Search results for: depression detection

Authors: Amira Dhokar, Lobna Hlaoua, Lotfi Ben Romdhane

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Tweet contextualization (TC) is a new issue that aims to answer questions of the form 'What is this tweet about?' The idea of this task was imagined as an extension of a previous area called multi-document summarization (MDS), which consists in generating a summary from many sources. In both TC and MDS, the summary should ideally contain the most relevant information of the topic that is being discussed in the source texts (for MDS) and related to the query (for TC). Furthermore of being informative, a summary should be coherent, i.e. well written to be readable and grammatically compact. Hence, coherence is an essential characteristic in order to produce comprehensible texts. In this paper, we propose a new approach to improve readability and coherence for tweet contextualization based on bipartite graphs. The main idea of our proposed method is to reorder sentences in a given paragraph by combining most expressive words detection and HITS (Hyperlink-Induced Topic Search) algorithm to make up a coherent context.

Keywords: bipartite graphs, readability, summarization, tweet contextualization

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Authors: Dona Grace Jeyaseeli

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Water quality conditions in a pond are controlled by both natural processes and human influences. Natural factors such as the source of the pond water and the types of rock and soil in the pond watershed will influence some water quality characteristics. These factors are difficult to control but usually cause few problems. Instead, most serious water quality problems originate from land uses or other activities near or in the pond. The effects of these activities can often be minimized through proper management and early detection of problems through testing. In the present study a survey of three ponds in Coimbatore city, Tamilnadu, India were analyzed and found that water quality problems in their ponds, ranging from muddy water to fish kills. Unfortunately, most pond owners have never tested their ponds, and water quality problems are usually only detected after they cause a problem. Hence the present study discusses some common water quality parameters that may cause problems in ponds and how to detect through testing for better management of pond.

Keywords: water quality, pond, test, problem

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Authors: Ng Liang Shen, Ngahzaifa Abdul Ghani

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This research aimed at developing an early warning system for pre-diabetic and diabetics by analyzing simple and easily determinable signs and symptoms of diabetes among the people living in Malaysia using Particle Swarm Optimized Artificial. With the skyrocketing prevalence of Type 2 diabetes in Malaysia, the system can be used to encourage affected people to seek further medical attention to prevent the onset of diabetes or start managing it early enough to avoid the associated complications. The study sought to find out the best predictive variables of Type 2 Diabetes Mellitus, developed a system to diagnose diabetes from the variables using Artificial Neural Networks and tested the system on accuracy to find out the patent generated from diabetes diagnosis result in machine learning algorithms even at primary or advanced stages.

Keywords: diabetes diagnosis, Artificial Neural Networks, artificial intelligence, soft computing, medical diagnosis

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Authors: Ines Elouedi, Atef Hammouda

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This paper presents a new fast version of the generalized Multi-Directional Radon Transform method. The new method uses the inverse Fast Fourier Transform to lead to a faster Generalized Radon projections. We prove in this paper that the fast algorithm leads to almost the same results of the eldest one but with a considerable lower time computation cost. The projection end result of the fast method is a parameterized Radon space where a high valued pixel allows the detection of a curve from the original image. The proposed fast inversion algorithm leads to an exact reconstruction of the initial image from the Radon space. We show examples of the impact of this algorithm on the pattern recognition domain.

Keywords: fast generalized multi-directional Radon transform, curve, exact reconstruction, pattern recognition

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Authors: Sai Maniveer Adapa, Sai Guptha Perla, Adithya Reddy P.

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Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords: thyroid tumor diagnosis, ultrasound images, deep learning, machine learning, convolutional auto-encoder, support vector machine

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Authors: Robert Caulk

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A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

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Authors: S. Turkan

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In this paper, the Turkey’s Top 500 Industrial Enterprises data in 2014 were analyzed by data envelopment analysis. Data envelopment analysis is used to detect efficient decision-making units such as universities, hospitals, schools etc. by using inputs and outputs. The decision-making units in this study are enterprises. To detect efficient enterprises, some financial ratios are determined as inputs and outputs. For this reason, financial indicators related to productivity of enterprises are considered. The efficient foreign weighted owned capital enterprises are detected via super efficiency model. According to the results, it is said that Mercedes-Benz is the most efficient foreign weighted owned capital enterprise in Turkey.

Keywords: data envelopment analysis, super efficiency, logistic regression, financial ratios

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Authors: Danny Barash

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Riboswitches are RNA genetic control elements that were originally discovered in bacteria and provide a unique mechanism of gene regulation. They work without the participation of proteins and are believed to represent ancient regulatory systems in the evolutionary timescale. One of the biggest challenges in riboswitch research is that many are found in prokaryotes but only a small percentage of known riboswitches have been found in certain eukaryotic organisms. The few examples of eukaryotic riboswitches were identified using sequence-based bioinformatics search methods that include some slight structural considerations. These pattern-matching methods were the first ones to be applied for the purpose of riboswitch detection and they can also be programmed very efficiently using a data structure called affix arrays, making them suitable for genome-wide searches of riboswitch patterns. However, they are limited by their ability to detect harder to find riboswitches that deviate from the known patterns. Several methods have been developed since then to tackle this problem. The most commonly used by practitioners is Infernal that relies on Hidden Markov Models (HMMs) and Covariance Models (CMs). Profile Hidden Markov Models were also carried out in the pHMM Riboswitch Scanner web application, independently from Infernal. Other computational approaches that have been developed include RMDetect by the use of 3D structural modules and RNAbor that utilizes Boltzmann probability of structural neighbors. We have tried to incorporate more sophisticated secondary structure considerations based on RNA folding prediction using several strategies. The first idea was to utilize window-based methods in conjunction with folding predictions by energy minimization. The moving window approach is heavily geared towards secondary structure consideration relative to sequence that is treated as a constraint. However, the method cannot be used genome-wide due to its high cost because each folding prediction by energy minimization in the moving window is computationally expensive, enabling to scan only at the vicinity of genes of interest. The second idea was to remedy the inefficiency of the previous approach by constructing a pipeline that consists of inverse RNA folding considering RNA secondary structure, followed by a BLAST search that is sequence-based and highly efficient. This approach, which relies on inverse RNA folding in general and our own in-house fragment-based inverse RNA folding program called RNAfbinv in particular, shows capability to find attractive candidates that are missed by Infernal and other standard methods being used for riboswitch detection. We demonstrate attractive candidates found by both the moving-window approach and the inverse RNA folding approach performed together with BLAST. We conclude that structure-based methods like the two strategies outlined above hold considerable promise in detecting riboswitches and other conserved RNAs of functional importance in a variety of organisms.

Keywords: riboswitches, RNA folding prediction, RNA structure, structure-based methods

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Authors: Kushal Patel, Manasa Dittakavi, Cyrus Falsafi, Gretchen Lovett

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Rural America has seen a surge in opioid addiction rates and overdose deaths in recent years, becoming a significant public health crisis. This may be due to a variety of factors, such as lack of access to healthcare or other economic and social factors that can contribute to addiction such as poverty, unemployment, and social isolation. As the opioid epidemic has disproportionately affected rural communities, pregnant women in these areas may be highly susceptible and face additional difficulties in facing the appropriate care they need. Opioid use disorder has many negative effects on prenatal infants. These include changes in their microbiome, mental health, neurodevelopment and cognition. These can affect how the child performs in various activities in life and how they interact with others. It has been demonstrated that using cognitive behavioral therapy improves not just pain-related results but also mobility, quality of life, disability, and mood outcomes. This indicates that cognitive behavioral therapy (CBT) may be a useful therapeutic strategy for enhancing general health and wellbeing in people with opioid use problems. In terms of treating psychiatric diseases, CBT carries fewer dangers than opioids. One study that illustrates the potential for CBT to promote a reduction in opioid use disorder used self-reported drug use patterns 6 months prior to and during their pregnancy. At the beginning of the study, participants reported an average of 3.78 drug or alcohol use days in the previous 28 days, which decreased to 1.63 days after treatment. The study also found a decrease in depression scores, as measured by IDS scores, from 23.9 to 17.1 at the end of treatment. These and other results show that CBT can have meaningful impacts on pregnant women in Rural America who struggle with an opioid use disorder. This project has been approved by the West Virginia School of Osteopathic Medicine- Office of Research and Sponsored Programs and deemed non-research scholarly work.

Keywords: appalachia, CBT, opiods, pregnancy

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Authors: Keng H. Kong, Louis C. Tan, Wing L. Aw, Kay Y. Tay

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Background: Fatigue is a common problem in patients with Parkinson's disease, with reported prevalence of up to 70%. Fatigue can be disabling and has adverse effects on patients' quality of life. There is currently no satisfactory treatment of fatigue. Acupuncture is effective in the treatment of fatigue, especially that related to cancer. Its role in Parkinson's disease-related fatigue is uncertain. Aims: To evaluate the clinical efficacy of acupuncture treatment in Parkinson's disease-related fatigue. Hypothesis: We hypothesize that acupuncture is effective in alleviating Parkinson's disease-related fatigue. Design: A single center, randomized, controlled study with two parallel arms. Participants: Forty participants with idiopathic Parkinson's disease will be enrolled. Interventions: Participants will be randomized to receive verum (real) acupuncture or placebo acupuncture. The retractable non-invasive sham needle will be used in the placebo group. The intervention will be administered twice a week for five weeks. Main outcome measures: The primary outcome will be the change in general fatigue score of the multidimensional fatigue inventory at week 5. Secondary outcome measures include other subscales of the multidimensional fatigue inventory, movement disorders society-unified Parkinson's disease rating scale, Parkinson's disease questionnaire-39 and geriatric depression scale. All outcome measures will be assessed at baseline (week 0), completion of intervention (week 5) and 4 weeks after completion of intervention (week 9). Results: To date, 23 participants have been recruited and nine have completed the study. The mean age is 63.5±14.2 years, mean duration of Parkinson’s disease is 6.4±1.8 years and mean MDS-UPDRS score is 8.3±2.8. The mean general fatigue score of the multidimensional fatigue inventory is 13.5±4.6. No significant adverse event related to acupuncture is noted. Potential significance: If the results are as expected, this study will provide preliminary scientific evidence for the efficacy of acupuncture in Parkinson's Disease-related fatigue, and opens the door for a larger multicentre trial to be performed. In the longer term, it may lead to the integration of acupuncture in the care of patients with Parkinson's disease.

Keywords: acupuncture, fatigue, Parkinson's disease, trial

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Authors: Kwangmin Joo

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Automatic detection of lumbar vertebrae and classification method is proposed for evaluating the degree of disc narrowing. Prior to classification, deep learning based segmentation is applied to detect individual lumbar vertebra. M-net is applied to segment five lumbar vertebrae and fine-tuning segmentation is employed to improve the accuracy of segmentation. Using the features extracted from previous step, clustering technique, k-means clustering, is applied to estimate the degree of disc space narrowing under four grade scoring system. As preliminary study, techniques proposed in this research could help building an automatic scoring system to diagnose the severity of disc narrowing from X-ray images.

Keywords: Disc space narrowing, Degenerative disc disorders, Deep learning based segmentation, Clustering technique

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Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

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Authors: Zahra Khoshnood, Mehdi Kazaie, Sajedeh Neisi

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In 2013, 24 fish samples were taken from two fishery regions in the north of Persian Gulf near the Iranian coastal lines. The two flatfishes were Yellofin seabream (Acanthopagrus latus) and Longtail tuna (Thannus tonggol). We analyzed total Hg concentration of liver and muscle tissues by Mercury Analyzer (model LECO AMA 254). The average concentration of total Hg in edible Muscle tissue of deep-Flounder was measured in Bandar-Abbas and was found to be 18.92 and it was 10.19 µg.g-1 in Bandar-Lengeh. The corresponding values for Oriental sole were 8.47 and 0.08 µg.g-1. The average concentration of Hg in liver tissue of deep-Flounder, in Bandar-Abbas was 25.49 and that in Bandar-Lengeh was 12.52 µg.g-1.the values for Oriental sole were 11.88 and 3.2 µg.g-1 in Bandar-Abbas and Bandar-Lengeh, respectively.

Keywords: mercury, Acanthopagrus latus, Thannus tonggol, Persian Gulf

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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This study considers the selection of the most suitable in silico molecular descriptors that could be used for s-triazine pesticides characterization. Suitable descriptors among topological, geometrical and physicochemical are used for quantitative structure-retention relationships (QSRR) model establishment. Established models were obtained using linear regression (LR) and multiple linear regression (MLR) analysis. In this paper, MLR models were established avoiding multicollinearity among the selected molecular descriptors. Statistical quality of established models was evaluated by standard and cross-validation statistical parameters. For detection of similarity or dissimilarity among investigated s-triazine pesticides and their classification, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used and gave similar grouping. This study is financially supported by COST action TD1305.

Keywords: chemometrics, classification analysis, molecular descriptors, pesticides, regression analysis

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Authors: Lateef Hakim Zai Khyber, Syed Rashid Ali

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In the post 9/11 era, a new trend has developed of terrorist profiling on the basis of the ethnic, religious, political, psychological, social, and economic background of the terrorists to anticipate and assess the possible risk and to prevent and prosecute the suspected before they commit any violent act. The same profiling approach was adopted in different militant or terrorist de-radicalization and rehabilitation programs across the world in order to evaluate and identify the reasons and causes for joining terrorism in terms of push and pull factors. This paper attempts to explore and investigate the profiles of the detainees in the Sabaoon de-radicalization and Emancipation program, which aimed at de-radicalizing the former militants of Tehrik-e-Taliban (TTP) Pakistan in the Swat valley of Pakistan. This research attempted to use qualitative methods for collecting data, including a number of formal and informal open-ended interviews with the former staff members of Sabaoon to explore various aspects of the program, such as various approaches used at Sabaoon for terrorist profiling. It conducts a thorough examination of the profiles of the terrorist through their socioeconomic, ideological, emotional, intellectual, and psychological conditions and orientations, personal details, family issues, social preferences, etc. The study finds out that the majority of the terrorists belonged to the marginalized groups or lower class, including underprivileged tenants and poor laborers, of society having no access to land. They possess almost the same profiles, including low socioeconomic status, absence of a father or strict behavior of parents, large and combined families, lack of education, lack of religious understanding, etc. They also possess some common traits such as anxiety disorder, emotional instability, aggressive impulses and insecurity, depression, inferiority complex, lack of critical thinking and logical reasoning, authority-seeking behavior, and revenge-seeking behavior.

Keywords: terrorist profiling, Sabaoon, de-radicalization, rehabilitation, Swat, Pakistan, juvenile militants

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Authors: Fariha Salman, Helmet Steinberg, Hiba Al-Zubeidi

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OBJECTIVE: Polycystic ovary syndrome (PCOS) is the most common cause of infertility, with a prevalence of 5-10 % in women of reproductive age. There is evidence for differences between adult and pediatric endocrinologists and other specialists in their approach to the diagnosis and management of PCOS. METHODS: A survey consisting of 37 questions was distributed among adult and pediatric endocrinologists, aiming to understand the current practice for the diagnosis and management of PCOS. A total of 100 responses were available for final analysis; 36 % from adult endocrinologists (AE) and 64 % from pediatric endocrinologists(PE). RESULTS: The majority (64%) of respondents to the survey were endocrinologists from a multispecialty group. For both adults and adolescents with PCOS, the most commonly reported presenting symptoms were menstrual irregularities, obesity and hirsutism. The most common features used for diagnostic criteria were clinical or biochemical hyperandrogenism and ovulatory dysfunction. Most AE and PE screened for PCOS with total testosterone (83%) and free Testosterone (71%), screening for prolactin excess in 70 % and congenital adrenal hyperplasia (83 %), 66 % of AE will obtain pelvic US for evaluation vs 45 % of PE. Only 20 % of all respondents will obtain a midluteal progesterone for documentation of anovulation. In terms of treatment of hyperandrogenism and menstrual irregularities in adolescents, the most common form used is oral contraceptive pills, followed by metformin, then spironolactone. A similar approach was used in adults however the use of spironolactone was higher, 53 % vs 21 % in adolescents. The most common modality used for infertility was lifestyle interventions followed by metformin and clomiphene citrate. Screening of OSA and depression was not done by most of the endocrinologists (never + sometimes), 72 % and 76 %, respectively. Though screening for diabetes/metabolic syndrome and insulin resistance was done by most of the endocrinologists (always+often), 95 and 68 %, respectively. DISCUSSION: There are multiple diagnostic criteria used for PCOS diagnosis, however, given the wide variation in presentation and approach to diagnosis in adults and adolescents, there has not been a consensus on which is the gold standard criteria. CONCLUSION: Our survey showed the most common trends in diagnosing and treating PCOS among adult and pediatric endocrinologists. Further studies and trials need to be conducted to compare different treatment modalities used for hyperandrogenism, menstrual irregularities and infertility, as PCOS, if not treated earlier, can lead to long-term complications.

Keywords: PCOS, adolescents, diagnosis, treatment

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Authors: Akbar Gharbali, Ali Abbasian Ardekani, Afshin Mohammadi

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Introduction: Thyroid nodules have an incidence of 33-68% in the general population. More than 5-15% of these nodules are malignant. Early detection and treatment of thyroid nodules increase the cure rate and provide optimal treatment. Between the medical imaging methods, Ultrasound is the chosen imaging technique for assessment of thyroid nodules. The confirming of the diagnosis usually demands repeated fine-needle aspiration biopsy (FNAB). So, current management has morbidity and non-zero mortality. Objective: To explore diagnostic potential of automatic texture analysis (TA) methods in differentiation benign and malignant thyroid nodules by ultrasound imaging in order to help for reliable diagnosis and monitoring of the thyroid nodules in their early stages with no need biopsy. Material and Methods: The thyroid US image database consists of 70 patients (26 benign and 44 malignant) which were reported by Radiologist and proven by the biopsy. Two slices per patient were loaded in Mazda Software version 4.6 for automatic texture analysis. Regions of interests (ROIs) were defined within the abnormal part of the thyroid nodules ultrasound images. Gray levels within an ROI normalized according to three normalization schemes: N1: default or original gray levels, N2: +/- 3 Sigma or dynamic intensity limited to µ+/- 3σ, and N3: present intensity limited to 1% - 99%. Up to 270 multiscale texture features parameters per ROIs per each normalization schemes were computed from well-known statistical methods employed in Mazda software. From the statistical point of view, all calculated texture features parameters are not useful for texture analysis. So, the features based on maximum Fisher coefficient and the minimum probability of classification error and average correlation coefficients (POE+ACC) eliminated to 10 best and most effective features per normalization schemes. We analyze this feature under two standardization states (standard (S) and non-standard (NS)) with Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA). The 1NN classifier was performed to distinguish between benign and malignant tumors. The confusion matrix and Receiver operating characteristic (ROC) curve analysis were used for the formulation of more reliable criteria of the performance of employed texture analysis methods. Results: The results demonstrated the influence of the normalization schemes and reduction methods on the effectiveness of the obtained features as a descriptor on discrimination power and classification results. The selected subset features under 1%-99% normalization, POE+ACC reduction and NDA texture analysis yielded a high discrimination performance with the area under the ROC curve (Az) of 0.9722, in distinguishing Benign from Malignant Thyroid Nodules which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Conclusions: Our results indicate computer-aided diagnosis is a reliable method, and can provide useful information to help radiologists in the detection and classification of benign and malignant thyroid nodules.

Keywords: ultrasound imaging, thyroid nodules, computer aided diagnosis, texture analysis, PCA, LDA, NDA

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Authors: Lorenzo Gontrani, Marilena Carbone, Domenica Tommasa Donia, Elvira Maria Bauer, Pietro Tagliatesta

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One of the fields where DES shows remarkable added values is the synthesis Of inorganic materials, in particular nanoparticles. In this field, the higher- ent and highly-tunable nano-homogeneities of DES structure give origin to a marked templating effect, a precious role that has led to the recent bloom of a vast number of studies exploiting these new synthesis media to prepare Nanomaterials and composite structures of various kinds. In this contribution, the most recent developments in the field will be reviewed, and some ex-citing examples of novel metal oxide nanoparticles syntheses using non-toxic type-IV Deep Eutectic Solvents will be described. The prepared materials possess nanometric dimensions and show flower-like shapes. The use of the pre- pared nanoparticles as fluorescent materials for the detection of various contaminants is under development.

Keywords: metal deep eutectic solvents, nanoparticles, inorganic synthesis, type IV DES, lamellar

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Authors: Krzysztof Pomorski

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In the given work we present universal mathematical framework for modeling of cattle farm system that can set and validate various hypothesis that can be tested against experimental data. The presented work is preliminary but it is expected to be valid tool for future deeper analysis that can result in new class of prediction methods allowing early detection of cow dieseaes as well as cow performance. Therefore the presented work shall have its meaning in agriculture models and in machine learning as well. It also opens the possibilities for incorporation of certain class of biological models necessary in modeling of cow behavior and farm performance that might include the impact of environment on the farm system. Particular attention is paid to the model of coupled oscillators that it the basic building hypothesis that can construct the model showing certain periodic or quasiperiodic behavior.

Keywords: coupled ordinary differential equations, cattle farm system, numerical methods, stochastic differential equations

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Authors: Mulligan T., Barker-Collo S., Gobson K., Jones K.

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There is a relatively scarce body of literature regarding adolescent experiences of traumatic brain injury (TBI). This qualitative study explored how sustaining a TBI at this unique stage of development might impact a young person as they navigate the challenges of adolescence and transition to adulthood, and what might support recovery. Thirteen young adults who sustained a mild-moderate TBI as an adolescent (aged 13 – 17 years), approximately 7.7 years (range = 6.7 – 8.0 years) prior, participated in the research. Semi-structured individual interviews were conducted to explore participants’ experiences surrounding and following their TBIs. Thematic analysis of interview data produced five key categories of findings: (1) Following their TBIs, many participants experienced problems with cognitive (e.g., forgetfulness, concentration difficulties), physical (e.g., migraines, fatigue) and emotional (e.g., depression, anxiety) functioning, which were often endured into adulthood. (2) TBI-related problems often adversely affected important areas of life for the participant, including school, work and friendships. (3) Changes following TBI commonly impacted identity formation. (4) Recovery processes evolved over time as the participants coped initially by just ‘getting on with it’, before learning to accept new limitations and, ultimately, growing from their TBI experiences. (5) While the presence of friends and family assisted recovery, struggles were often exacerbated by a lack of emotional support from others, in addition to the absence of any assistance or information-provision from professionals regarding what to expect following TBI. The findings suggest that even mild TBI sustained during adolescence can have consequences for an individual’s functioning, engagement in life and identity development, whilst also giving rise to post-traumatic growth. Recovery following adolescent TBI might be maximised by facilitating greater understanding of the injury and acknowledging its impacts on important areas of life, as well as the provision of emotional support and facilitating self-reflection and meaning-making.

Keywords: adolescent, brain Injury, qualitative, post-traumatic growth

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Authors: Sajidullah Khan, Najeeb Ullah, Wang Yin Chai, Chai Soo See

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In this paper, we propose an approach to remove impulse and Poisson noise from X-ray images. Many filters have been used for impulse noise removal from color and gray scale images with their own strengths and weaknesses but X-ray images contain Poisson noise and unfortunately there is no intelligent filter which can detect impulse and Poisson noise from X-ray images. Our proposed filter uses the upgraded layer discrimination approach to detect both Impulse and Poisson noise corrupted pixels in X-ray images and then restores only those detected pixels with a simple efficient and reliable one line equation. Our Proposed algorithms are very effective and much more efficient than all existing filters used only for Impulse noise removal. The proposed method uses a new powerful and efficient noise detection method to determine whether the pixel under observation is corrupted or noise free. Results from computer simulations are used to demonstrate pleasing performance of our proposed method.

Keywords: X-ray image de-noising, impulse noise, poisson noise, PRWF

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Authors: Launcelot McGrath

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The Coronavirus disease 2019 (COVID-19) has spread rapidly around the world, resulting in high mortality rates and very large numbers of people requiring medical treatment in ICU. Management of patient hospitalisation is a critical aspect to control this disease and reduce chaos in the healthcare systems. Remote monitoring provides a solution to protect vulnerable and elderly high-risk patients. Continuous remote monitoring of oxygen saturation, respiratory rate, heart rate, and temperature, etc., provides medical systems with up-to-the-minute information about their patients' statuses. Remote monitoring also limits the spread of infection by reducing hospital overcrowding. This paper examines the potential of remote monitoring for Covid-19 to assist in the rapid identification of patients at risk, facilitate the detection of patient deterioration, and enable early interventions.

Keywords: remote monitoring, patient care, oxygen saturation, Covid-19, hospital management

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Authors: Caroline Dupre, David Hupin, Christ Goumou, Francois Belan, Frederic Roche, Thomas Celarier, Bienvenu Bongue

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Background: Previous cohorts have been notably criticized for not studying the different type of physical activity and not investigating household activities. The objective of this work was to analyse the relationship between physical activity and cognitive decline in older people living in the community. Impact of type of physical activity on the results has been realised. Methods: The study used data from the longitudinal and observational study , FrèLE (FRagility: Longitudinal Study of Expressions). The collected data included: socio-demographic variables, lifestyle, and health status (frailty, comorbidities, cognitive status, depression). Cognitive decline was assessed by using: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Physical activity was assessed by the Physical Activity Scale for the Elderly (PASE). This tool is structured in three sections: the leisure activity, domestic activity, and professional activity. Logistic regressions and proportional hazards regression models (Cox) were used to estimate the risk of cognitive disorders. Results: At baseline, the prevalence of cognitive disorders was 6.9% according to MMSE. In total, 1167 participants without cognitive disorders were included in the analysis. The mean age was 77.4 years, and 52.1% of the participants were women. After a 2 years long follow-up, we found cognitive disorders on 53 participants (4.5%). Physical activity at baseline is lower in older adults for whom cognitive decline was observed after two years of follow-up. Subclass analyses showed that leisure and domestic activities were associated with cognitive decline, but not professional activities. Conclusions: Analysis showed a relationship between cognitive disorders and type of physical activity. The current study will be completed by the MoCA for mild cognitive impairment. These findings compared to other ongoing studies, will contribute to the debate on the beneficial effects of physical activity on cognition.

Keywords: aging, cognitive function, physical activity, mixed models

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Authors: Jolanta Jonak

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Emotional wellbeing is crucial for healthy development of children, youth, and adolescence. Lack of healthy emotional development and functioning can lead to many challenges in academic, social, and physical domains. Unfortunately, mental health problems are steadily increasing over the past several years and significantly intensified during and after the Covid 19 pandemic. Some experts warn that globally, we are entering a mental health crisis pandemic. Prior to the Covid 19 pandemic in 2020, a considerable number of youth aged 6-17 experienced a mental health disorder each year, with half of all mental health conditions beginning by age 14. The most common mental health disorders in children are Attention-Deficit/Hyperactivity Disorder (ADHD), behavior problems, anxiety, and depression. In U.S. schools, youth with these diagnoses tend to be classified under the umbrella of Emotional Disability. Several concerning trends have been emerging about the mental health of U.S. high school students. These trends have been steadily increasing for the past decade, particularly having increasing numbers of high school students who experienced persistent feelings of sadness or hopelessness, seriously considered attempting suicide, made a suicide plan, and attempted suicide. The number of students who felt persistently sad or hopeless increased across every racial and ethnic group. Parents, educators, and mental health providers are key stakeholders that are responsible for actively and effectively supporting struggling youth. A commonly faced challenge is that mental wellbeing is not supported as effectively as it should be. When effective prevention is paired with enhanced treatment developed on a multidisciplinary approach, mental health and emotional wellbeing in young people can be positively enhanced. Major health organizations including the Center for Disease Control and the National Institutes of Health encourage health professionals and other stakeholders to focus on addressing emotional wellbeing and mental health of young people.

Keywords: social emotional wellbeing, disability, learning, prevention

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Authors: Mosab Alrashed, Wei Xu, Stephen Abineri, Yifan Zhao, Jörn Mehnen

Abstract:

Thermographic inspection is a relatively new technique for Non-Destructive Testing (NDT) which has been gathering increasing interest due to its relatively low cost hardware and extremely fast data acquisition properties. This technique is especially promising in the area of rapid automated damage detection and quantification. In collaboration with a major industry partner from the aerospace sector advanced thermography-based NDT software for impact damaged composites is introduced. The software is based on correlation analysis of time-temperature profiles in combination with an image enhancement process. The prototype software is aiming to a) better visualise the damages in a relatively easy-to-use way and b) automatically and quantitatively measure the properties of the degradation. Knowing that degradation properties play an important role in the identification of degradation types, tests and results on specimens which were artificially damaged have been performed and analyzed.

Keywords: NDT, correlation analysis, image processing, damage, inspection

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Authors: Seyed Mahdi Jameii

Abstract:

Underwater wireless sensor networks have been attracting the interest of many ‎researchers lately, and the past three decades have beheld the rapid progress of ‎underwater acoustic communication. One of the major problems in underwater wireless ‎sensor networks is how to transfer data from the moving node to the base stations and ‎choose the optimized route for data transmission. Secure routing in underwater ‎wireless sensor network (UWCNs) is necessary for packet delivery. Some routing ‎protocols are proposed for underwater wireless sensor networks. However, a few ‎researches have been done on secure routing in underwater sensor networks. In this ‎article, a secure routing protocol is provided to resist against wormhole and sybil ‎attacks. The results indicated acceptable performance in terms of increasing the packet ‎delivery ratio with regards to the attacks, increasing network lifetime by creating ‎balance in the network energy consumption, high detection rates against the attacks, ‎and low-end to end delay.‎

Keywords: attacks, routing, security, underwater wireless sensor networks

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Authors: Pocholo Rodriguez, Anne Bernadine Ocampo, Ian Benedict Chan, Janric Micah Gray

Abstract:

The state of an electric power system may be classified as either stable or unstable. The borderline of stability is at any condition for which a slight change in an unfavourable direction of any pertinent quantity will cause instability. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. The researchers will present an intelligent system using back propagation algorithm that can detect voltage instability and output voltage of a power distribution and classify it as stable or unstable. The researchers’ work is the use of parameters involved in voltage instability as input parameters to the neural network for training and testing purposes that can provide faster detection and monitoring of the power distribution system.

Keywords: back-propagation algorithm, load instability, neural network, power distribution system

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

Abstract:

Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Koray U. Erbas

Abstract:

Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Sheng-Shih Wang, Wei-Ting Wang

Abstract:

This paper designs an intelligent guidance system, based on wireless sensor networks, for efficient parking in parking lots. The proposed system consists of a parking space allocation subsystem, a parking space monitoring subsystem, a driving guidance subsystem, and a vehicle detection subsystem. In the system, we propose a novel and effective virtual coordinate system for sensing and displaying devices to determine the proper vacant parking space and provide the precise guidance to the driver. This study constructs a ZigBee-based wireless sensor network on Arduino platform and implements the prototype of the proposed system using Arduino-based complements. Experimental results confirm that the proposed prototype can not only work well, but also provide drivers the correct parking information.

Keywords: Arduino, parking guidance, wireless sensor network, ZigBee

Procedia PDF Downloads 579