Search results for: false positive

Authors: Kumar Dron Shrivastav, Ankan Mukherjee Das, Arti Taneja, Harpreet Singh, Priya Ranjan, Rajiv Janardhanan

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Cervical cancer is one of the most common cancer among women worldwide which can be cured if detected early. Manual pathology which is typically utilized at present has many limitations. The current gold standard for cervical cancer diagnosis is exhaustive and time-consuming because it relies heavily on the subjective knowledge of the oncopathologists which leads to mis-diagnosis and missed diagnosis resulting false negative and false positive. To reduce time and complexities associated with early diagnosis, we require an interactive diagnostic tool for early detection particularly in developing countries where cervical cancer incidence and related mortality is high. Incorporation of digital pathology in place of manual pathology for cervical cancer screening and diagnosis can increase the precision and strongly reduce the chances of error in a time-specific manner. Thus, we propose a robust and interactive cervical cancer image analysis and diagnostic tool, which can categorically process both histopatholgical and cytopathological images to identify abnormal cells in the least amount of time and settings with minimum resources. Furthermore, incorporation of a set of specific parameters that are typically referred to for identification of abnormal cells with the help of open source software -’R’ is one of the major highlights of the tool. The software has the ability to automatically identify and quantify the morphological features, color intensity, sensitivity and other parameters digitally to differentiate abnormal from normal cells, which may improve and accelerate screening and early diagnosis, ultimately leading to timely treatment of cervical cancer.

Keywords: cervical cancer, early detection, digital Pathology, screening

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Authors: Wajid Rehman, Sirajul Haq, Bakhtiar Muhammad, Syed Fahad Hassan, Amin Badshah, Muhammad Waseem, Fazal Rahim, Obaid-Ur-Rahman Abid, Farzana Latif Ansari, Umer Rashid

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Five novel triorganotin (IV) compounds have been synthesized and characterized. The tin atom is penta-coordinated to assume trigonal-bipyramidal geometry. Using in silico derived parameters; the objective of our study is to design and synthesize promiscuous antibacterials potent enough to combat resistance. Among various synthesized organotin (IV) complexes, compound 5 was found as potent antibacterial agent against various bacterial strains. Further lead optimization of drug-like properties was evaluated through in silico predictions. Data mining and computational analysis were utilized to derive compound promiscuity phenomenon to avoid drug attrition rate in designing antibacterials. Xanthine oxidase and human glucose- 6-phosphatase were found as only true positive off-target hits by ChEMBL database and others utilizing similarity ensemble approach. Propensity towards a-3 receptor, human macrophage migration factor and thiazolidinedione were found as false positive off targets with E-value 1/4> 10^-4 for compound 1, 3, and 4. Further, displaying positive drug-drug interaction of compound 1 as uricosuric was validated by all databases and docked protein targets with sequence similarity and compositional matrix alignment via BLAST software. Promiscuity of the compound 5 was further confirmed by in silico binding to different antibacterial targets.

Keywords: antibacterial activity, drug promiscuity, ADMET prediction, metallo-pharmaceutical, antimicrobial resistance

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: B. Kriheli, E. Levner, T. C. E. Cheng, C. T. Ng

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During the last decades, research interest in planning, scheduling, and control of emergency response operations, especially people rescue and evacuation from the dangerous zone of marine accidents, has increased dramatically. Until the survivors (called ‘targets’) are found and saved, it may cause loss or damage whose extent depends on the location of the targets and the search duration. The problem is to efficiently search for and detect/rescue the targets as soon as possible with the help of intelligent mobile robots so as to maximize the number of saved people and/or minimize the search cost under restrictions on the amount of saved people within the allowable response time. We consider a special situation when the autonomous mobile robots (AMR), e.g., unmanned aerial vehicles and remote-controlled robo-ships have no operator on board as they are guided and completely controlled by on-board sensors and computer programs. We construct a mathematical model for the search process in an uncertain environment and provide a new fast algorithm for scheduling the activities of the autonomous robots during the search-and rescue missions after an accident at sea. We presume that in the unknown environments, the AMR’s search-and-rescue activity is subject to two types of error: (i) a 'false-negative' detection error where a target object is not discovered (‘overlooked') by the AMR’s sensors in spite that the AMR is in a close neighborhood of the latter and (ii) a 'false-positive' detection error, also known as ‘a false alarm’, in which a clean place or area is wrongly classified by the AMR’s sensors as a correct target. As the general resource-constrained discrete search problem is NP-hard, we restrict our study to finding local-optimal strategies. A specificity of the considered operational research problem in comparison with the traditional Kadane-De Groot-Stone search models is that in our model the probability of the successful search outcome depends not only on cost/time/probability parameters assigned to each individual location but, as well, on parameters characterizing the entire history of (unsuccessful) search before selecting any next location. We provide a fast approximation algorithm for finding the AMR route adopting a greedy search strategy in which, in each step, the on-board computer computes a current search effectiveness value for each location in the zone and sequentially searches for a location with the highest search effectiveness value. Extensive experiments with random and real-life data provide strong evidence in favor of the suggested operations research model and corresponding algorithm.

Keywords: disaster management, intelligent robots, scheduling algorithm, search-and-rescue at sea

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Authors: Frederick Huie, Lauren Fusfeld, William Burchenal, Scott Howell, Alyssa McVey, Thomas F. Goss

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Alzheimer’s Disease (AD) is a degenerative brain disease characterized by memory loss and cognitive decline that presents a substantial economic burden for patients and health insurers in the US. This study evaluates the payer budget impact of the DISCERN™ test in the diagnosis and management of patients with symptoms of dementia evaluated for AD. DISCERN™ comprises three assays that assess critical factors related to AD that regulate memory, formation of synaptic connections among neurons, and levels of amyloid plaques and neurofibrillary tangles in the brain and can provide a quicker, more accurate diagnosis than tests in the current diagnostic pathway (CDP). An Excel-based model with a three-year horizon was developed to assess the budget impact of DISCERN™ compared with CDP in a Medicare Advantage plan with 1M beneficiaries. Model parameters were identified through a literature review and were verified through consultation with clinicians experienced in diagnosis and management of AD. The model assesses direct medical costs/savings for patients based on the following categories: •Diagnosis: costs of diagnosis using DISCERN™ and CDP. •False Negative (FN) diagnosis: incremental cost of care avoidable with a correct AD diagnosis and appropriately directed medication. •True Positive (TP) diagnosis: AD medication costs; cost from a later TP diagnosis with the CDP versus DISCERN™ in the year of diagnosis, and savings from the delay in AD progression due to appropriate AD medication in patients who are correctly diagnosed after a FN diagnosis.•False Positive (FP) diagnosis: cost of AD medication for patients who do not have AD. A one-way sensitivity analysis was conducted to assess the effect of varying key clinical and cost parameters ±10%. An additional scenario analysis was developed to evaluate the impact of individual inputs. In the base scenario, DISCERN™ is estimated to decrease costs by $4.75M over three years, equating to approximately $63.11 saved per test per year for a cohort followed over three years. While the diagnosis cost is higher with DISCERN™ than with CDP modalities, this cost is offset by the higher overall costs associated with CDP due to the longer time needed to receive a TP diagnosis and the larger number of patients who receive a FN diagnosis and progress more rapidly than if they had received appropriate AD medication. The sensitivity analysis shows that the three parameters with the greatest impact on savings are: reduced sensitivity of DISCERN™, improved sensitivity of the CDP, and a reduction in the percentage of disease progression that is avoided with appropriate AD medication. A scenario analysis in which DISCERN™ reduces the utilization for patients of computed tomography from 21% in the base case to 16%, magnetic resonance imaging from 37% to 27% and cerebrospinal fluid biomarker testing, positive emission tomography, electroencephalograms, and polysomnography testing from 4%, 5%, 10%, and 8%, respectively, in the base case to 0%, results in an overall three-year net savings of $14.5M. DISCERN™ improves the rate of accurate, definitive diagnosis of AD earlier in the disease and may generate savings for Medicare Advantage plans.

Keywords: Alzheimer’s disease, budget, dementia, diagnosis.

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Authors: Martina Kaufmann, Thomas Goetz, Anastasiya A. Lipnevich, Reinhard Pekrun

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Positive emotions have been shown to benefit from optimistic perceptions, even if these perceptions are illusory. The current research investigated the impact of illusions of control on positive emotions. There is empirical evidence showing that people are more emotionally attentive to losses than to gains. Hence, we expected that, compared to gains, losses in illusory control would have a stronger impact on positive emotions. The results of two experimental studies support this assumption: Participants who experienced gains in illusory control showed no substantial change in positive emotions. However, positive emotions decreased when they perceived a loss in illusory control. These results suggest that a loss of illusory control (but not a gain thereof) mediates the impact of the situation on individuals’ positive emotions. Implications for emotion theory and practice are discussed.

Keywords: cognitive appraisal, control, illusions, optimism, positive emotions

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Authors: S. Benchelha, H. Chennaoui, M. Hakdaoui, L. Baidder, H. Mansouri, H. Ejjaaouani, T. Benchelha

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Northern Morocco is characterized by relatively young mountains experiencing a very important dynamic compared to other areas of Morocco. The dynamics associated with the formation of the Rif chain (Alpine tectonics), is accompanied by instabilities essentially related to tectonic movements. The realization of important infrastructures (Roads, Highways,...) represents a triggering factor and favoring landslides. This paper is part of the establishment of landslides susceptibility map and concerns the mapping of unstable areas in the province of Taounate. The landslide was identified using the components of the false color (FCC) of images Landsat OLI: i) the first independent component (IC1), ii) The main component (PC), iii) Normalized difference index (NDI). This mapping for landslides class is validated by in-situ surveys.

Keywords: landslides, False Color Composite (FCC), Independent Component Analysis (ICA), Principal Component Analysis (PCA), Normalized Difference Index (NDI), Normalized Difference Mid Red Index (NDMIDR)

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Authors: Jaco Oosthuizen, Nerina C. Van Der Merwe

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The National Health Laboratory Services in Bloemfontien has been the diagnostic testing facility for 1830 patients for familial breast cancer since 1997. From the cohort, 540 were comprehensively screened using High-Resolution Melting Analysis or Next Generation Sequencing for the presence of point mutations and/or indels. Approximately 90% of these patients stil remain undiagnosed as they are BRCA1/2 negative. Multiplex ligation-dependent probe amplification was initially added to screen for copy number variation detection, but with the introduction of next generation sequencing in 2017, was substituted and is currently used as a confirmation assay. The aim was to investigate the viability of utilizing internationally designed copy number variation detection assays based on mostly European/Caucasian genomic data for use within a South African context. The multiplex ligation-dependent probe amplification technique is based on the hybridization and subsequent ligation of multiple probes to a targeted exon. The ligated probes are amplified using conventional polymerase chain reaction, followed by fragment analysis by means of capillary electrophoresis. The experimental design of the assay was performed according to the guidelines of MRC-Holland. For BRCA1 (P002-D1) and BRCA2 (P045-B3), both multiplex assays were validated, and results were confirmed using a secondary probe set for each gene. The next generation sequencing technique is based on target amplification via multiplex polymerase chain reaction, where after the amplicons are sequenced parallel on a semiconductor chip. Amplified read counts are visualized as relative copy numbers to determine the median of the absolute values of all pairwise differences. Various experimental parameters such as DNA quality, quantity, and signal intensity or read depth were verified using positive and negative patients previously tested internationally. DNA quality and quantity proved to be the critical factors during the verification of both assays. The quantity influenced the relative copy number frequency directly whereas the quality of the DNA and its salt concentration influenced denaturation consistency in both assays. Multiplex ligation-dependent probe amplification produced false positives due to ligation failure when ligation was inhibited due to a variant present within the ligation site. Next generation sequencing produced false positives due to read dropout when primer sequences did not meet optimal multiplex binding kinetics due to population variants in the primer binding site. The analytical sensitivity and specificity for the South African population have been proven. Verification resulted in repeatable reactions with regards to the detection of relative copy number differences. Both multiplex ligation-dependent probe amplification and next generation sequencing multiplex panels need to be optimized to accommodate South African polymorphisms present within the genetically diverse ethnic groups to reduce the false copy number variation positive rate and increase performance efficiency.

Keywords: familial breast cancer, multiplex ligation-dependent probe amplification, next generation sequencing, South Africa

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Authors: Ho Jeong Jin, Chang Won Seo, Choon Sik Cho, Bong Yong Choi, Kwang Kyun Na, Sang Rok Lee

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In this paper, a radar signal processing technique using the LFM-FSK (Linear Frequency Modulation-Frequency Shift Keying) is proposed for reducing the false alarm rate based on the compressive sensing. The LFM-FSK method combines FMCW (Frequency Modulation Continuous Wave) signal with FSK (Frequency Shift Keying). This shows an advantage which can suppress the ghost phenomenon without the complicated CFAR (Constant False Alarm Rate) algorithm. Moreover, the parametric sparse algorithm applying the compressive sensing that restores signals efficiently with respect to the incomplete data samples is also integrated, leading to reducing the burden of ADC in the receiver of radars. 24 GHz FMCW signal is applied and tested in the real environment with FSK modulated data for verifying the proposed algorithm along with the compressive sensing.

Keywords: compressive sensing, LFM-FSK radar, radar signal processing, sparse algorithm

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Authors: Manal Azzidani

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This research consideres the extension of special functions called Positive Linear Operators. the bounded linear operator which defined from normed space to Banach space will extend to the closure of the its domain, And extend identified linear functional on a vector subspace by Hana-Banach theorem which could be generalized to the positive linear operators.

Keywords: extension, positive operator, Riesz space, sublinear function

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Authors: Tulin Coskun

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We investigate the approximation of analytic functions of several variables in polydisc by the sequences of linear k-positive operators in Gadjiev sence. The approximation of analytic functions of complex variable by linear k-positive operators was tackled, and k-positive operators and formulated theorems of Korovkin's type for these operators in the space of analytic functions on the unit disc were introduced in the past. Recently, very general results on convergence of the sequences of linear k-positive operators on a simply connected bounded domain within the space of analytic functions were proved. In this presentation, we extend some of these results to the approximation of analytic functions of several complex variables by sequences of linear k-positive operators.

Keywords: analytic functions, approximation of analytic functions, Linear k-positive operators, Korovkin type theorems

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Authors: Thanin Sitthiwirattham, Jiraporn Reunsumrit

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We study the existence of positive solutions to the three points difference summation boundary value problem. We show the existence of at least one positive solution if f is either superlinear or sublinear by applying the fixed point theorem due to Krasnoselskii in cones.

Keywords: positive solution, boundary value problem, fixed point theorem, cone

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Authors: Jui-Chien Hsieh

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Background:12-lead electrocardiography(ECG) is one of frequently-used tools to detect atrial fibrillation (AF), which might degenerate into life-threaten stroke, in clinical Practice. Based on this study, the AF detection by the clinically-used 12-lead ECG device has only 0.73~0.77 positive predictive value (ppv). Objective: It is on great demand to develop a new algorithm to improve the precision of AF detection using 12-lead ECG. Due to the progress on artificial intelligence (AI), we develop an ECG deep model that has the ability to recognize AF patterns and reduce false-positive errors. Methods: In this study, (1) 570-sample 12-lead ECG reports whose computer interpretation by the ECG device was AF were collected as the training dataset. The ECG reports were interpreted by 2 senior cardiologists, and confirmed that the precision of AF detection by the ECG device is 0.73.; (2) 88 12-lead ECG reports whose computer interpretation generated by the ECG device was AF were used as test dataset. Cardiologist confirmed that 68 cases of 88 reports were AF, and others were not AF. The precision of AF detection by ECG device is about 0.77; (3) A parallel 4-layer 1 dimensional convolutional neural network (CNN) was developed to identify AF based on limb-lead ECGs and chest-lead ECGs. Results: The results indicated that this model has better performance on AF detection than traditional computer interpretation of the ECG device in 88 test samples with 0.94 ppv, 0.98 sensitivity, 0.80 specificity. Conclusions: As compared to the clinical ECG device, this AI ECG model promotes the precision of AF detection from 0.77 to 0.94, and can generate impacts on clinical applications.

Keywords: 12-lead ECG, atrial fibrillation, deep learning, convolutional neural network

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Authors: Nuray Turan, Maria Karanika-Murray

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In working life, positive behavior and positive mood researches has given importance more and more. Increasing research on the subject sreveals this importance. In this context, positive psychological capital (PsyCap), job crafting (JC), and creativity areamongtheprominentissues in working life. However, it is noteworthy that there is not enough research on the interaction between these three concepts. Therefore, this research has been designed. The question “Does the interaction between JC and PsyCap improve creativity in working life?” has been raised, and“JobCrafting Mediating Effect Between Positive Psychological Capital and Creativity” has been questioned. A questionnaire will be applied using PsyCap, JC and Creativity scales to find answers to the aforementioned questions. Who will be the survey participants is in the process of being determined.

Keywords: positive psychological capital, job crafting, creativity, working life

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Authors: C. C. Okafor

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The effect of different varieties of plantain (Horn, false horn and French) and fibre types (soy bean residue, cassava sievette and rice bran) on functional and organoleptic properties of plantain-based flour was assessed. Horn, false horn french were processed by washing, peeling with knife, slicing into 3mm thickness and steam blanched at 80℃ for 5minutes, oven dried at 65℃ for 48 hours and milled into flours with attrition mill, sieved with 60 mesh sieve, separately. Fibre sources were processed, milled and fractionated into 60, 40 & 20 mesh sizes. Both flours were blended as 80:20, 70:30 and 60:40. Results obtained indicated that water absorption capacity is highest (2.68) in French plantain variety irrespective of the fibre type used. And in all variety tested the swelling capacity is highest (2.93) when the plantain flour is blended with soy residue (SR) and lowest (1.25) when blended with rice brain (RB). The results show that there is significant variety and fibre type interaction effect at (P < : 0.05). Again the results showed that texture mold ability and overall acceptability were best (7.00) when soy residue was used where as addition of rice bran into plantain flour resulted in fufu with poor texture. This trend was observed in all the verities of plantain tested and in all of the particle size of flour. Using cassava serviette also yield fufu similar to that produced with soy residue in all the parameter tested (mold ability, texture and overall acceptability. Generally, plantain flours from french and false horn yielded better quality fufu in terms of texture mold ability, overall acceptability, irrespective of the fibre type used.

Keywords: functional, organoleptic, particle size, sieve mesh, variety

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: Ali Almadan, Anoop Krishnan, Ajita Rattani

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Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors.

Keywords: face recognition, body-worn cameras, deep learning, person identification

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Authors: Erva Kaygun

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The purpose of this research is to investigate the effects of positive psychology interventions on college students' positive-negative emotions, coronavirus anxiety, and career stress. 4 groups of college students are compared in terms of the level of exposure to PP constructs ( Non-Psychology, Psychology, Positive Psychology Course, and Positive Psychology Boot Camp). In this research, Pearson Correlation, independent t-tests, ANOVA, and Post-Hoc tests are conducted. Without being significant, the groups exposed to PP constructs showed higher positive emotions and total PERMA scores, whereas negative emotions, career stress, and coronavirus stress remained similar. It is crucial to indicate that career stress is higher among all psychology students when compared to non-psychology students. The results showed that the highest exposure group (PP Boot Camp) showed no difference in negative emotions, whereas higher PERMA scores and positive emotion scores were on the Positive and Negative Affect Schedule (PANAS) scale.

Keywords: positive psychology, college students, well being, anxiety

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Authors: Bryony Pound

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This study investigated how views of green or urban space affect learning performance. It provides evidence of the value of visual access to greenspace in work and learning environments, and builds on the extensive research into the cognitive and learning-related benefits of access to green and natural spaces, particularly in learning environments. It demonstrates that benefits of visual access to natural spaces whilst learning can produce statistically significant faster responses than those facing urban views after only 5 minutes. The primary hypothesis of this research was that a greenspace view would improve short-term learning. Participants were randomly assigned to either a view of parkland or of urban buildings from the same room. They completed a psychological test of two stages. The first stage consisted of a presentation of words from eight different categories (four manmade and four natural). Following this a 2.5 minute break was given; participants were not prompted to look out of the window, but all were observed doing so. The second stage of the test involved a word recognition/false memory test of three types. Type 1 was presented words from each category; Type 2 was non-presented words from those same categories; and Type 3 was non-presented words from different categories. Participants were asked to respond with whether they thought they had seen the words before or not. Accuracy of responses and reaction times were recorded. The key finding was that reaction times for Type 2 words (highest difficulty) were significantly different between urban and green view conditions. Those with an urban view had slower reaction times for these words, so a view of greenspace resulted in better information retrieval for word and false memory recognition. Importantly, this difference was found after only 5 minutes of exposure to either view, during winter, and with a sample size of only 26. Greenspace views improve performance in a learning task. This provides a case for better visual access to greenspace in work and learning environments.

Keywords: benefits, greenspace, learning, restoration

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Authors: Kimberli Breen

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Background Check-In, Check-Out is noted as the most widely implemented evidence-based intervention for youth at-promise within schools. Over twenty years of peer-reviewed research demonstrates the powerful effects of this Positive Behavioral Interventions and Supports (PBIS) practice when implemented with fidelity. However, literature to date has not explicitly connected this intervention with Positive Psychology. Aims This session will illustrate the powerful role Positive Psychology and core elements of PERMA play in the worldwide success of this intervention and how more explicitly aligning Positive Behavioral Interventions and Supports (PBIS) practices with Positive Psychology might remove common barriers to current implementation. Method Students receiving the Check-In, Check-Out intervention experience a warm, positive greeting from a caring adult (CICO Coach) before entering their first class of the day. Teachers then provide high frequency positive feedback to the students at the end of each time block, or segment, of the day. An “optimistic close” to the day is then provided by the same CICO Coach at the end of the school day via the “check-out” process, where students assess the day’s accomplishments and goal-set for the next day. Results CICO clearly aligns with the Positive Psychology core elements of PERMA (Positive Emotion, Engagement, Relationships, Meaning and Accomplishments) and could be further strengthened through explicit integration. Conclusion The already powerful impact and reach of the Check-In, Check-Out intervention can be further enhanced and expanded through greater alignment with Positive Psychology elements and practices. Initiating this important alignment with CICO also offers promise for further integration of Positive Psychology and Positive Behavioral Interventions and Supports.

Keywords: positive pscyhology, check-In check-out, schools, alignment

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Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Saugata Bose, Ritambhra Korpal

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The internet has increased the copy-paste scenarios amongst students as well as amongst researchers leading to different levels of plagiarized documents. For this reason, much of research is focused on for detecting plagiarism automatically. In this paper, an initiative is discussed where Natural Language Processing (NLP) techniques as well as supervised machine learning algorithms have been combined to detect plagiarized texts. Here, the major emphasis is on to construct a framework which detects external plagiarism from monolingual texts successfully. For successfully detecting the plagiarism, n-gram frequency comparison approach has been implemented to construct the model framework. The framework is based on 120 characteristics which have been extracted during pre-processing the documents using NLP approach. Afterwards, filter metrics has been applied to select most relevant characteristics and then supervised classification learning algorithm has been used to classify the documents in four levels of plagiarism. Confusion matrix was built to estimate the false positives and false negatives. Our plagiarism framework achieved a very high the accuracy score.

Keywords: lexical matching, shallow NLP, supervised machine learning algorithm, word n-gram

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Authors: Chamika Chiran Perera, Dananjaya Perera, Chirath Dasanayake, Banuka Athuraliya

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Male infertility is a major problem in the world, and it is a neglected and sensitive health issue in Sri Lanka. It can be determined by analyzing human semen samples. Sperm motility is one of many factors that can evaluate male’s fertility potential. In Sri Lanka, this analysis is performed manually. Manual methods are time consuming and depend on the person, but they are reliable and it can depend on the expert. Machine learning and deep learning technologies are currently being investigated to automate the spermatozoa motility analysis, and these methods are unreliable. These automatic methods tend to produce false positive results and false detection. Current automatic methods support different techniques, and some of them are very expensive. Due to the geographical variance in spermatozoa characteristics, current automatic methods are not reliable for motility analysis in Sri Lanka. The suggested system, DeepMotile, is to explore a method to analyze motility of human spermatozoa automatically and present it to the andrology laboratories to overcome current issues. DeepMotile is a novel deep learning method for analyzing spermatozoa motility parameters in the Sri Lankan population. To implement the current approach, Sri Lanka patient data were collected anonymously as a dataset, and glass slides were used as a low-cost technique to analyze semen samples. Current problem was identified as microscopic object detection and tackling the problem. YOLOv5 was customized and used as the object detector, and it achieved 94 % mAP (mean average precision), 86% Precision, and 90% Recall with the gathered dataset. StrongSORT was used as the object tracker, and it was validated with andrology experts due to the unavailability of annotated ground truth data. Furthermore, this research has identified many potential ways for further investigation, and andrology experts can use this system to analyze motility parameters with realistic accuracy.

Keywords: computer vision, deep learning, convolutional neural networks, multi-target tracking, microscopic object detection and tracking, male infertility detection, motility analysis of human spermatozoa

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Authors: Dylan Elliott, Katya Pertsova

Abstract:

Preposition errors are some of the most common errors created by L2 speakers. In addition, improving error correction and detection methods remains an open issue in the realm of Natural Language Processing (NLP). This research investigates whether the bidirectional encoder representations from the transformers model (BERT) have the potential to correct preposition errors accurately enough to be useful in error correction software. This research finds that BERT performs strongly when the scope of its error correction is limited to preposition choice. The researchers used an open-source BERT model and over three hundred thousand edited sentences from Wikipedia, tagged for part of speech, where only a preposition edit had occurred. To test BERT’s ability to detect errors, a technique known as multi-level masking was used to generate suggestions based on sentence context for every prepositional environment in the test data. These suggestions were compared with the original errors in the data and their known corrections to evaluate BERT’s performance. The suggestions were further analyzed to determine if BERT more often agreed with the judgements of the Wikipedia editors. Both the untrained and fined-tuned models were compared. Finetuning led to a greater rate of error-detection which significantly improved recall, but lowered precision due to an increase in false positives or falsely flagged errors. However, in most cases, these false positives were not errors in preposition usage but merely cases where more than one preposition was possible. Furthermore, when BERT correctly identified an error, the model largely agreed with the Wikipedia editors, suggesting that BERT’s ability to detect misused prepositions is better than previously believed. To evaluate to what extent BERT’s false positives were grammatical suggestions, we plan to do a further crowd-sourcing study to test the grammaticality of BERT’s suggested sentence corrections against native speakers’ judgments.

Keywords: BERT, grammatical error correction, preposition error detection, prepositions

Procedia PDF Downloads 123

Authors: Minh Dong Le, Viet Dung Nguyen, Do Huu Viet, Nguyen Huu Tu

Abstract:

In this paper, we present a new method for circumscribed masses detection in mammograms. Our method is evaluated on 23 mammographic images of circumscribed masses and 20 normal mammograms from public Mini-MIAS database. The method is quite sanguine with sensitivity (SE) of 95% with only about 1 false positive per image (FPpI). To achieve above results we carry out a progression following: Firstly, the input images are preprocessed with the aim to enhance key information of circumscribed masses; Next, we calculate and evaluate statistically basic features of abnormal regions on training database; Then, mammograms on testing database are divided into equal blocks which calculated corresponding features. Finally, using priority order of basic features to classify blocks as an abnormal or normal regions.

Keywords: mammograms, circumscribed masses, evaluated statistically, priority order of basic features

Procedia PDF Downloads 311

Authors: S. Wietrzychowski

Abstract:

This systematic review incorporates concepts used in the field of positive psychology in order to integrate important elements into suicide prevention programs for children and adolescents. The goal of this review is to help students and professionals gain insight to available prevention programs for suicide and to incorporate aspects of positive psychology into these programs. Evidence-based interventions such as Positive Youth Development will be discussed in detail in its relation to prevention and positive psychology. Concepts such as hope, optimism, coping, and resilience will be related to these interventions in order to improve these interventions. The review will also explain how these programs can help prevent suicidal thoughts and/or behaviors. Research on mentorship programs and early intervention programs will be included and related to the aforementioned positive psychology concepts. Since children and adolescents are such a vulnerable population, the review will highlight specific considerations for working with children in order to prevent risk factors for suicide and to build protective factors. This review will discuss the effectiveness of school-based programs that are integrated with positive psychology. Elements of these programs that have been shown to be most effective in preventing suicide in schools will also be identified. As a result of this presentation, participants will be able to 1) List at least 2 evidence-based suicide prevention programs, 2) Understand the connection between specific positive psychology concepts and suicide prevention, 3) Identify at least 3 factors which protect against suicide, 4) Describe at least 3 risk factors for suicide, and 5) Think critically about the positive elements of suicide prevention programs.

Keywords: children, adolescents, suicide, positive

Procedia PDF Downloads 355

Authors: Marwa Qaraqe, Muhammad Ismail, Erchin Serpedin

Abstract:

This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography (EEG). The proposed architecture is based on the decision fusion calculated from energy and neural synchronization related features. Specifically, one level of the detector calculates the condition number (CN) of an EEG matrix to evaluate the amount of neural synchronization present within the EEG channels. On a parallel level, the detector evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent (parallel) classification units based on support vector machines to determine the onset of a seizure event. The decisions from the two classifiers are then combined together according to two fusion techniques to determine a global decision. Experimental results demonstrate that the detector based on the AND fusion technique outperforms existing detectors with a sensitivity of 100%, detection latency of 3 seconds, while it achieves a 2:76 false alarm rate per hour. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0:17 seconds), yet it achieves 12 false alarms per hour.

Keywords: epilepsy, EEG, seizure onset, electroencephalography, neuron, detection

Procedia PDF Downloads 453

Authors: Mustafa Jahanara

Abstract:

The aim of present research was to study of the relationship between emotional intelligence, perceived stress, positive religious coping with psychological distress to in a sample of undergraduate students in Polytechnic University in Kabul. One hundred and fifty-tow students (102 male, 50 female) were included in this study. All participants completed the Emotional Intelligence Scale (EIS), General Health Questionnaire (GHQ 12), Perceived Stress Scale (PSS-10), and the Brief RCOPE. The results revealed that EI was negatively associated with perceived stress and psychological distress. Also emotional intelligence was positively correlated with positive religious coping. Perceived stress was positive related with psychological distress and negatively correlated with positive religious coping. Eventually positive religious coping was significantly and negatively correlated with psychological distress. However, emotional intelligence and positive religious coping could influence on mental health.

Keywords: emotional intelligence, perceived stress, positive religious coping, psychological distress

Procedia PDF Downloads 491

Authors: Luiz Carlos Dias Lima De Oliveira

Abstract:

Psychology does not have the task of being confined only to the knowledge of losses, weaknesses or diseases, because it is necessary to give analogous dedication to the investigation of human virtues, efforts and aptitudes. The reasons for living with greater constancy and expressiveness act as a protective condition for risk behaviors, but with less constancy and expressiveness they can be a viable parameter of suicidal ideation or potential suicidal initiatives. In other words, Positive Psychology scientifically studies human strengths and virtues. In the same way, we refer to the basic psychological needs of the human being, according to the Theory of Self-Determination: the need for belonging, competence and autonomy to live the best possible life or the ability to make positive decisions in life. In this sense, following the assumptions of Positive Psychology, we raise the question of what are the reasons for living, seeking a way to draw attention to positive aspects of life.

Keywords: psychology, positive, self-determination, belonging, competence, autonomy, depression, suicide.

Procedia PDF Downloads 54

Authors: Gautam Sharda

Abstract:

The concept of right speech is the third stage of the noble eightfold path as prescribed by the Buddha and followed by millions of practicing Buddhists. The Buddha lays a lot of importance on the notion of right speech (Samma Vacca). In the Angutara Nikaya, the Buddha mentioned what constitutes right speech, which is basically four kinds of abstentions; namely abstaining from false speech, abstaining from slanderous speech, abstaining from harsh or hateful speech and abstaining from idle chatter. The Buddha gives reasons in support of his view as to why abstaining from these four kinds of speeches is favourable not only for maintaining the peace and equanimity within an individual but also within a society. It is a known fact that when we say something harsh or slanderous to others, it eventually affects our individual peace of mind too. We also know about the many examples of hate speeches which have led to senseless cases of violence and which are well documented within our country and the world. Also, indulging in false speech is not a healthy sign for individuals within a group as this kind of a social group which is based on falsities and lies cannot really survive for long and will eventually lead to chaos. Buddha also told us to refrain from idle chatter or gossip as generally we have seen that idle chatter or gossip does more harm than any good to the individual and the society. Hence, if most of us actually inculcate this third stage (namely, right speech) of the noble eightfold path of the Buddha in our daily life, it would be highly beneficial both for the individual and for the harmony of the society.

Keywords: Buddhism, speech, individual, society

Procedia PDF Downloads 241