Search results for: Global Accuracy Indicator (GAI)

Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: Sabahat Zahra

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The purpose of this research is to present the changing global environment and its potential impacts on sustainable food and energy production at global level, particularly in Pakistan. The food and energy related-economic sector has been subjected to negative consequences due to recent extreme changes in weather conditions, particularly in developing countries. Besides continuous modifications in weather, population is also increasing by time, therefore it is necessary to take special steps and start effective initiatives to cope with the challenges of food and energy security to fight hunger and for economic stability of country. Severe increase in temperature and heat waves has also negative impacts on food production as well as energy sustainability. Energy (in terms of electricity) consumption has grown up than the production potential of the country as a consequence of increasing warm weather. Ultimately prices gone up when there is more consumption than production. Therefore, all these aspects of climate change are interrelated with socio-economic issues. There is a need to develop long-term policies on regional and national levels for maintainable economic growth. This research presents a framework-plan and recommendations for implementation needed to mitigate the potential threats due to global climate change sustainable food and energy production under climate change in the country.

Keywords: climate changes, energy security, food security, global climate change

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Authors: Di Luo, Maria Buchberger, Anja Pickhard

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Objectives: The purpose of this study was to assess and compare the diagnostic accuracy of four common morphological approaches, including sonography, computed tomography (CT), positron emission tomography/computed tomography (PET/CT), and magnetic resonance imaging (MRI) for the evaluation of cervical lymph node metastases in head and neck squamous cell carcinoma (HNSCC) patients. Material and Methods: Included in this retrospective study were 26 patients diagnosed with HNSCC between 2010 and 2011 who all underwent sonography, CT, PET/CT, and MRI imaging before neck dissection. Morphological data were compared to the corresponding histopathological results. Statistical analysis was performed with SPSS statistic software (version 26.0), calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for detection of cervical lymph node metastases. Results: The 5-year survival rate of the patient collective was 55.5%.Risk factors for survival included initial primary tumor stage, initial lymph node stage, initial metastasis status, and therapeutic approaches. Cox regression showed initial metastasis status(HR 8.671, 95%CI 1.316-57.123, p=0.025) and therapeutic approaches(HR 6.699, 95%CI 1.746-25.700, p=0.006)to be independent predictive risk factors for survival. Sensitivity was highest for MRI (96% compared to 85% for sonography and 89% for CT and PET/CT). Specificity was comparable with 95 % for CT and 98 % for sonography and PET/CT, but only 68% for MRI. While the MRI showed the least PPV (34%) compared to all other methods (85% for sonography,75% for CT, and 86% for PET/CT), the NPV was comparable in all methods(98-99%). The overall accuracy of cervical lymph node metastases detection was comparable for sonography, CT, and PET/CT with 96%,97%,94%, respectively, while MRI had only 72% accuracy. Conclusion: Since the initial status of metastasis is an independent predictive risk factor for patients’ survival, efficient detection is crucial to plan adequate therapeutic approaches. Sonography, CT, and PET/CT have better diagnostic accuracy than MRI for the evaluation of cervical lymph node metastases in HNSCC patients.

Keywords: cervical lymph node metastases, diagnostic accuracy, head and neck squamous carcinoma, risk factors, survival

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Authors: Ho-Hsin Lee, Yue-Min Jiang, Shu-Hui Tsai, Jian-Ren Chen, Mei-Yu XU, Wen-Tien Wu

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This project presents an AI-based method and system for wound status analysis. The system uses a three-in-one sensor device to analyze wound status, including color, temperature, and a 3D sensor to provide wound information up to 2mm below the surface, such as redness, heat, and blood circulation information. The system has a 90% accuracy rate, requiring only one manual correction in 70% of cases, with a one-second delay. The system also provides an offline application that allows for manual correction of the wound bed range using color-based guidance to estimate wound bed size with 96% accuracy and a maximum of one manual correction in 96% of cases, with a one-second delay. Additionally, AI-assisted wound bed range selection achieves 100% of cases without manual intervention, with an accuracy rate of 76%, while AI-based wound tissue type classification achieves an 85.3% accuracy rate for five categories. The AI system also includes similar case search and expert recommendation capabilities. For AI-assisted wound range selection, the system uses WIFI6 technology, increasing data transmission speeds by 22 times. The project aims to save up to 64% of the time required for human wound record keeping and reduce the estimated time to assess wound status by 96%, with an 80% accuracy rate. Overall, the proposed AI method and system integrate multiple sensors to provide accurate wound information and offer offline and online AI-assisted wound bed size estimation and wound tissue type classification. The system decreases delay time to one second, reduces the number of manual corrections required, saves time on wound record keeping, and increases data transmission speed, all of which have the potential to significantly improve wound care and management efficiency and accuracy.

Keywords: wound status analysis, AI-based system, multi-sensor integration, color-based guidance

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Authors: Areej Salaymeh, Loren Schwiebert, Stephen Remias, Jonathan Waddell

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Extracting information from new data sources has emerged as a crucial task in many traffic planning processes, such as identifying traffic patterns, route planning, traffic forecasting, and locating infrastructure improvements. Given the advanced technologies used to collect Global Positioning System (GPS) data from dedicated GPS devices, GPS equipped phones, and navigation tools, intelligent data analysis methodologies are necessary to mine this raw data. In this research, an automatic detection framework is proposed to help identify and classify the locations of stopped GPS waypoints into two main categories: signalized intersections or highway congestion. The Delaunay triangulation is used to perform this assessment in the clustering phase. While most of the existing clustering algorithms need assumptions about the data distribution, the effectiveness of the Delaunay triangulation relies on triangulating geographical data points without such assumptions. Our proposed method starts by cleaning noise from the data and normalizing it. Next, the framework will identify stoppage points by calculating the traveled distance. The last step is to use clustering to form groups of waypoints for signalized traffic and highway congestion. Next, a binary classifier was applied to find distinguish highway congestion from signalized stop points. The binary classifier uses the length of the cluster to find congestion. The proposed framework shows high accuracy for identifying the stop positions and congestion points in around 99.2% of trials. We show that it is possible, using limited GPS data, to distinguish with high accuracy.

Keywords: Delaunay triangulation, clustering, intelligent transportation systems, GPS data

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Authors: Jan Cernohorsky

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This article aims to assess the evolution of imperfect competition in selected banking markets, in particular in the banking markets of Slovakia, Poland, Hungary, Slovenia and Croatia. Another objective is to assess the evolution of the relationship of imperfect competition and profit development in the banking markets. The article first provides an overview of literature on the topic. It then measures the degree of imperfect competition in individual markets using the Herfindahl-Hirschman Index. The commonly used indicator of total assets was chosen as an indicator. Based on this measurement, the individual banking sectors are categorized into theoretical definitions of the various types of imperfect competition - namely all surveyed banking sectors falling within the theoretical definition of monopolistic competition. Subsequently, using correlation analysis, i.e., the Pearson correlation coefficient, or the Spearman correlation coefficient, the connection between the evolution of imperfect competition and the development of the gross profit on selected banking markets was surveyed. It was found that with the exception of the banking market in Slovenia, where there is a positive correlation; there is no correlation between the evolution of imperfect competition and profit development in the selected markets. This means a recommendation for the regulators that it is not appropriate to rationalize a higher degree of regulation in granting banking licenses on the size of the profits attained in the banking market, as the relationship between the degree of concentration in the banking market and the amount of profit according to our measurements does not exist.

Keywords: bank, banking system, imperfect competition, profitability

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Authors: Fuzhang Zhao

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Particle swarm optimization (PSO) is becoming one of the most important swarm intelligent paradigms for solving global optimization problems. Although some progress has been made to improve PSO algorithms over the last two decades, additional work is still needed to balance parameters to achieve better numerical properties of accuracy, efficiency, and stability. In the optimal PSO algorithm, the optimal weightings of (√ 5 − 1)/2 and (3 − √5)/2 are used for the cognitive factor and the social factor, respectively. By the same token, the same optimal weightings have been applied for intensification searches and diversification searches, respectively. Perturbation and constriction effects are optimally balanced. Simulations of the de Jong, the Rosenbrock, and the Griewank functions show that the optimal PSO algorithm indeed achieves better numerical properties and outperforms the canonical PSO algorithm.

Keywords: diversification search, intensification search, optimal weighting, particle swarm optimization

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Authors: M. Akhlaque Ahmed, Maliha Afshan, Adeel Tahir

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Global and diffuse solar radiation studies have been carried out for the Capital city of Pakistan, Islamabad ( latitude 330 43’N and Longitude 370 71’E) to assess the solar potential of the area. The global and diffuse solar radiation were carried out using sunshine hour data for the above-mentioned area. Monthly total solar radiation is calculated through regression constants a and b through declination angle of the sun and sunshine hours and KT that is cloudiness index are used to calculate the diffuse solar radiation. Result obtained shows variation in the direct and diffuse component of solar radiation in summer and winter months for Islamabad. Diffuse solar radiation was found maximum in July, i.e., 32% whereas direct or beam radiation was found to be high in April to June, i.e., 73%. During July, August, and December, the sky was found cloudy. From the result, it appears that with the exception of monsoon month July and August the solar energy can be utilized very efficiently throughout the year in Islamabad.

Keywords: global radiation, Islamabad, diffuse radiation, sky condition, sunshine hour

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Authors: Eko Nursanty, Joesron Alie Syahbana, Atik Suprapti

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Tourist gaze is a set of experiences that experienced by a tourist in attempt to familiarize himself with the certain local tourism site’s condition. It is started from looking for information prior arriving at the location, then during the visit and gaining unique experience with the local inhabitant, and then experiencing the ingenuity of the location, finally to bring impression that keeps on attaching despite leaving from it. This research attempted to grab the message of tourist gaze in the process of structuring which is conducted in the global tourism in the cities in Indonesia, particularly Solo as the study case of the research. The method employed is the field observation of qualitative research. The expected result is to relate the tourist gaze theory with the development of ongoing global tourism.

Keywords: tourist gaze, tourism, city branding, Solo

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Authors: John G. Vongas, Raghid Al Hajj

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We investigated the effects of winning and losing on men’s testosterone and assessed whether androgen reactivity affected their empathic accuracy and their aggression. We also explored whether their power motivation would moderate the relationships between competitive, hormonal, and behavioral outcomes. In Experiment 1, 84 males competed on a task that allegedly gauged their leadership potential and future earnings, after which they interpreted people’s emotional expressions. Results showed that winners were more capable of accurately inferring others’ emotions compared to losers and this ability improved with increasing power. Second, testosterone change mediated the relationship between competitive outcomes and empathic accuracy, with post-competitive testosterone increases relating to more accuracy. In Experiment 2, 72 males again competed after which they were measured on two aggression subtypes: proactive and reactive. Results showed that neither the competitive outcome nor the testosterone change had a significant effect on either types of aggression. However, as power increased, winners aggressed more proactively than losers whereas losers aggressed more reactively than winners. Finally, in both experiments, power moderated the relationship between competitive outcomes and testosterone change. Collectively, these studies add to existing research that explores the psychophysiological effects of competition on individuals’ empathic and aggressive responses.

Keywords: competition, testosterone, power motivation, empathic accuracy, proactive aggression, reactive aggression

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Authors: Arista Mayang Sari Slamet

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This study was conducted to see the shape or behavior that shows the attitude of confidence in the practice of debate on science program students in Senior High School. This research is a descriptive qualitative study by explaining the forms of behavior of each indicator (there are ten indicators) confidence of Santrock. Data collection using interviews with Indonesian language teachers, direct observation, and documents. In this study, it was found that there is one item that is not visible indicator of the high school students of class X, which is the fourth item ‘Sitting with others in social activities’. This is caused by the forum examined are debating forum, so there is a social activity can’t be seen. The result of this study there are two students who do not show the behavior of confidence, their name is Dea and Audria (from the pro team). This indicates that the head of a pro team dominated the debate. The time for the debate is 45 minutes. Therefore all students in both of team can’t demonstrate their debate skill. In each team is only dominated by one student. The most common forms of confidence behavior are expressing opinion, look at the other person (speaker), and keeping eye contact with the other person. This indicates that the attitude of confidence by looking at the other person makes them more confident about their opinion. The most uncommon indicators is to direct or instruct to the other person. This shows that the attitude of self-confidence shown by the students isn’t lead.

Keywords: confidence, debate, senior high school, Jakarta

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Authors: Ferzat Anka

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Many types of problems can be solved using traditional analytical methods. However, these methods take a long time and cause inefficient use of resources. In particular, different approaches may be required in solving complex and global engineering problems that we frequently encounter in real life. The bigger and more complex a problem, the harder it is to solve. Such problems are called Nondeterministic Polynomial time (NP-hard) in the literature. The main reasons for recommending different metaheuristic algorithms for various problems are the use of simple concepts, the use of simple mathematical equations and structures, the use of non-derivative mechanisms, the avoidance of local optima, and their fast convergence. They are also flexible, as they can be applied to different problems without very specific modifications. Thanks to these features, it can be easily embedded even in many hardware devices. Accordingly, this approach can also be used in trend application areas such as IoT, big data, and parallel structures. Indeed, the metaheuristic approaches are algorithms that return near-optimal results for solving large-scale optimization problems. This study is focused on the new metaheuristic method that has been merged with the chaotic approach. It is based on the chaos theorem and helps relevant algorithms to improve the diversity of the population and fast convergence. This approach is based on Chimp Optimization Algorithm (ChOA), that is a recently introduced metaheuristic algorithm inspired by nature. This algorithm identified four types of chimpanzee groups: attacker, barrier, chaser, and driver, and proposed a suitable mathematical model for them based on the various intelligence and sexual motivations of chimpanzees. However, this algorithm is not more successful in the convergence rate and escaping of the local optimum trap in solving high-dimensional problems. Although it and some of its variants use some strategies to overcome these problems, it is observed that it is not sufficient. Therefore, in this study, a newly expanded variant is described. In the algorithm called Ex-ChOA, hybrid models are proposed for position updates of search agents, and a dynamic switching mechanism is provided for transition phases. This flexible structure solves the slow convergence problem of ChOA and improves its accuracy in multidimensional problems. Therefore, it tries to achieve success in solving global, complex, and constrained problems. The main contribution of this study is 1) It improves the accuracy and solves the slow convergence problem of the ChOA. 2) It proposes new hybrid movement strategy models for position updates of search agents. 3) It provides success in solving global, complex, and constrained problems. 4) It provides a dynamic switching mechanism between phases. The performance of the Ex-ChOA algorithm is analyzed on a total of 8 benchmark functions, as well as a total of 2 classical and constrained engineering problems. The proposed algorithm is compared with the ChoA, and several well-known variants (Weighted-ChoA, Enhanced-ChoA) are used. In addition, an Improved algorithm from the Grey Wolf Optimizer (I-GWO) method is chosen for comparison since the working model is similar. The obtained results depict that the proposed algorithm performs better or equivalently to the compared algorithms.

Keywords: optimization, metaheuristic, chimp optimization algorithm, engineering constrained problems

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Authors: Ehsan Soltanzadeh, Hassan Zare

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This paper introduces examples of the influences of global warming on water resources and means of adaptation. The contributing causes of shortage in water resources are sophisticated and have interactions with each other. The world-scale phenomena like global warming have led to an increase in air and ocean’s mean temperature, and this has already caused adverse effects on water resources. Other factors that exacerbated this situation such as population increase, changes in farming habits, rise in city dwellers, unbalanced request for energy and aquatic resources, improved living standards, new eating habits, increasing economic growth and consequently flourishing industrial activities, and different types of pollution such as air, water, etc., are compelling more pressure on our limited water resources. The report will briefly discuss climate change and its detrimental impacts on the water resources and finally will introduce two effective solutions to mitigate the consequences or even reverse them in the near to mid-term future: utilization of molten salt technology for storing huge amounts of generated electricity in solar power plants to accommodate power grid demands, and implementing fuel cell CHPs to reduce carbon emission, and consequently, mitigate the global warming phenomenon as the major root cause of threatening water resources.

Keywords: climate change, global warming, water resources, GHG emissions, fuel cell-CHP, solar power plant, molten salt storage

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Authors: Aarati Susan Mathew, Radhika Narendra Patel, Jiji Mathew

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A retrospective study conducted at Christian Medical College (CMC) Teaching Hospital, Vellore, India on 14th August 2014 to assess the accuracy of clinically estimated foetal weight upon labour admission. Estimating foetal weight is a crucial factor in assessing maternal and foetal complications during and after labour. Medical notes of ninety-eight postnatal women who fulfilled the inclusion criteria were studied to evaluate the correlation between their recorded Estimated Foetal Weight (EFW) on admission and actual birth weight (ABW) of the newborn after delivery. Data concerning maternal and foetal demographics was also noted. Accuracy was determined by absolute percentage error and proportion of estimates within 10% of ABW. Actual birth weights ranged from 950-4080g. A strong positive correlation between EFW and ABW (r=0.904) was noted. Term deliveries (≥40 weeks) in the normal weight range (2500-4000g) had a 59.5% estimation accuracy (n=74) compared to pre-term (<40 weeks) with an estimation accuracy of 0% (n=2). Out of the term deliveries, macrosomic babies (>4000g) were underestimated by 25% (n=3) and low birthweight (LBW) babies were overestimated by 12.7% (n=9). Registrars who estimated foetal weight were accurate in babies within normal weight ranges. However, there needs to be an improvement in predicting weight of macrosomic and LBW foetuses. We have suggested the use of an amended version of the Johnson’s formula for the Indian population for improvement and a need to re-audit once implemented.

Keywords: clinical palpation, estimated foetal weight, pregnancy, India, Johnson’s formula

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Authors: Ana Avila Bohorquez

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Elite (Netflix, 2018-) is the second original series produced by Netflix in Spain. Premiered in 2018, it became an international success, both critically and among audiences. Reviewers praised its use of teen drama tropes with a more progressive twist. Netflix announced that the first season had been streamed by over 20 million accounts within its first month of release. This paper aims to determine what characteristics led to Elite’s international success, finding the elements of its narrative and visual design that resonate with global audiences. After reviewing the bibliography about transnational fiction, questionnaires sent to international audience members through social media shed light on what these characteristics are. Additionally, interviews with the creative team were performed in order to compare their point of view with the audiences’ perception. Even though Elite can be considered a Spanish show from its inception, it's setting in the “fantasy” world of the rich and its lack of social realism so common among Spanish productions managed to attract global audiences, to whom it has appealed on a more emotional level.

Keywords: elite, global television, Netflix, teen drama, transnational fiction

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Authors: Kayode A. Olaniyi, Olabanji F. Omotoye, Adeola A. Ogunleye

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The skew detection and correction form an important part of digital document analysis. This is because uncompensated skew can deteriorate document features and can complicate further document image processing steps. Efficient text document analysis and digitization can rarely be achieved when a document is skewed even at a small angle. Once the documents have been digitized through the scanning system and binarization also achieved, document skew correction is required before further image analysis. Research efforts have been put in this area with algorithms developed to eliminate document skew. Skew angle correction algorithms can be compared based on performance criteria. Most important performance criteria are accuracy of skew angle detection, range of skew angle for detection, speed of processing the image, computational complexity and consequently memory space used. The standard Hough Transform has successfully been implemented for text documentation skew angle estimation application. However, the standard Hough Transform algorithm level of accuracy depends largely on how much fine the step size for the angle used. This consequently consumes more time and memory space for increase accuracy and, especially where number of pixels is considerable large. Whenever the Hough transform is used, there is always a tradeoff between accuracy and speed. So a more efficient solution is needed that optimizes space as well as time. In this paper, an improved Hough transform (HT) technique that optimizes space as well as time to robustly detect document skew is presented. The modified algorithm of Hough Transform presents solution to the contradiction between the memory space, running time and accuracy. Our algorithm starts with the first step of angle estimation accurate up to zero decimal place using the standard Hough Transform algorithm achieving minimal running time and space but lacks relative accuracy. Then to increase accuracy, suppose estimated angle found using the basic Hough algorithm is x degree, we then run again basic algorithm from range between ±x degrees with accuracy of one decimal place. Same process is iterated till level of desired accuracy is achieved. The procedure of our skew estimation and correction algorithm of text images is implemented using MATLAB. The memory space estimation and process time are also tabulated with skew angle assumption of within 00 and 450. The simulation results which is demonstrated in Matlab show the high performance of our algorithms with less computational time and memory space used in detecting document skew for a variety of documents with different levels of complexity.

Keywords: hough-transform, skew-detection, skew-angle, skew-correction, text-document

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Authors: Gehan Hussein, Hala Agha, Rasha Abdelraof, Marina George, Antoine Fakhri

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Background: β-Thalassemia major (TM) and sickle cell disease (SCD) are inherited hemoglobin disorders resulting in chronic hemolytic anemia. Cardiovascular involvement is an important cause of morbidity and mortality in these groups of patients. The narrow border is between overt myocardial dysfunction and clinically silent left ventricular (LV) and / or right ventricular (RV) dysfunction in those patients. 3 D Speckle tracking echocardiography (3D STE) is a novel method for the detection of subclinical myocardial involvement. We aimed to study myocardial affection in SCD and TM using 3D STE, comparing it with conventional echocardiography, correlate it with serum ferritin level and lactate dehydrogenase (LDH). Methodology: Thirty SCD and thirty β TM patients, age range 4-18 years, were compared to 30 healthy age and sex matched control group. Cases were subjected to clinical examination, laboratory measurement of hemoglobin level, serum ferritin, and LDH. Transthoracic color Doppler echocardiography, 3D STE, tissue Doppler echocardiography, and aortic speckle tracking were performed. Results: significant reduction in global longitudinal strain (GLS), global circumferential strain (GCS), and global area strain (GAS) in SCD and TM than control (P value <0.001) there was significantly lower aortic speckle tracking in patients with TM and SCD than control (P value< 0.001). LDH was significantly higher in SCD than both TM and control and it correlated significantly positive mitral inflow E, (p value:0.022 and 0.072. r: 0.416 and -0.333 respectively) lateral E/E’ (p value.<0.001and 0.818. r. 0.618 and -0. 044.respectively) and septal E/E’ (p value 0.007 and 0.753& r value 0.485 and -0.060 respectively) in SCD but not TM and significant negative correlation between LDH and aortic root speckle tracking (value 0.681& r. -0.078.). The potential diagnostic accuracy of LDH in predicting vascular dysfunction as represented by aortic root GCS with a sensitivity 74% and aortic root GCS was predictive of LV dysfunction in SCD patients with sensitivity 100% Conclusion: 3D STE LV and RV systolic dysfunction in spite of their normal values by conventional echocardiography. SCD showed significantly lower right ventricular dysfunction and aortic root GCS than TM and control. LDH can be used to screen patients for cardiac dysfunction in SCD, not in TM

Keywords: thalassemia major, sickle cell disease, 3d speckle tracking echocardiography, LDH

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Authors: Sorayya Malek, Mogeeb Mosleh, Sharifah M. Syed

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In this study, Support Vector Machine (SVM) technique was applied to predict the dichotomized value of Dissolved oxygen (DO) from two freshwater lakes namely Chini and Bera Lake (Malaysia). Data sample contained 11 parameters for water quality features from year 2005 until 2009. All data parameters were used to predicate the dissolved oxygen concentration which was dichotomized into 3 different levels (High, Medium, and Low). The input parameters were ranked, and forward selection method was applied to determine the optimum parameters that yield the lowest errors, and highest accuracy. Initial results showed that pH, water temperature, and conductivity are the most important parameters that significantly affect the predication of DO. Then, SVM model was applied using the Anova kernel with those parameters yielded 74% accuracy rate. We concluded that using SVM models to predicate the DO is feasible, and using dichotomized value of DO yields higher prediction accuracy than using precise DO value.

Keywords: dissolved oxygen, water quality, predication DO, support vector machine

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Authors: Jianhong Xiang, Hao Xiang, Linyu Wang

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Compressed sensing (CS) has a wide range of applications in sparse signal reconstruction. Aiming at the problems of low recovery accuracy and long reconstruction time of existing reconstruction algorithms in medical imaging, this paper proposes a corrected smoothing L0 algorithm based on compressed sensing (CSL0). First, an approximate hyperbolic tangent function (AHTF) that is more similar to the L0 norm is proposed to approximate the L0 norm. Secondly, in view of the "sawtooth phenomenon" in the steepest descent method and the problem of sensitivity to the initial value selection in the modified Newton method, the use of the steepest descent method and the modified Newton method are jointly optimized to improve the reconstruction accuracy. Finally, the CSL0 algorithm is simulated on various images. The results show that the algorithm proposed in this paper improves the reconstruction accuracy of the test image by 0-0. 98dB.

Keywords: smoothed L0, compressed sensing, image processing, sparse reconstruction

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Authors: Ramona Zharfpeykan

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This study analyses the trend and nature of sustainability key performance indicators (KPIs) reporting in firms globally. It presents both trend and panel data of sustainability reports of 798 firms in the Global Reporting Initiative (GRI) database from 2010 to 2014. The results show some fluctuations in the frequency of sustainability KPI reporting globally across the time while the major focus of reports in firms stayed almost the same. It made us further analyse this trend and found that there are some indicators, such as 'environmental protect expenses' and 'number of grievances', that was barely reported over this period along with some highly popular ones such as 'direct economic value' and 'employment rate'. We could not find any statistical correlation between the KPI reporting percentage and the firms’ industries generally and neither if they belong to environmentally sensitive industries.

Keywords: global reporting initiatives, sustainability reporting, sustainability KPI, trends of sustainability reporting

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Authors: Leah Wilck

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Eyewitness identification is arguably one of the most utilized practices within our legal system; however, exoneration cases indicate that this practice may lead to accuracy and conviction errors. The purpose of this study was to examine the effects of the cross-race effect, the phenomena in which people are able to more easily and accurately identify faces from within their racial category, on the accuracy of eyewitness identification. Participants watched three separate videos of a perpetrator trying to steal a bicycle. In each video, the perpetrator was of a different race and gender. Participants watched a video where the perpetrator was a Black male, a White male, and a White female. Following the completion of watching each video, participants were asked to recall everything they could about the perpetrator they witnessed. The initial results of the study did not find the expected cross-race effect impacted the eyewitness identification accuracy. These surprising results are discussed in terms of cross-race bias and recognition theory as well as applied implications.

Keywords: cross race effect, eyewitness identification, own-race bias, racial profiling

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Authors: Andrey Matyukhin

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Russian official statistics have been showing a steady decline in residential real estate prices for several consecutive years. Price risk in real estate markets is thus affecting various groups of economic agents, namely, individuals, construction companies and financial institutions. Potential use of property derivatives might help mitigate adverse consequences of negative price dynamics. Unless a sustainable price indicator is developed, settlement of such instruments imposes constraints on counterparties involved while imposing restrictions on real estate market development. The study addresses geographical and classification heterogeneity in real estate prices by means of variance analysis in various groups of real estate properties. In conclusion, we determine optimal sample structure of representative real estate assets with sufficient level of price homogeneity. The composite price indicator based on the sample would have a higher level of robustness and reliability and hence improving liquidity in the market for property derivatives through underlying standardization. Unlike the majority of existing real estate price indices, calculated on country-wide basis, the optimal indices for Russian market shall be constructed on the city-level.

Keywords: price homogeneity, property derivatives, real estate price index, real estate price risk

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Authors: Mohd Ruzlin Mohd Mokhtar

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At present, most operators who are working on projects for utilities such as power, water, oil, gas, telecommunication and sewerage are using technologies e.g. Total station, Global Positioning System (GPS), Electromagnetic Locator (EML) and Ground Penetrating Radar (GPR) to perform underground utility mapping. With the increase in popularity of Horizontal Directional Drilling (HDD) method among the local authorities and asset owners, most of newly installed underground utilities need to use the HDD method. HDD method is seen as simple and create not much disturbance to the public and traffic. Thus, it was the preferred utilities installation method in most of areas especially in urban areas. HDDs were installed much deeper than exiting utilities (some reports saying that HDD is averaging 5 meter in depth). However, this impacts the accuracy or ability of existing underground utility mapping technologies. In most of Malaysia underground soil condition, those technologies were limited to maximum of 3 meter depth. Thus, those utilities which were installed much deeper than 3 meter depth could not be detected by using existing detection tools. The accuracy and reliability of existing underground utility mapping technologies or work procedure were in doubt. Thus, a mitigation action plan is required. While installing new utility using Horizontal Directional Drilling (HDD) method, a more accurate underground utility mapping can be achieved by using Gyro OMU compared to existing practice using e.g. EML and GPR. Gyro OMU is a method to accurately identify the location of HDD thus this mapping can be used or referred to avoid those cost of breakdown due to future HDD works which can be caused by inaccurate underground utility mapping.

Keywords: Gyro Orientation Measurement Unit (Gyro OMU), Horizontal Directional Drilling (HDD), Ground Penetrating Radar (GPR), Electromagnetic Locator (EML)

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Authors: Jonathan Gong

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Michelle J. Miller

Abstract:

In recent year, there has been a rise of two emerging issues that impact the global employment and business market that the legal community must review closer: offshore outsourcing and data privacy. These two issues intersect because employment opportunities are shifting due to offshore outsourcing and some States, like the United States, anti-outsourcing legislation has been passed or presented to retain jobs within the country. In addition, the legal requirements to retain the privacy of data as a global employer extends to employees and third party service provides, including services outsourced to offshore locations. For this reason, this paper will review the intersection of these two issues with a specific focus on data privacy.

Keywords: outsourcing, data privacy, international compliance, multinational corporations

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Authors: C. Rana, S. K. Jain

Abstract:

The enormous amount of Web data has challenged its usage in efficient manner in the past few years. As such, a range of techniques are applied to tackle this problem; prominent among them is personalization and recommender system. In fact, these are the tools that assist user in finding relevant information of web. Most of the e-commerce websites are applying such tools in one way or the other. In the past decade, a large number of recommendation algorithms have been proposed to tackle such problems. However, there have not been much research in the evaluation criteria for these algorithms. As such, the traditional accuracy and classification metrics are still used for the evaluation purpose that provides a static view. This paper studies how the evolution of user preference over a period of time can be mapped in a recommender system using a new evaluation methodology that explicitly using time dimension. We have also presented different types of experimental set up that are generally used for recommender system evaluation. Furthermore, an overview of major accuracy metrics and metrics that go beyond the scope of accuracy as researched in the past few years is also discussed in detail.

Keywords: collaborative filtering, data mining, evolutionary, clustering, algorithm, recommender systems

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Authors: Huda Algharib, Amal Algharib, Hanan Algharib, Ali Mohammad Alqudah

Abstract:

Image registration is an important topic for many imaging systems and computer vision applications. The standard image registration techniques such as Mutual information/ Normalized mutual information -based methods have a limited performance because they do not consider the spatial information or the relationships between the neighbouring pixels or voxels. In addition, the amount of image noise may significantly affect the registration accuracy. Therefore, this paper proposes an efficient method that explicitly considers the relationships between the adjacent pixels, where the gradient information of the reference and scene images is extracted first, and then the cosine similarity of the extracted gradient information is computed and used to improve the accuracy of the standard normalized mutual information measure. Our experimental results on different data types (i.e. CT, MRI and thermal images) show that the proposed method outperforms a number of image registration techniques in terms of the accuracy.

Keywords: image registration, mutual information, image gradients, image transformations

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Authors: David Tena-Gago, Jose M. Alcaraz Calero, Qi Wang

Abstract:

The evolution and integration of automated vehicles have become more and more tangible in recent years. State-of-the-art technological advances in the field of camera-based Artificial Intelligence (AI) and computer vision greatly favor the performance and reliability of the Advanced Driver Assistance System (ADAS), leading to a greater knowledge of vehicular operation and resembling human behavior. However, the exclusive use of this technology still seems insufficient to control vehicular operation at 100%. To reveal the degree of accuracy of the current camera-based automated driving AI modules, this paper studies the structure and behavior of one of the main solutions in a controlled testing environment. The results obtained clearly outline the lack of reliability when using exclusively the AI model in the perception stage, thereby entailing using additional complementary sensors to improve its safety and performance.

Keywords: accuracy assessment, AI-driven mobility, artificial intelligence, automated vehicles

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Authors: Awf A. Al-Khan, Michael J. Day, Judith Nimmo, Mourad Tayebi, Stewart D. Ryan, Samantha J. Richardson, Janine A. Danks

Abstract:

Osteosarcoma (OS) is the most common type of malignant primary bone tumour in dogs. In addition to their critical roles in bone formation and remodeling, parathyroid hormone-related protein (PTHrP) and its receptor (PTHR1) are involved in progression and metastasis of many types of tumours in humans. The aims of this study were to determine the localisation and expression levels of PTHrP and PTHR1 in canine OS tissues using immunohistochemistry and to investigate if this expression is correlated with survival time. Formalin-fixed, paraffin-embedded tissue samples from 44 dogs with known survival time that had been diagnosed with primary osteosarcoma were analysed for localisation of PTHrP and PTHR1. Findings showed that both PTHrP and PTHR1 were present in all OS samples. The dogs with high level of PTHR1 protein (16%) had decreased survival time (P<0.05) compared to dogs with less PTHR1 protein. PTHrP levels did not correlate with survival time (P>0.05). The results of this study indicate that the PTHR1 is expressed differently in canine OS tissues and this may be correlated with poor prognosis. This may mean that PTHR1 may be useful as a prognostic indicator in canine OS and could represent a good therapeutic target in OS.

Keywords: dog, expression, osteosarcoma, parathyroid hormone receptor 1 (PTHR1), parathyroid hormone-related protein (PTHrP), survival

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Authors: Shiva Kumar, G. S. Vijay, Srinivas Pai P., Shrinivasa Rao B. R.

Abstract:

In the present study RBF neural networks were used for predicting the performance and emission parameters of a biodiesel engine. Engine experiments were carried out in a 4 stroke diesel engine using blends of diesel and Honge methyl ester as the fuel. Performance parameters like BTE, BSEC, Tech and emissions from the engine were measured. These experimental results were used for ANN modeling. RBF center initialization was done by random selection and by using Clustered techniques. Network was trained by using fixed and varying widths for the RBF units. It was observed that RBF results were having a good agreement with the experimental results. Networks trained by using clustering technique gave better results than using random selection of centers in terms of reduced MRE and increased prediction accuracy. The average MRE for the performance parameters was 3.25% with the prediction accuracy of 98% and for emissions it was 10.4% with a prediction accuracy of 80%.

Keywords: radial basis function networks, emissions, performance parameters, fuzzy c means

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