Search results for: early Alzheimer’s recognition

Authors: Shih-Heng Sun, Hsiu-Yu Chang

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“Family centered service model” becomes mainstream in early intervention. Family outcome should be evaluated in addition child improvement in terms of outcome evaluation in early intervention. The purpose of this study is to develop a surveys to evaluate family outcomes in early intervention. Method: “Family Outcomes Survey- Revised Taiwan Version” (FOS-RT) was developed through translation, back-translation, and review by the original author. Expert meeting was held to determine the content validity. Two hundred and eighty six parent-child dyads recruited from 10 local Early Intervention Resource Centers (EIRC) participated in the study after they signed inform consent. The results showed both parts of FOS-RT exhibits good internal consistency and test-retest reliability. The result of confirmatory factor analysis indicated moderate fit of 5 factor structure of part A and 3 factor structure of part B of FOS-RT. The correlation between different sessions reached moderate to high level reveals some sessions measure similar latent trait of family outcomes. Correlation between FOS-RT and Parents‘ Perceived Parenting Skills Questionnaire was calculated to determine the convergence validity. The moderate correlation indicates the two assessments measure different parts of early intervention outcome although both assessments have similar sub-scales. The results of this study support FOS-RT is a valid and reliable tool to evaluate family outcome after the family and children with developmental disability receive early intervention services.

Keywords: early intervention, family service, outcome evaluation, parenting skills, family centered

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Authors: El Sayed A. Sharara, A. Tsuji, K. Terada

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Call centers have been expanding and they have influence on activation in various markets increasingly. A call center’s work is known as one of the most demanding and stressful jobs. In this paper, we propose the fatigue detection system in order to detect burnout of call center agents in the case of a neck pain and upper back pain. Our proposed system is based on the computer vision technique combined skin color detection with the Viola-Jones object detector. To recognize the gesture of hand poses caused by stress sign, the YCbCr color space is used to detect the skin color region including face and hand poses around the area related to neck ache and upper back pain. A cascade of clarifiers by Viola-Jones is used for face recognition to extract from the skin color region. The detection of hand poses is given by the evaluation of neck pain and upper back pain by using skin color detection and face recognition method. The system performance is evaluated using two groups of dataset created in the laboratory to simulate call center environment. Our call center agent burnout detection system has been implemented by using a web camera and has been processed by MATLAB. From the experimental results, our system achieved 96.3% for upper back pain detection and 94.2% for neck pain detection.

Keywords: call center agents, fatigue, skin color detection, face recognition

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Authors: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: Chiou-Shiue Ko, Pei-Fang Wu, Shu-hsien Tseng

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The researchers adopted two-year action research to investigate the professional collaborative process and development in learning communities for both early childhood and early childhood special education teachers on implementing the children’s school success curriculum framework. The participating teachers were recruited from three preschool sites for this current study. Research data were collected from multiple methods in order to ensure the data quality and validity. The results showed that participating educators had achieved professional growth, and they became more aware of teaching intentions and the preparation for the curriculum. Teachers in this research become more child-focused in teaching and create opportunities for children to participate in classroom activities and routines. The researcher also finds teachers’ participation levels were driven by each individual personality; during professional growth, some teachers are more proactive and reflective, and some are not. According to the research findings, suggestions for future studies and practices are provided.

Keywords: children’s school success curriculum framework, early childhood special education, preschool education, professional learning community

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Authors: Mohhamed El Habib Labdouni

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Introduction: Co-infection with VIH and tuberculosis poses one of the major ongoing challenges for global TB and AIDS prevention and control. The objective of this study is to raise the issue of the resurgence of TB, in People living with VIH supported in a referent center in western Algeria. Its epidemiological, clinical, biological and radiological new trends, and to compare the mortality rate between early and delayed ART. Methods: It was a prospective study, during 36 months from the 01st/01/2012 to 31st/12/2014, by identifying and analyzing cases of TB-VIH co-infection. Our population was devised in two groups/ early ART and delayed ART. The primary and secondary endpoints were analyzed with Kaplan-Meier curves and log-rank test the period of follow up, which was fixed at 300 weeks. Results: Sixty cases of co-infection TB -VIH were enrolled in our study: 78.3% had pulmonary tuberculosis associated with extra-pulmonary, 13.3% had only pulmonary tuberculosis and 08.3% presented strictly extra-pulmonary TB. The clinical particularity of this co-infection is the frequency of serious localization such us: pleural 23.3%, peritoneal 31.7%, and meningeal suffusion 13.3%.y-.biologicaly we notice the predominance both of pancytopenia and leucoanemia, hyponatremia in 38,6% and hypokalemia in 19,3%. By analyzing Kaplan-Meier survival curves, we notice that early ART initiation is associated with a significant reduction of all-cause mortality (p = 0,000), and we have identified several prognostic factors such as hypokalemia hyponatremia, leukocytosis thrombopenemia leucothrombopenia (p = 0,005). Conclusion: Our study confirms most of the results reported in the literature. Early ART initiation reduces the rate of all-cause mortality, despite the probability of the occurrence of TB-IRIS.

Keywords: TB-HIV co-infection, early ART, hyponatremia, extrapulmonary tuberculosis

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Authors: Amin Pashaye Amiri

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Freedom of information, according to which the public has a right to have access to government-held information, is largely considered as a tool for improving transparency and accountability in governments, and as a requirement of self-governance and good governance. So far, more than ninety countries have recognized citizens’ right to have access to public information. This recognition often took place through the adoption of an act referred to as “freedom of information act”, “access to public records act”, and so on. A freedom of information act typically imposes a positive obligation on a government to initially and regularly release certain public information, and also obliges it to provide individuals with information they request. Such an act usually allows governmental bodies to withhold information only when it falls within a limited number of exemptions enumerated in the act such as exemptions for protecting privacy of individuals and protecting national security. Some steps have been taken at the national and international level towards the recognition of freedom of information as a human right. Freedom of information was recognized in a few countries as a part of freedom of expression, and therefore, as a human right. Freedom of information was also recognized by some international bodies as a human right. The Inter-American Court of Human Rights ruled in 2006 that Article 13 of the American Convention on Human Rights, which concerns the human right to freedom of expression, protects the right of all people to request access to government information. The European Court of Human Rights has recently taken a considerable step towards recognizing freedom of information as a human right. However, in spite of the measures that have been taken, public access to government information is not yet widely accepted as an international human right. The paper will consider the degree to which freedom of information has been recognized as a human right, and study the possibility of widespread recognition of such a human right in the future. It will also examine the possible benefits of such recognition for the development of the human right to free expression.

Keywords: freedom of information, freedom of expression, human rights, government information

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Authors: Dagmawit Tewahido

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Purpose: This qualitative study was conducted to explore social norms around adolescent girls’ marriage practices in West Hararghe, Ethiopia, where early marriage is prohibited by law. Methods: Twenty Focus Group Discussions were conducted with Married and Unmarried adolescent girls, adolescent boys and parents of girls using locally developed vignettes. A total of 32 in-depth interviews were conducted with married and unmarried adolescent girls, husbands of adolescent girls and mothers-in-law. Key informant interviews were conducted with 36 district officials. Data analysis was assisted by Open Code computer software. The Social Norms Analysis Plot (SNAP) framework developed by CARE guided the development and analysis of vignettes. A thematic data analysis approach was utilized to summarize the data. Results: Early marriage is seen as a positive phenomenon in our study context, and girls who are not married by the perceived ideal age of 15 are socially sanctioned. They are particularly influenced by their peers to marry. Marrying early is considered a chance given by God and a symbol of good luck. The two common types of marriage are decided: 1) by adolescent girl and boy themselves without seeking parental permission (’Jalaa-deemaa’- meaning ‘to go along’), and 2) by just informing girl’s parents (‘Cabsaa’- meaning ‘to break the culture’). Relatives and marriage brokers also arrange early marriages. Girls usually accept the first marriage proposal regardless of their age. Parents generally tend not to oppose marriage arrangements chosen by their daughters. Conclusions: In the study context social norms encourage early marriage despite the existence of a law prohibiting marriage before the age of eighteen years. Early marriage commonly happens through consensual arrangements between adolescent girls and boys. Interventions to reduce early marriage need to consider the influence of Reference Groups on the decision makers for marriages, especially girls’ own peers.

Keywords: adolescent girls, social norms, early marriage, Ethiopia

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Authors: Ichihan Tai, Bill Olson, Paul Blessner

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Traditional early warning systems that alarm against crisis are generally based on structured or numerical data; therefore, a system that can make predictions based on unstructured textual data, an uncorrelated data source, is a great complement to the traditional early warning systems. The Chicago Board Options Exchange (CBOE) Volatility Index (VIX), commonly referred to as the fear index, measures the cost of insurance against market crash, and spikes in the event of crisis. In this study, news data is consumed for prediction of whether there will be a market-wide crisis by predicting the movement of the fear index, and the historical references to similar events are presented in an unsupervised manner. Topic modeling-based prediction and representation are made based on daily news data between 1990 and 2015 from The Wall Street Journal against VIX index data from CBOE.

Keywords: early warning system, knowledge management, market prediction, topic modeling.

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Authors: Sang-Keun Moon, Hong-Rok Lim, Jin-O Kim

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This paper presents a Power Quality (PQ) modeling and filtering processes for the distribution system disturbances using recognition learning methods. Typical PQ waveforms with mathematical applications and gathered field data are applied to the proposed models. The objective of this paper is analyzing PQ data with respect to monitoring, discriminating, and evaluating the waveform of power disturbances to ensure the system preventative system failure protections and complex system problem estimations. Examined signal filtering techniques are used for the field waveform noises and feature extractions. Using extraction and learning classification techniques, the efficiency was verified for the recognition of the PQ disturbances with focusing on interactive modeling methods in this paper. The waveform of selected 8 disturbances is modeled with randomized parameters of IEEE 1159 PQ ranges. The range, parameters, and weights are updated regarding field waveform obtained. Along with voltages, currents have same process to obtain the waveform features as the voltage apart from some of ratings and filters. Changing loads are causing the distortion in the voltage waveform due to the drawing of the different patterns of current variation. In the conclusion, PQ disturbances in the voltage and current waveforms indicate different types of patterns of variations and disturbance, and a modified technique based on the symmetrical components in time domain was proposed in this paper for the PQ disturbances detection and then classification. Our method is based on the fact that obtained waveforms from suggested trigger conditions contain potential information for abnormality detections. The extracted features are sequentially applied to estimation and recognition learning modules for further studies.

Keywords: power quality recognition, PQ modeling, waveform feature extraction, disturbance trigger condition, PQ signal filtering

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Authors: Sanja Seljan, Ivan Dunđer

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The paper presents combined automatic speech recognition (ASR) for English and machine translation (MT) for English and Croatian in the domain of business correspondence. The first part presents results of training the ASR commercial system on two English data sets, enriched by error analysis. The second part presents results of machine translation performed by online tool Google Translate for English and Croatian and Croatian-English language pairs. Human evaluation in terms of usability is conducted and internal consistency calculated by Cronbach's alpha coefficient, enriched by error analysis. Automatic evaluation is performed by WER (Word Error Rate) and PER (Position-independent word Error Rate) metrics, followed by investigation of Pearson’s correlation with human evaluation.

Keywords: automatic machine translation, integrated language technologies, quality evaluation, speech recognition

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: Kusmayadi Ita Nursita, Sundoro Ali

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Cleft lip and palate are one of the most common congenital abnormalities in the face. It could happen to anyone, but mostly affect Asian population including Indonesia. Factors that influence the occurrence of cleft lip and palate vary from genetic to environmental factors. Children with cleft lip and palate will often have various problems such as airway disorders, eating disorders, speech and language developmental disorders, hearing disorders and psycho-social disorders, one of which is caused by appearance disorders. During his life, the child will experience multidisciplinary surgery and non-surgical treatment and can be accompanied by a psychological and financial burden on himself and his family. In Indonesia, there are no detailed scientific data on the prevalence and characteristic of cleft lip and palate patients. It was mainly caused by the absence of a national level organization, differences in geographical location, and the absence of national guidelines. This study aimed to describe the characteristic and prevalence of cleft lip and palate patients in Bandung Cleft Lip and Palate Center from 1 January 2016 to 31 December 2017. A total of 560 patients were included in the study. The highest percentage of cases are left unilateral cleft lip and palate with higher number of female patient and labioplasty as the most often surgical procedure to be conducted in Bandung Cleft Lip and Palate Center. In order to improve quality of life in patients with cleft lip and palate, early recognition and early treatment based on actual comprehensive data should be conducted. The data from Bandung Cleft Lip and Palate Center as one of the largest center of cleft lip and palate in West Java Indonesia hopefully could provide a big step of further comprehensive data collection in Indonesia and for the better overall management of cleft lip and palate in the future.

Keywords: cleft lip, cleft palate, characteristic, prevalence

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Authors: Tuiana A. Aiusheeva, Sergey S. Tamozhnikov, Alexander E. Saprygin, Arina A. Antonenko, Valentina V. Stepanova, Natalia N. Tolstykh, Alexander N. Savostyanov

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Situation of choice is an important condition for the formation of essential character qualities of a child, such as being initiative, responsible, hard-working. We have studied the behavioral and EEG reactions in Russian schoolchildren during recognition of syntactic errors in emotionally colored sentences that describe the choice situation. Twenty healthy children (mean age 9,0±0,3 years, 12 boys, 8 girls) were examined. Forty sentences were selected for the experiment; the half of them contained a syntactic error. The experiment additionally had the hidden condition: 50% of the sentences described the children's own choice and were emotionally colored (positive or negative). The other 50% of the sentences described the forced-choice situation, also with positive or negative coloring. EEG were recorded during execution of error-recognition task. Reaction time and quality of syntactic error detection were chosen as behavioral measures. Event-related spectral perturbation (ERSP) was applied to characterize the oscillatory brain activity of children. There were two time-frequency intervals in EEG reactions: (1) 500-800 ms in the 3-7 Hz frequency range (theta synchronization) and (2) 500-1000 ms in the 8-12 Hz range (alpha desynchronization). We found out that behavioral and brain reactions in child brain during recognition of positive and negative sentences describing forced-choice situation did not have significant differences. Theta synchronization and alpha desynchronization were stronger during recognition of sentences with children's own choice, especially with negative coloring. Also, the quality and execution time of the task were higher for this types of sentences. The results of our study will be useful for improvement of teaching methods and diagnostics of children affective disorders.

Keywords: choice situation, electroencephalogram (EEG), emotionally colored sentences, schoolchildren

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Authors: J. Aliyu, A. O. Raji, A. A. Ibrahim

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The study was carried out to assess the fertility, early and late embryonic mortalities as well as hatchability by strain, season and hen’s weight in Nigerian indigenous chickens reared on deep litter. Four strains (normal feathered, naked neck, frizzle and dwarf) of hens maintained at a mating ratio of 1 cock to 4 hens, fed breeders mash and water ad libitum were used in a three year experiment. The data generated were subjected to analysis of variance using the SAS package and the means, where significant, were separated using the least significant difference (LSD). There were significant effects (P < 0.05) of strain on all the traits studied. Fertility was generally high (84.29 %) in all the strains. Early embryonic mortality was significantly lowest (P < 0.01) in naked neck which had the highest late embryonic mortality (P < 0.001). Hatchability was significantly highest (P < 0.01) in normal feathered (80.23 %) and slightly depressed in frizzle (74.95 %) and dwarf (72.27 %) while naked neck had the lowest (60.80 %). Season of the year had significant effects on early embryonic mortality. Dry hot season significantly (P < 0.05) depressed fertility while early embryonic mortality was depressed in the wet season (15.33 %). Early and late embryonic mortalities significantly increased (P < 0.05) with increasing weight of hen. Dwarf, frizzle and normal feathered hens could be used to improve hatchability as well as reduce early and late embryonic mortalities in Nigerian indigenous chickens.

Keywords: chicken, fertility, hatchability, indigenous, strain

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Authors: Yalong Jiang, Zheru Chi

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In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, convolutional neural network, capsule network, capacity optimization, character recognition, data augmentation, semantic segmentation

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Authors: Darma Putra Sitepu, Dewi Larasati, Yohanes Wolter Hendrik George

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Sepsis is a life-threatening medical emergency frequently observed in intensive care unit (ICU). Surviving Sepsis Campaign in 2018 has recommended the administration of early vasopressor in the first hour of sepsis or septic shock but has not yet included de-resuscitation protocol. De-resuscitation in acute management of septic shock is where patient received active removal of accumulated fluid. It has been proposed by some studies and ongoing clinical trials. Here we present a case with early vasopressor and de-resuscitation. Male, 27 years old presenting to the emergency room with shortness of breath, altered mental status, and widespread blisters on his body and lips started a few hours prior, after receiving non-steroidal anti-inflammatory drug through intravenous injection. Patient was hypotensive, tachycardic, and tachypneic at admission, diagnosed with Steven Johnson Syndrome with Septic Shock. Patient received fluid resuscitation, early vasopressor, and diuresis agent aimed to actively remove fluid after the initial phase of resuscitation. Patient was admitted to ICU and progressively recovering. At day-10, patient was stabilized and was transferred to general ward. Early vasopressor and de-resuscitation are beneficial for the patient.

Keywords: sepsis, shock, de-resuscitation, vasopressor, fluid, case report

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Authors: Xiaoxiao Li, Shuangcheng Jia, Qian Li

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Keypoint detection has always been a challenge in the field of image recognition. This paper proposes a novelty keypoint detection method which is called Multi-Scale Feature Fusion Convolutional Network with Attention (MFFCNA). We verified that the multi-scale features with the attention mechanism module have better feature expression capability. The feature fusion between different scales makes the information that the network model can express more abundant, and the network is easier to converge. On our self-made street sign corner dataset, we validate the MFFCNA model with an accuracy of 97.8% and a recall of 81%, which are 5 and 8 percentage points higher than the HRNet network, respectively. On the COCO dataset, the AP is 71.9%, and the AR is 75.3%, which are 3 points and 2 points higher than HRNet, respectively. Extensive experiments show that our method has a remarkable improvement in the keypoint recognition tasks, and the recognition effect is better than the existing methods. Moreover, our method can be applied not only to keypoint detection but also to image classification and semantic segmentation with good generality.

Keywords: keypoint detection, feature fusion, attention, semantic segmentation

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

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The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection

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Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

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In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: brain-computer interface, speech recognition, artificial neural network, electroencephalography, EEG, wernicke area

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Authors: Youhang Zhou, Weimin Zeng, Qi Xie

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Abstract: In order to solve the wear recognition problem of the machine tool guide surface, a new machine tool guide surface recognition method based on the radar-graph barycentre feature is presented in this paper. Firstly, the gray mean value, skewness, projection variance, flat degrees and kurtosis features of the guide surface image data are defined as primary characteristics. Secondly, data Visualization technology based on radar graph is used. The visual barycentre graphical feature is demonstrated based on the radar plot of multi-dimensional data. Thirdly, a classifier based on the support vector machine technology is used, the radar-graph barycentre feature and wear original feature are put into the classifier separately for classification and comparative analysis of classification and experiment results. The calculation and experimental results show that the method based on the radar-graph barycentre feature can detect the guide surface effectively.

Keywords: guide surface, wear defects, feature extraction, data visualization

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Authors: Ria Ghosh, Soumendra Singh, Dipanjan Mukherjee, Susmita Mondal, Monojit Das, Uttam Pal, Aniruddha Adhikari, Aman Bhushan, Surajit Bose, Siddharth Sankar Bhattacharyya, Debasish Pal, Tanusri Saha-Dasgupta, Maitree Bhattacharyya, Debasis Bhattacharyya, Asim Kumar Mallick, Ranjan Das, Samir Kumar Pal

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Drug delivery to a target without adverse effects is one of the major criteria for clinical use. Herein, we have made an attempt to explore the delivery efficacy of SDS surfactant in a monomer and micellar stage during the delivery of the model drug, Toluidine Blue (TB) from the micellar cavity to DNA. Molecular recognition of pre-micellar SDS encapsulated TB with DNA occurs at a rate constant of k1 ~652 s 1. However, no significant release of encapsulated TB at micellar concentration was observed within the experimental time frame. This originated from the higher binding affinity of TB towards the nano-cavity of SDS at micellar concentration which does not allow the delivery of TB from the nano-cavity of SDS micelles to DNA. Thus, molecular recognition controls the extent of DNA recognition by TB which in turn modulates the rate of delivery of TB from SDS in a concentration-dependent manner.

Keywords: DNA, drug delivery, micelle, pre-micelle, SDS, toluidine blue

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Authors: Uswah Khairuddin, Rubiyah Yusof, Nenny Ruthfalydia Rosli

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This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy.

Keywords: feature selection, genetic algorithm, optimization, wood recognition system

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Authors: A. Shoiynbek, K. Kozhakhmet, P. Menezes, D. Kuanyshbay, D. Bayazitov

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Speech emotion recognition has received increasing research interest all through current years. There was used emotional speech that was collected under controlled conditions in most research work. Actors imitating and artificially producing emotions in front of a microphone noted those records. There are four issues related to that approach, namely, (1) emotions are not natural, and it means that machines are learning to recognize fake emotions. (2) Emotions are very limited by quantity and poor in their variety of speaking. (3) There is language dependency on SER. (4) Consequently, each time when researchers want to start work with SER, they need to find a good emotional database on their language. In this paper, we propose the approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describe the sequence of actions of the proposed approach. One of the first objectives of the sequence of actions is a speech detection issue. The paper gives a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian languages. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To illustrate the working capacity of the developed model, we have performed an analysis of speech detection and extraction from real tasks.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Vladimir Ryabov, Mikhail Baryshevs, Mikhail Voskresenskey, Boris Popov

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The human prostate gland (PG) samples were obtained from patients who had undergone radical prostatectomy for prostate cancer (PC) and used to extract total RNA and prepare the prostate stromal cell cultures (PSCC) and patients-derived organoids (PDO). Growth of the cell cultures was accessed under microscopic evaluation in transmitted light and the marker expression by reverse polymerase chain reaction (RT-PCR), immunofluorescence, and immunoblotting. Some PCR products from prostate tissue, PSCC, and PDO were cloned and sequenced. We found that the cells of early and late passages of PSCC and corresponding PDO expressed luminal (androgen receptor, AR; cytokeratin 18, CK18) and basal (CK5, p63) epithelial markers, the production of which decreased or disappeared in late PSCC and PDO. The PSCC and PDO of early passages from cancer tissue additionally produced cancer markers AMACR, TMPRSS2-ERG, and Ezh2. The expression of TMPRSS2-ERG fusion transcripts was verified by cloning and sequencing the PCR products. The results obtained suggest that early passages of PSCC might be used as a pre-clinical model for the evaluation of early markers of prostate cancer.

Keywords: localized prostate cancer, prostate epithelial markers, prostate cancer markers, AMACR, TMPRSS2-ERG, prostate stromal cell cultures, PDO

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

Abstract:

Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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Authors: Bounoua Nadia, Rebizi Mohamed Nadjib

Abstract:

Chiral molecules in relation to particular biological roles are stereoselective. Enantiomers differ significantly in their biochemical responses in a biological environment. Despite the current advancement in drug discovery and pharmaceutical biotechnology, the chiral separation of some racemic mixtures continues to be one of the greatest challenges because the available techniques are too costly and time-consuming for the assessment of therapeutic drugs in the early stages of development worldwide. Various nanoparticles became one of the most investigated and explored nanotechnology-derived nanostructures, especially in chirality, where several studies are reported to improve the enantiomeric separation of different racemic mixtures. The production of surface-modified nanoparticles has contributed to these limitations in terms of sensitivity, accuracy, and enantioselectivity that can be optimized and therefore makes these surface-modified nanoparticles convenient for enantiomeric identification and separation.

Keywords: chirality, enantiomeric recognition, selectors, analysis, surface-modified nanoparticles

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Authors: Ya-Chuan Hsu, Wen-Chen Ouyang, Wei-Siang Huang

Abstract:

Problem/Background: With constantly increasing aged populations, quality of life (QOL) in persons with dementia has become a significant research concern. The Alzheimer’s Related Quality of Life (ADRQL) is a high-validated, theory-derived, and multidimensional instrument. It has widely utilized in many countries, except in Taiwan. However, diverse results of quality of life from different countries by using the same measurement can provide the potential to help understand the impact of cultural contributor on QOL. Objective: To investigate the extent to which quality of life on older adults with dementia in Taiwan. Methods: Cross-sectional, descriptive study conducted in an assisting living facility affiliated with a daycare center in southern Taiwan. A purposeful sample of 34 participants was recruited. Inclusion criteria included those who were at least 65 years old, able to communicate, and diagnosed with mild to moderate dementia. The QOL was measured by Chinese version ADRQL. This observational instrument consists of 30 items that is divided into five subscales with the full range of each subscale scores from 0 to 100.0. Higher scores indicate better QOL. Results: The means for subscale of the Social Interaction, Awareness of Self, Feelings and Mood, Enjoyment of Activities, and Response to Surroundings were 87.9, 74.7, 91.3, 64.5, and 90.3, respectively. The overall mean for the ADQOL was 0.83. Conclusion: Findings suggest that the level of Enjoyment of Activities is the lowest and may convey information about a need of evaluation on arrangement of facility’s activities.

Keywords: dementia, quality of life, elders, Alzheimer’s related quality of life

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Authors: Sean W. T. Bayly, Daniel Glover, Don Horrell, Simon Horrocks, Barnes Callum, Stuart Gibson, Mac Misuira

Abstract:

We assess suitability of recent, ∼7B parameter, instruction-tuned Language Models Mistral-v0.3, Llama-3, and Phi-3, for Generative Named Entity Recognition (GNER). Our proposed Multi-Level Information Retrieval method achieves notable improvements over finetuned entity-level and sentence-level methods. We consider recent developments at the cross roads of prompt engineering and Retrieval Augmented Generation (RAG), such as EmotionPrompt. We conclude that language models directed toward this task are highly capable when distinguishing between positive classes (precision). However, smaller models seem to struggle to find all entities (recall). Poorly defined classes such as ”Miscellaneous” exhibit substantial declines in performance, likely due to the ambiguity it introduces to the prompt. This is partially resolved through a self verification method using engineered prompts containing knowledge of the stricter class definitions, particularly in areas where their boundaries are in danger of overlapping, such as the conflation between the location ”Britain” and the nationality ”British”. Finally, we explore correlations between model performance on the GNER task with performance on relevant academic benchmarks.

Keywords: generative named entity recognition, information retrieval, lightweight artificial intelligence, prompt engineering, personal information identification, retrieval augmented generation, self verification

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Authors: Sadeem A. Alolayan

Abstract:

The purpose of early intervention (EI) programs and services is to minimize the impact of disability on children ages 0-5 and to reduce future special education costs. This literature review identifies the status of families of children with special needs. Four major themes emerged from this literature review. The first was the family’s needs and the expressed desire for services to be obtained or outcomes to be achieved. The second was family support, meaning any information or skills needed to facilitate parents’ role as professionals in order to enable them to train and provide their child with the best quality of life. The third theme, barriers, was defined as parents’ actions or life circumstances that hindered families in obtaining appropriate EI services. The conclusions derived from the recommendations are that effective parent participation involves careful planning, establishing and maintaining a trusted rapport between parents, and EI providers that understand parents’ individual needs and interests, thus motivating effective parent involvement in early intervention programs.

Keywords: early intervention, individuals with disabilities education act, parents, recommendations

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Authors: Kenneth Larsen, Astrid Aasland, Synnve Schjølberg, Trond Diseth

Abstract:

Autism Spectrum Disorders (ASD) are neurodevelopmental disorders emerging in early development characterized by impairment in social communication skills and a restricted, repetitive and stereotyped patterns of behavior and interests. Early identification and interventions potentially improve development and quality of life of children with ASD. Symptoms of ASD are apparent through the second year of life, yet diagnostic age are still around 4 years of age. This study explored whether symptoms associated with ASD are possible to identify in typical Norwegian day-care centers in the second year of life. Results of this study clearly indicates that most described symptoms also are identifiable by day-care staff, and that a short observation list of 5 symptoms clearly identify children with ASD from a sample of normal developing peers.

Keywords: autism, early identification, day-care, screening

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