning algorithm. After model evaluation, classification accuracy is verified using the n-fold cross-validation technique.
Critical Success Factors of Public-private Partnership (PPP) Projects in the Kingdom of Saudi Arabia
Authors: Jubair Saud Mohammed Bin , Singh Jugindar Singh Kartar
Published: January 2022

In a world where governments are looking at alternative ways of developing economies, infrastructure development remains a key aspect of these aspirations. For Middle East economies, which have relied on oil proceeds, the growth strategy is not as before as the oil prices have been fluctuating and resulting in low fiscal incomes. Saudi Arabia is one such economy. Thus, in the Kingdom's development strategy outlined in Vision 2030, there has been an emphasis on private players coming into partnering with the government in infrastructure and broader economic development projects. While the concept is easy on paper, running successful Public-Private Partnerships (PPP) projects is a complex endeavor given the conflicting interests of the partners themselves. This research investigated the critical success factors of PPP projects in Saudi Arabia based on the reflections of a quantitative study for infrastructure projects. For descriptive analysis, the entire population of participants (1200 respondents was used), and for the inferential statistics, a sample of 543 respondents was used in a Structured Equation Model tested on Smart PLS was used. The model tested critical success factors mediated by Openness and Communication and a multigroup analysis moderated by job level (representing the level of decision-making classified into strategic formulators - strategic level and above; and strategic implementers- operational managers). The models had an R2 of a stratified sample of 54.5% for the strategic level model and 45.9% for the strategic implementers model. Both models confirmed the importance of Procurement transparency, Risk sharing and allocation, Knowledge management. However, they had differences in the influence of governance, Openness and Communication and efficiency of the financial model. Governance had a significant influence on the success of PPPs only at the operational level while having a fully mediated relationship with Openness and Communication at the operational level. The study then produced two frameworks that are relevant for policymakers and potential private players to understand the relevant factors to be prioritised at each decision-making level to ensure the successful completion of PPPs in Saudi Arabia.
Gender Identification from Speech Recognition Using Machine Learning Techniques and Convolutional Neural Networks
Authors: Jaleel Hanan Qassim , Stephan Jane Jaleel , Naji Sinan A.
Published: January 2022

Gender identification represents a fundamental component of speech recognition and automatic interacting sound responding systems. Identifying the voice gender minimizes the computational loads of these systems for additional processing. Standard approaches for gender estimation from the speech have broadly relied on the extraction of speech features and classification tasks. This paper proposes a technique for gender identification of speech samples using the speech recognition process. The proposed technique extracts essential voice features like Mean, Zero-Crossing, Standard Deviation, and Amplitude, as well as 12 most significant features from every voice sample, and combines them to create voice feature vectors. The proposed technique uses several machine and deep learning methods such as Random Forest, KNN, Logistic Regression, Decision Tree, and CNNs, in order to classify the voice vectors into Male and Female classes. After comparing the evaluation metrics results of all classifiers, the proposed technique finds out that the CNN model is the best classifier used to classify the voice vectors with a higher precision value of 1.0.
Effect of Intellectual Capital on Organizational Performance Moderated by Cultural
Authors: Ningrum Endah Prawesti , Arsyah Regina Jansen
Published: January 2022

This study analyzes Organizational Performance which is the influenced by intellectual Capital moderated by culture. The method used in this research is by surveying and distributing questionnaires based on the time horizon using a cross section study. The population manufacturing companies in MM2100 area using convenience sampling. Regression analysis and inferential statistics, descriptive statistics is used by data analysis technique. The intellectual shows positive and significant impact on organizational performance and cultural as a moderating effect in this results. This research imply that by increasing intellectual and cultural Capital will be more effective on improving organizational performance.
Inverted Classroom in Self-regulated Learning during the Covid-19 Pandemic in University Students
Authors: Calle Juan Carlos Chumacero , Loayza Marya Graciela Barzola , Carrión Christian Esteban Gómez , Alviar Ruth Asela Saravia , Rivera-Zamudio July
Published: January 2022

The year 2020, the Peruvian authorities decreed the development of virtual learning in universities, due to the appearance of the pandemic that originated the COVID.19, in order to avoid the increase of contagions, it is this context that the higher houses of study should, implemented strategies to improve the performance academice of the students, being one of them the use of the inverted classroom, the objective of this study was to demonstrate that the use of the Inverted Classroom significantly influences the self-regulated learning during the pandemic of the COVID-19. The method used was quantitative, basic, with a quasi-experimental design, applying a program to second cycle students of a public university. From the statistical processing it was possible to conclude that the inverted classroom significantly influences self-regulated learning.
Non-functional Requirements Elicitation in Agile Base Models
Authors: Kumar Devendra , Kumar Anil , Singh Laxman
Published: January 2022

The elicitation of non-functional and functional needs is one of the most critical jobs of a requirement engineer. This scenario involves the imposition of limits on non-functional needs, whereas functional requirements call for the operation of a system in order to carry out functionality. Over the last few years, agile software development approaches have gained widespread acceptance in the software industry as a problem-solving paradigm. Non-functional requirements (NFRs) are frequently cited as a point of contention in non-functional requirements (NFR) approaches. As well as functional requirements like speed and efficiency, security is desired, amongst a host of other things. Aspects like usability, security, and privacy must all be taken into account. Functional needs must be treated as though they were first-class under the current industry standard of practice. Functional requirements are distinguished from non-functional requirements by the fact that only implemented requirements can be evaluated. To give an example, this method attracts the attention of the system's end users to a critical defect in its architecture. Projects of this type frequently fail because to dissatisfaction among the target audience. If you'd like a great demonstration, consider the London Ambulance System. When dealing with non-compliance to the necessary degree of detail, it is feasible to raise the likelihood of software success this is the first study of its kind in its sector to bring attention to the most critical NFR issues. The problems that arise during the elicitation stage of requirement engineering in agile base models. It also outlines the techniques and strategies that are being considered. Proposed in the literature as a means of dealing with these problems.