Search results for: least square support vector machine

Authors: J. Kloypayan, W. Pimpakan

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The pipe taper thread measurement and uncertainty normally used the four-wire probe according to the JIS B 0262. Besides, according to the EA-10/10 standard, the pipe thread could be measured using the three-wire probe. This research proposed to use the three-wire probe measuring the pitch diameter of the pipe taper thread. The measuring accessory component was designed and made, then, assembled to one side of the ULM 828 CiM machine. Therefore, this machine could be used to measure and calibrate both the pipe thread and the pipe taper thread. The equations and the expanded uncertainty for pitch diameter measurement were formulated. After the experiment, the results showed that the pipe taper thread had the pitch diameter equal to 19.165 mm and the expanded uncertainty equal to 1.88µm. Then, the experiment results were compared to the results from the National Institute of Metrology Thailand. The equivalence ratio from the comparison showed that both results were related. Thus, the proposed method of using the three-wire probe measured the pitch diameter of the pipe taper thread was acceptable.

Keywords: pipe taper thread, three-wire probe, measure and calibration, the universal length measuring machine

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Authors: Muhammad Sohail Sajid, Muhammad Abdullah Malik, Muhammad Saqib, Faiz Ahmad Raza, Waseem Akram

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Aedes (Ae.) aegypti, the vector of pathogens of one health significance, has gained currency over the last decade. The present study reports the prevalence of A. aegypti larvae in indoor and outdoor niches from the three districts of different agro-geo-climatic zones of Punjab, including Chakwal (north), Faisalabad (central), and Dera Ghazi Khan (south). Mosquito larvae were collected, preserved, and transferred for identification. The relevant data were collected on a predesigned questionnaire. Stegomyia indices, including House Index (HI), Breteau Index (BI), and Container Index (CI), were calculated. The association of different breeding containers with the prevalence of Ae. aegypti larvae were estimated through Chi-square analysis. The highest Stegomyia indices were calculated in Chakwal (HI = 46.61%, BI = 91.67%, and CI = 15.28%) as compared to Faisalabad (HI = 34.11%, BI = 68.75% and, CI = 13.04%) and DG Khan (HI = 28.39%, BI = 68.23% and, CI = 11.29%), respectively. Irrespective of the geographical area, earthen jars, water tanks, and tree holes were found to be significantly associated (p < 0.05) with the abundance of Ae. aegypti larvae. However, tires and plastic bottles in Faisalabad and DG Khan while flower tubs and plastic buckets in Faisalabad and Chakwal were found to be significantly associated (p < 0.05) with the larval abundance. The results are a maiden attempt to correlate the magnitude of Ae. aegypti larvae in various microclimatic niches of Punjab, Pakistan, which might help in policy-making for preventive management of the menace.

Keywords: Aedes aegypti, ecology, breeding habitats, Stegomyia indices, breeding containers

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Authors: Sunghae Jun

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Technology management (TM) is important issue in a company improving the competitiveness. Among many activities of TM, technology analysis (TA) is important factor, because most decisions for management of technology are decided by the results of TA. TA is to analyze the developed results of target technology using statistics or Delphi. TA based on Delphi is depended on the experts’ domain knowledge, in comparison, TA by statistics and machine learning algorithms use objective data such as patent or paper instead of the experts’ knowledge. Many quantitative TA methods based on statistics and machine learning have been studied, and these have been used for technology forecasting, technological innovation, and management of technology. They applied diverse computing tools and many analytical methods case by case. It is not easy to select the suitable software and statistical method for given TA work. So, in this paper, we propose a methodology for quantitative TA using statistical computing software called R and data science to construct a general framework of TA. From the result of case study, we also show how our methodology is applied to real field. This research contributes to R&D planning and technology valuation in TM areas.

Keywords: technology management, R system, R data science, statistics, machine learning

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Authors: Jian Qu, Akira Shimazu

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OOV (Out Of Vocabulary) terms are terms that cannot be found in many dictionaries. Although it is possible to translate such OOV terms, the translations do not provide any real information for a user. We present an OOV term definition extraction method by using information available from the Internet. We use features such as occurrence of the synonyms and location distances. We apply machine learning method to find the correct definitions for OOV terms. We tested our method on both biomedical type and name type OOV terms, our work outperforms existing work with an accuracy of 86.5%.

Keywords: information retrieval, definition retrieval, OOV (out of vocabulary), biomedical information retrieval

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Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Soran Tarkhani

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A glance at social media shows that Russia's invasion of Ukraine receives considerable support among Arabs. This significant support for the Russian invasion of Ukraine is puzzling since most Arab leaders openly condemned the Russian invasion through the UN ES‑11/4 Resolution, and Arabs are among the first who experienced the devastating consequences of war firsthand. This article tries to answer this question by using multiple regression to analyze the online content of Arab responses to Russia's invasion of Ukraine on seven major news networks: CNN Arabic, BBC Arabic, Sky News Arabic, France24 Arabic, DW, Aljazeera, and Al-Arabiya. The article argues that the underlying reason for this Arab support is a reaction to the common anti-Western sentiments among Arabs. The empirical result from regression analysis supports the central arguments and uncovers the motivations behind the endorsement of the Russian invasion of Ukraine and the opposing Ukraine by many Arabs.

Keywords: Ukraine, Russia, Arabs, Ukrainians, Russians, Putin, invasion, Europe, war

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Authors: Muhammad Emad Feteha

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Cairo has been famous for the unique identity of its medieval architecture, which was formed by multiple dynasties that ruled Egypt. However, only a few researches were done on the identity of its public space. This paper links both the architectural and the socio-political aspects of the Cairene public space and studies how they affected each other. The subject of the study is Maydan Salah al-Din, the main city square of medieval Cairo, which reveals a quite useful information, not only about the architectural identity of the Cairene public space but also about the socio-political patterns that operated within. The analytical framework is based on Lefebvre’s theory, the ‘production of space’, in which he applied 'the Hegelian dialectic' in order to understand how the social practice forms the space, and how, in turn, the space forms the social practice. This framework offers a comprehensive understanding of the identity of the Cairene public space, which does not separate architecture from the social practice.

Keywords: architectural identity, Cairene public space, Islamic architectural history, production of space

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Authors: Fahad Qureshi

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This paper provides detailed study on the diagnosis of intermittent high vibration spikes at exhaust bearing (Non-Drive End) of Mitsubishi H-25 gas turbine installed in a petrochemical plant in Pakistan. The diagnosis is followed by successful root cause analysis of the issue and recommendations for improving the reliability of machine. Engro Polymer and Chemicals (EPCL), a Chlor Vinyl complex, has a captive power plant consisting of one combined cycle power plant (CCPP), having two gas turbines each having 25 MW capacity (make: Hitachi) and one extraction condensing steam turbine having 15 MW capacity (make: HTC). Besides, one 6.75 MW SGT-200 1S gas turbine (make: Alstom) is also available. In 2018, the organization faced an issue of intermittent high vibration at exhaust bearing of one of H-25 units having tag GT-2101 A, which eventually led to tripping of machine at configured securities. Since the machine had surpassed 64,000 running hours and major inspection was also due, so bearings inspection was performed. Inspection revealed excessive coke deposition at labyrinth where evidence of rotor rub was also present. Bearing clearance was also at upper limit, and slight babbitt (soft metal) chip off was observed at one of its pads so it was preventively replaced. The unit was restated successfully and exhibited no abnormality until October 2020, when these spikes reoccurred, leading to machine trip. Recurrence of the issue within two years indicated that root cause was not properly addressed, so this paper furthers the discussion on in-depth analysis of findings and establishes successful root cause analysis, which captured significant learnings both in terms of machine design deficiencies and gaps in operation & maintenance (O & M) regime. Lastly, revised O& M regime along with set of recommendations are proposed to avoid recurrence.

Keywords: exhaust side bearing, Gas turbine, rubbing, vibration

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Authors: Musarrat Jabeen

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Thoughtful intelligence offers a sustainable position to enhance the influence of decision-makers. Thoughtful Intelligence implies the understanding to realize the impact of one’s thoughts, words and actions on the survival, dignity and development of the individuals, groups and nations. Thoughtful intelligence has received minimal consideration in the area of Decision Support Systems, with an end goal to evaluate the quantity of knowledge and its viability. This pattern degraded the imbibed contribution of thoughtful intelligence required for sustainable decision making. Given the concern, this paper concentrates on the question: How to present a model of Thoughtful Decision Support System (TDSS)? The aim of this paper is to appreciate the concepts of thoughtful intelligence and insinuate a Decision Support System based on thoughtful intelligence. Thoughtful intelligence includes three dynamic competencies: i) Realization about long term impacts of decisions that are made in a specific time and space, ii) A great sense of taking actions, iii) Intense interconnectivity with people and nature and; seven associate competencies, of Righteousness, Purposefulness, Understanding, Contemplation, Sincerity, Mindfulness, and Nurturing. The study utilizes two methods: Focused group discussion to count prevailing Decision Support Systems; 70% results of focus group discussions found six decision support systems and the positive inexistence of thoughtful intelligence among decision support systems regarding sustainable decision making. Delphi focused on defining thoughtful intelligence to model (TDSS). 65% results helped to conceptualize (definition and description) of thoughtful intelligence. TDSS is offered here as an addition in the decision making literature. The clients are top leaders.

Keywords: thoughtful intelligence, sustainable decision making, thoughtful decision support system

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Authors: Lakhoua Najeh

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Aims of the work: The aim of this paper is to improve the reliability and the availability of a Packer production line of cigarettes based on two methods: The SADT method (Structured Analysis Design Technique) and the FMECA approach (Failure Mode Effects and Critically Analysis). The first method enables us to describe the functionality of the Packer production line of cigarettes and the second method enables us to establish an FMECA analysis. Methods: The methodology adopted in order to contribute to the improvement of the reliability and the availability of a Packer production line of cigarettes has been proposed in this paper, and it is based on the use of Structured Analysis Design Technique (SADT) and Failure mode, effects, and criticality analysis (FMECA) methods. This methodology consists of using a diagnosis of the existing of all of the equipment of a production line of a factory in order to determine the most critical machine. In fact, we use, on the one hand, a functional analysis based on the SADT method of the production line and on the other hand, a diagnosis and classification of mechanical and electrical failures of the line production by their criticality analysis based on the FMECA approach. Results: Based on the methodology adopted in this paper, the results are the creation and the launch of a preventive maintenance plan. They contain the different elements of a Packer production line of cigarettes; the list of the intervention preventive activities and their period of realization. Conclusion: The diagnosis of the existing state helped us to found that the machine of cigarettes used in the Packer production line of cigarettes is the most critical machine in the factory. Then this enables us in the one hand, to describe the functionality of the production line of cigarettes by SADT method and on the other hand, to study the FMECA machine in order to improve the availability and the performance of this machine.

Keywords: production system, diagnosis, SADT method, FMECA method

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Authors: Lagnajeet Mohanty, Mohnish Mishra, Pratham Tapdiya, Himanshu Sekhar Nayak, Swetapadma Singh

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In the era of global interconnectedness, effective risk assessment and management are critical for organizational resilience. This review explores the integration of machine learning (ML) into risk processes, examining its transformative potential and the challenges it presents. The literature reveals ML's success in sectors like consumer credit, demonstrating enhanced predictive accuracy, adaptability, and potential cost savings. However, ethical considerations, interpretability issues, and the demand for skilled practitioners pose limitations. Looking forward, the study identifies future research scopes, including refining ethical frameworks, advancing interpretability techniques, and fostering interdisciplinary collaborations. The synthesis of limitations and future directions highlights the dynamic landscape of ML in risk management, urging stakeholders to navigate challenges innovatively. This abstract encapsulates the evolving discourse on ML's role in shaping proactive and effective risk management strategies in our interconnected and unpredictable global landscape.

Keywords: machine learning, risk assessment, ethical considerations, financial inclusion

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Authors: Kholod Khairy Salama, Shabban Hassan Thabet

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In the present study, a comprehensive investigation has been undertaken into an Egyptian mural painting with feet wear slippers approach to choose the most successful restoration methods. The mural painting under examination dates back to the Amarna period; it was detached from a wall of an unknown tomb in Egypt, and currently, it is initially displayed in a showcase at the Egyptian Museum – Tahrir Square – Cairo, Egypt. The main objectives of this research were to (a) reveal the pigment used in the mural painting, (b) reveal the medium used with colours, (c) determine the technique of manufacturing, (e) determine the ground support, and (f) reveal the main deterioration aspects. The analytical techniques used for investigation were Optical Microscopy, Raman, X-ray Florescence, X-ray diffraction, and Fourier transform infrared coupled with attenuated total reflectance “FTIR-ATR”. The investigation revealed that the vital deterioration factors affecting the object. This research aims to examine and analyze the mural painting to choose the suitable method for the restoration process (a) define the colours through comparative analysis to choose the suitable material for cleaning, (b) define the natural structure of the ground support layer, which appeared as mud layer (c) determine the medium used with colours (d) diagnosis the presence of the white wash layer, and (e) choose the suitable restoration methods according to the results. Conclusion: This study focused mainly on the physical and chemical properties of the mural painting compound and the main changes that happened to the mural painting material, which caused deterioration and fall down of the painting parts, so we can find the best and optimum restoration ways for this object.

Keywords: mural paintings, Tal Al-Amarna, digital microscope, Raman, XRF, XRD, FTIR

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Authors: Matheus Nunes, Rafael Mendes, Taygoara Felamingo Oliveira, Antonio Brasil Junior

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Wind tunnel experiments of horizontal axis propeller hydrokinetic turbines model were carried out, in order to determine the performance behavior for different configurations and operational range. The present experiments introduce the use of two different geometries of rear diffusers to enhance the performance of the free flow machine. The present paper reports an increase of the power coefficient about 50%-80%. It represents an important feature that has to be taken into account in the design of this kind of machine.

Keywords: diffuser-enhanced turbines, hydrokinetic turbine, wind tunnel experiments, micro hydro

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Authors: Mahesh S. Harne, Sunil V. Deshmukh

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A continuous passive motion machine improves and helps the patient to restore range of motion in various physiotherapy activities. The paper presents a concept for portable CPM. The device is used for various joint for upper and lower body extremities. The device is designed so that the active and passive motion is incorporated. During development, the physiotherapist and patient need is integrated with designer aspects. Various tools such as Analytical Higher Hierarchy process (AHP) and Quality Function Deployment (QFD) is used to integrate the need at the design stage. With market survey of various commercial CPM the gaps are identified, and efforts are made to fill the gaps with ergonomic need. Indian anthropomorphic dimension is referred. The device is modular to best suit for all the anthropomorphic need of different human. Experimentation is carried under the observation of physiotherapist and doctor on volunteer patient. We reported better results are compare to conventional CPM with comfort and less pain. We concluded that the concept will be helpful to reduces therapy cost and wide utility of device for various joint and physiotherapy exercise.

Keywords: continuous passive motion machine, ergonomics, physiotherapy, quality function deployment

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Authors: Chi-Tai Shen

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This study aims to examine whether perceived organizational support moderates the relationship between person–former organization fit and person–organization fit after the mediating effect of feelings of violation. A two-stage data collection method was used. Based on our research requirements, we only approached participants who were involuntary turnover from their former organizations and looking for a new job. Our final usable sample was comprised of a total of 264 participants from Taiwan. We followed Muller, Judd, and Yzerbyt, and Preacher, Rucker, and Hayes’s suggestions to test our moderated mediation model. This study found that employee perceived organizational support moderated the indirect effect of person–former organization fit on person–organization fit (through feelings of violation). Our study ends with a discussion of the main research findings and their limitations and presents suggestions regarding the direction of future studies and the empirical implications of the results.

Keywords: person–organization fit, feelings of violation, organizational support, moderated mediation

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Authors: Thomas Abraham, Garcia de la Vega Felipe, Lubna Nishath, Nzekwe Nduka, Powell Anne-Marie

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Introduction: The purpose of this paper is to discuss the effectiveness of a peer support network created to provide medical education, pastoral support, and reliable resources to registrars to help them pass the MRCGP exams. This paper will include a description of the network and its purpose, discuss how it has been used by trainees since its creation, and explain how this methodology can be applied to other areas of medical education and primary care. Background: The peer support network was created in February 2021, using Facebook, Telegram, and WhatsApp platforms to facilitate discussion of cases and answer queries about the exams, share resources, and offer peer support from qualified GPs and specialists. The network was created and is maintained by the authors of this paper and is open to anyone who is registered with the General Medical Council (GMC) and is studying for the MRCGP exams. Purpose: The purpose of the network is to provide medical education, pastoral support, and reliable resources to registrars to help them pass the exams. The network is free to use and is designed to take the onus away from a single medical educator and collate a vast amount of information from multiple medical educators/trainers; thereby creating a digital library of information for all trainees - exam related or otherwise. Methodology The network is managed by a team of moderators who respond to queries and facilitate discussion. Smaller study groups are created from the main group and provide a platform for trainees to work together, share resources, and provide peer support. The network has had thousands of trainees using it since February 2021, with positive feedback from all trainees. Results: The feedback from trainees has been overwhelmingly positive. Word of mouth has spread rapidly, growing the groups exponentially. Trainees add colleagues to the groups and often stay after they pass their exams to 'give back' to their fellow trainees. To date, thousands of trainees have passed the MRCGP exams using the resources and support provided by the network. Conclusion The success of this peer support network demonstrates the effectiveness of creating a network of thousands of doctors to provide medical education and support.

Keywords: peer support, medical education, pastoral support, MRCGP exams

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Authors: Abdelghani Chahmi

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This article examines the detection and localization of faults in electrical systems, particularly those using asynchronous machines. First, the process of failure will be characterized, relevant symptoms will be defined and based on those processes and symptoms, a model of those malfunctions will be obtained. Second, the development of the diagnosis of the machine will be shown. As studies of malfunctions in electrical systems could only rely on a small amount of experimental data, it has been essential to provide ourselves with simulation tools which allowed us to characterize the faulty behavior. Fault detection uses signal processing techniques in known operating phases.

Keywords: induction motor, modeling, bearing damage, airgap eccentricity, torque variation

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Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Abdeghani Qadem

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Climate change is now an undeniable reality with increasing impacts on water systems worldwide, especially leading to severe drought episodes. The Southern Mediterranean region is particularly affected by this drought, which can have devastating consequences on water resources. Morocco, due to its geographical location in North Africa and the Southern Mediterranean, is especially vulnerable to these effects of climate change, particularly drought. In this context, this article focuses on the study of climate variability and drought characteristics in the Saiss Plateau region and its adjacent areas with the Middle Atlas, using specific statistical indices. The study begins by analyzing the annual precipitation variation, with a particular emphasis on data homogenization and gap filling using a regional vector. Then, the analysis delves into drought episodes in the region, using the Standardized Precipitation Index (SPI) over a 12-month period. The central objective is to accurately assess significant drought changes between 1980 and 2015, based on data collected from nine meteorological stations located in the study area.

Keywords: climate variability, regional vector, drought, standardized precipitation index, Saiss Plateau, middle atlas

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Authors: Joseph Salim

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This paper proposes the development of a supervised machine learning system to predict the consequences of untreated bruxism (teeth grinding) on gingival (gum) recession and jaw pain (most often bilateral jaw pain with possible headaches and limited ability to open the mouth). As a general dentist in a multi-specialty practice, the author has encountered many patients suffering from these issues due to uncontrolled bruxism (teeth grinding) at night. The most effective treatment for managing this problem involves wearing a nightguard during sleep and receiving therapeutic Botox injections to relax the muscles (the masseter muscle) responsible for grinding. However, some patients choose to postpone these treatments, leading to potentially irreversible and costlier consequences in the future. The proposed machine learning model aims to track patients who forgo the recommended treatments and assess the percentage of individuals who will experience worsening jaw pain, gingival (gum) recession, or both within a 3-to-5-year timeframe. By accurately predicting these outcomes, the model seeks to motivate patients to address the root cause proactively, ultimately saving time and pain while improving quality of life and avoiding much costlier treatments such as full-mouth rehabilitation to help recover the loss of vertical dimension of occlusion due to shortened clinical crowns because of bruxism, gingival grafts, etc.

Keywords: artificial intelligence, machine learning, predictive insights, bruxism, teeth grinding, therapeutic botox, nightguard, gingival recession, gum recession, jaw pain

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Authors: Kenneth Fon Mbah, Arash Habibi Lashkari, Ali A. Ghorbani

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Phishing e-mails are a security issue that not only annoys online users, but has also resulted in significant financial losses for businesses. Phishing advertisements and pornographic e-mails are difficult to detect as attackers have been becoming increasingly intelligent and professional. Attackers track users and adjust their attacks based on users’ attractions and hot topics that can be extracted from community news and journals. This research focuses on deceptive Phishing attacks and their variants such as attacks through advertisements and pornographic e-mails. We propose a framework called Phishing Alerting System (PHAS) to accurately classify e-mails as Phishing, advertisements or as pornographic. PHAS has the ability to detect and alert users for all types of deceptive e-mails to help users in decision making. A well-known email dataset has been used for these experiments and based on previously extracted features, 93.11% detection accuracy is obtainable by using J48 and KNN machine learning techniques. Our proposed framework achieved approximately the same accuracy as the benchmark while using this dataset.

Keywords: phishing e-mail, phishing detection, anti phishing, alarm system, machine learning

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Authors: John M. Ikome

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In the process of manufacturing, a machine breakdown usually forces a modified flow shop out of the prescribed state, this strategy reschedules part of the initial schedule to match up with the pre-schedule at some point with the objective to create a schedule that is reliable with the other production planning decisions like material flow, production and suppliers by utilizing a critical decision-making concept. We propose a rescheduling strategy and a match-up point that will have a determination procedure through an advanced feedback control mechanism to increase both the schedule quality and stability. These approaches are compared with alternative re-scheduling methods under different experimental settings.

Keywords: scheduling, heuristics, branch, integrated

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Authors: Apolinar Picado, Ronald Solís, Rafael Gamero

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The thin-layer drying characteristics of instant coffee solution were investigated in a laboratory tunnel dryer. Drying experiments were carried out at three temperatures (80, 100 and 120 °C) and an air velocity of 1.2 m/s. Drying experimental data obtained are fitted to six (6) thin-layer drying models using the non-linear least squares regression analysis. The acceptability of the thin-layer drying model has been based on a value of the correlation coefficient that should be close to one, and low values for root mean square error (RMSE) and chi-square (x²). According to this evaluation, the most suitable model for describing drying process of thin-layer instant coffee solution is the Page model. Further, the effective moisture diffusivity and the activation energy were computed employing the drying experimental data. The effective moisture diffusivity values varied from 1.6133 × 10⁻⁹ to 1.6224 × 10⁻⁹ m²/s over the temperature range studied and the activation energy was estimated to be 162.62 J/mol.

Keywords: activation energy, diffusivity, instant coffee, thin-layer models

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Authors: Ravinder Bahl, Jamini Sharma

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The paper demonstrates analysis of the cervical cancer based on a probabilistic model. It involves technique for classification and prediction by recognizing typical and diagnostically most important test features relating to cervical cancer. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases. The combination of the conventional statistical and machine learning tools is applied for the analysis. Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Keywords: cervical cancer, recurrence, no recurrence, probabilistic, classification, prediction, machine learning

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Authors: Dhyaalddin Bahaalddin Noori Zangana

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This study involves the behavior of reinforced sand under a square footing. A series of bearing capacity tests were performed on a small-scale laboratory model, which filled with a poorly-graded homogenous bed of sand, which was placed in a medium dense state using sand raining technique. The sand was reinforced with 40 mm wide household aluminum foil strips. The main studied parameters was to consider the effect of reinforcing strip length, with various linear density of reinforcement, number of reinforcement layers and depth of top layer of reinforcement below the footing, on load-settlement behavior, bearing capacity ratio and settlement reduction factor. The relation of load-settlement generally showed similar trend in all the tests. Failure was defined as settlement equal to 10% of the footing width. The recommended optimum reinforcing strip length, linear density of reinforcement, number of reinforcement layers and depth of top layer of reinforcing strips that give the maximum bearing capacity improvement and minimum settlement reduction factor were presented and discussed. Different bearing capacity ration versus length of the reinforcing strips and settlement reduction factor versus length of the reinforcing strips relations at failure were showed improvement of bearing capacity ratio by a factor of 3.82 and reduction of settlement reduction factor by a factor of 0.813. The optimum length of reinforcement was found to be 7.5 times the footing width.

Keywords: square footing, relative density, linear density of reinforcement, bearing capacity ratio, load-settlement behaviour

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Authors: Toshiki Ito, Takahiro Yamane, Yuki Adachi, Yoshiko Kato, Eiji Tsuda, Kousaku Nagasaka, Keigo Yoshida, Yoshiko Kawasaki, Naoki Aizawa, Kyouhei Nishi, Tetsuko Kato

Abstract:

The epidemic of the coronavirus (COVID-19), first reported in Wuhan at the end of 2019, has drastically changed our daily lives. Under these circumstances, counseling, which provides psychological support to people, was also greatly affected. The structure of counseling, which had generally been implicitly common practice to be conducted in person, was greatly shaken. The author wondered how counseling can be conducted in situations where it is impossible to meet face-to-face. This is where telepsychological support (online counseling) came into use. The authors found that there were the following problems in telepsychological support: (1) anxiety about whether the communication is appropriate, (2) difficulty in understanding the client's situation and condition, (3) inability to perceive what was normally perceived in person, (4) difficulty in adjusting to severely ill clients, (5) difficulty in dealing with emergency situations, etc. In this study, we interviewed psychologists who had been accustomed to telepsychological support for more than two years after the Corona disaster began to clarify how they had or had not overcome the problems of telepsychological support identified in the above studies. We also aim to consider the unique possibilities of how telepsychological support, a new technique of psychological support, can be implemented to provide more effective and meaningful support in society after the end of the Corona disaster (post-Corona society). Thirteen psychologists who are currently providing telepsychological support in the Corona Disaster will be interviewed, and semi-structured interviews will be conducted for one hour per person. In order to empirically examine how the problems in telepsychological support had been overcome or not through the interview survey, the authors asked (1) how they overcame their anxiety about whether they were able to communicate appropriately, (2) how they devised ways to overcome it, (3) how they overcame the difficulty in adapting to heavy clients in terms of the level of the disease, (4) how they overcame the difficulty in dealing with emergency situations. The interviews were analyzed using Thematic Analysis, a qualitative analysis method commonly used in qualitative research overseas. The authors found that some devices and perspectives were newly discovered as a result of two years of practice of telepsychological support and that psychologists in this study considered face-to-face interviews and telepsychological support to be separate and were flexible enough to use them when available and to move to face-to-face interviews when not appropriate.

Keywords: telepsychology, COVID-19, Corona, psychologist

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Authors: Rizwan Fazal

Abstract:

This study investigates the behavior of external sector of Pakistan economy and its impact on economic growth, using quarterly data for the period 1990:01-2010:04. External sector indices used in this study are financial integration, net foreign assets and trade integration. Augmented Ducky fuller confirms that all variables of external sector are non-stationary at level, but at first difference it becomes stationary. The co-integration test suggests one co-integrating variables in the study. The analysis is based on Vector Auto Regression model followed by Vector Error Correction Model. The empirical findings show that financial integration play important role in increasing economic growth in Pakistan economy while trade integration has negative effect on economic growth of Pakistan in the long run. However, the short run confirms that output lag accounts for error correction. The estimated CUSUM and CUSUMQ stability test provide information that the period of the study equation remains stable.

Keywords: financial integration, trade integration, net foreign assets, gross domestic product

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Authors: Bowen Ding, Yihao Kuang

Abstract:

In recent years, with the development of large-scale pre-trained lan-guage models, building vector representations of text through deep neural network models has become a standard practice for natural language processing tasks. From the performance on downstream tasks, we can know that the text representation constructed by these models contains linguistic information, but its encoding mode and extent are unclear. In this work, a structural probe is proposed to detect whether the vector representation produced by a deep neural network is embedded with a syntax tree. The probe is trained with the deep metric learning method, so that the distance between word vectors in the metric space it defines encodes the distance of words on the syntax tree, and the norm of word vectors encodes the depth of words on the syntax tree. The experiment results on ELMo and BERT show that the syntax tree is encoded in their parameters and the word representations they produce.

Keywords: deep metric learning, syntax tree probing, natural language processing, word representations

Procedia PDF Downloads 63

Authors: B. Seetanah, R. V. Sannassee, Lamport, K. Padachi, K. Seetah, S. Matadeen, N. Okurutt, N. Ama, L. Mokoodi

Abstract:

This paper analyses the impact of the various enabling elements towards fostering entrepreneurial behavior for two Sub Saharan African countries namely Mauritius and Botswana, with focus is on role of institutions (ministries, government support institutions, financing institutions and SME associations). Using a structural equation modeling framework, it is found that finance was some of the most determinant of respondents’ evaluation of the business climate thus emphasizing on the crucial of such an ingredient. Interestingly government related factors such as government support and institutional support are also reported to have a significant influence on the SME business climate in both countries.

Keywords: institutions, SME, SEM, Mauritius, Botswana

Procedia PDF Downloads 391