Search results for: feature detection and description

Authors: Margot Whitfield, Andrea Ducent, Marie Catherine Rombaut, Katia Iassinovskaia, Deborah Fels

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Learning how to create inclusive media, such as closed captioning (CC) and audio description (AD), in North America is restricted to the private sector, proprietary company-based training. We are delivering (through synchronous and asynchronous online learning) the first Canadian post-secondary, practice-based continuing education course package in inclusive media for broadcast production and processes. Despite the prevalence of CC and AD taught within the field of translation studies in Europe, North America has no comparable field of study. This novel approach to audio visual translation (AVT) education develops evidence-based methodology innovations, stemming from user study research with blind/low vision and Deaf/hard of hearing audiences for television and theatre, undertaken at Ryerson University. Knowledge outcomes from the courses include a) Understanding how CC/AD fit within disability/regulatory frameworks in Canada. b) Knowledge of how CC/AD could be employed in the initial stages of production development within broadcasting. c) Writing and/or speaking techniques designed for media. d) Hands-on practice in captioning re-speaking techniques and open source technologies, or in AD techniques. e) Understanding of audio production technologies and editing techniques. The case study of the curriculum development and deployment, involving first-time online course delivery from academic and practitioner-based instructors in introductory Captioning and Audio Description courses (CDIM 101 and 102), will compare two different instructors' approaches to learning design, including the ratio of synchronous and asynchronous classroom time and technological engagement tools on meeting software platform such as breakout rooms and polling. Student reception of these two different approaches will be analysed using qualitative thematic and quantitative survey analysis. Thus far, anecdotal conversations with students suggests that they prefer synchronous compared with asynchronous learning within our hands-on online course delivery method.

Keywords: inclusive media theory, broadcasting practices, AVT post secondary education, respeaking, audio description, learning design, virtual education

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Authors: Shekher Kummari, V. Sunil Kumar, K. Vengatajalabathy Gobi

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A simple, cost-effective, reusable and reagent-free electrochemical biosensor is developed with functionalized multiwall carbon nanotube paste electrode (f-CNTPE) for the sensitive and selective determination of the important chemotherapeutic drug methotrexate (MTX), which is widely used for the treatment of various cancer and autoimmune diseases. The electrochemical response of the fabricated electrode towards the detection of MTX is examined by cyclic voltammetry (CV), differential pulse voltammetry (DPV) and square wave voltammetry (SWV). CV studies have shown that f-CNTPE electrode system exhibited an excellent electrocatalytic activity towards the oxidation of MTX in phosphate buffer (0.2 M) compared with a conventional carbon paste electrode (CPE). The oxidation peak current is enhanced by nearly two times in magnitude. Applying the DPV method under optimized conditions, a linear calibration plot is achieved over a wide range of concentration from 4.0×10⁻⁷ M to 5.5×10⁻⁶ M with the detection limit 1.6×10⁻⁷ M. further, by applying the SWV method a parabolic calibration plot was achieved starting from a very low concentration of 1.0×10⁻⁸ M, and the sensor could detect as low as 2.9×10⁻⁹ M MTX in 10 s and 10 nM were detected in steady state current-time analysis. The f-CNTPE shows very good selectivity towards the specific recognition of MTX in the presence of important biological interference. The electrochemical biosensor detects MTX in-vitro directly from pharmaceutical sample, undiluted urine and human blood serum samples at a concentration range 5.0×10⁻⁷ M with good recovery limits.

Keywords: amperometry, electrochemical detection, human blood serum, methotrexate, MWCNT, SWV

Procedia PDF Downloads 298

Authors: Joanna Cabaj, Sylwia Baluta, Karol Malecha, Kamila Drzozga

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Catecholamines are the principal neurotransmitters that mediate a variety of the central nervous system functions, such as motor control, cognition, emotion, memory processing, and endocrine modulation. Dysfunctions in catecholamine neurotransmission are induced in some neurologic and neuropsychiatric diseases. Changeable neurotransmitters level in biological fluids can be a marker of several neurological disorders. Because of its significance in analytical techniques and diagnostics, sensitive and selective detection of neurotransmitters is increasingly attracting a lot of attention in different areas of bio-analysis or biomedical research. Recently, fluorescent techniques for detection of catecholamines have attracted interests due to their reasonable cost, convenient control, as well as maneuverability in biological environments. Nevertheless, with the observed need for a sensitive and selective catecholamines sensor, the development of a convenient method for this neurotransmitter is still at its basic level. The manipulation of nanostructured materials in conjunction with biological molecules has led to the development of a new class of hybrid modified biosensors in which both enhancement of charge transport and biological activity preservation may be obtained. Immobilization of biomaterials on electrode surfaces is the crucial step in fabricating electrochemical as well as optical biosensors and bioelectronic devices. Continuing systematic investigation in the manufacturing of enzyme–conducting sensitive systems, here is presented a convenient fluorescence sensing strategy for catecholamines detection based on FRET (fluorescence resonance energy transfer) phenomena observed for, i.e., complexes of Fe²⁺ and epinephrine. The biosensor was constructed using low temperature co-fired ceramics technology (LTCC). This sensing system used the catalytical oxidation of catecholamines and quench of the strong luminescence of obtained complexes due to FRET. The detection process was based on the oxidation of substrate in the presence of the enzyme–laccase/tyrosinase.

Keywords: biosensor, conducting polymer, enzyme, FRET, LTCC

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Authors: Neeraj Neeraj, Soumen Basu, Banibrata Maity

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Glutathione (GSH) is one of the most important biomolecules having small molecular weight, which helps in various cellular functions like regulation of gene, xenobiotic metabolism, preservation of intracellular redox activities, signal transduction, etc. Therefore, the detection of GSH requires huge attention by using extremely selective and sensitive techniques. Herein, a rapid fluorometric nanosensor is designed by combining carbon dots (Cdots) and MnO₂ nanoparticles of different sizes for the detection of GSH. The bottom-up approach, i.e., microwave method, was used for the preparation of the water soluble and greatly fluorescent Cdots by using ascorbic acid as a precursor. MnO₂ nanospheres of different sizes (large, medium, and small) were prepared by varying the ratio of concentration of methionine and KMnO₄ at room temperature, which was confirmed by HRTEM analysis. The successive addition of MnO₂ nanospheres in Cdots results fluorescence quenching. From the fluorescence intensity data, Stern-Volmer quenching constant values (KS-V) were evaluated. From the fluorescence intensity and lifetime analysis, it was found that the degree of fluorescence quenching of Cdots followed the order: large > medium > small. Moreover, fluorescence recovery studies were also performed in the presence of GSH. Fluorescence restoration studies also show the order of turn on follows the same order, i.e., large > medium > small, which was also confirmed by quantum yield and lifetime studies. The limits of detection (LOD) of GSH in presence of Cdots@different sized MnO₂ nanospheres were also evaluated. It was observed thatLOD values were in μM region and lowest in case of large MnO₂ nanospheres. The separation distance (d) between Cdots and the surface of different MnO₂ nanospheres was determined. The d values increase with increase in the size of the MnO₂ nanospheres. In summary, the synthesized Cdots@MnO₂ nanocomposites acted as a rapid, simple, economical as well as environmental-friendly nanosensor for the detection of GSH.

Keywords: carbon dots, fluorescence, glutathione, MnO₂ nanospheres, turn off-on

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Authors: Shih-Wen Hsiao, Chu-Hsuan Lee, Rong-Qi Chen

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When it comes to last, it is regarded as the critical foundation of shoe design and development. Not only the last relates to the comfort of shoes wearing but also it aids the production of shoe styling and manufacturing. In order to enhance the efficiency and application of last development, a computer aided methodology for customized last form designs is proposed in this study. The reverse engineering is mainly applied to the process of scanning for the last form. Then the minimum energy is used for the revision of surface continuity, the surface of the last is reconstructed with the feature curves of the scanned last. When the surface of a last is reconstructed, based on the foundation of the proposed last form reconstruction module, the weighted arithmetic mean method is applied to the calculation on the shape morphing which differs from the grading for the control mesh of last, and the algorithm of subdivision is used to create the surface of last mesh, thus the feet-fitting 3D last form of different sizes is generated from its original form feature with functions remained. Finally, the practicability of the proposed methodology is verified through later case studies.

Keywords: 3D last design, customization, reverse engineering, weighted morphing, shape blending

Procedia PDF Downloads 331

Authors: Monday Agbonica Bello Eje

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The language of the print media is characterized by the use of apposition. This linguistic element function strategically in journalistic discourse where it is communicatively necessary to name individuals and provide information about them. Linguistic studies on the language of the print media with bias for apposition have largely dwelt on other areas but the examination of the typology of appositive reference in newspaper discourse. Yet, it is capable of revealing ways writers communicate and provide information necessary for readers to follow and understand the message. The study, therefore, analyses the patterns of appositional occurrences and the typology of reference in newspaper articles. The data were obtained from The Punch and Daily Trust Newspapers. A total of six editions of these newspapers were collected randomly spread over three months. News and feature articles were used in the analysis. Guided by the referential theory of meaning in discourse, the appositions identified were subjected to analysis. The findings show that the semantic relation of coreference and speaker coreference have the highest percentage and frequency of occurrence in the data. This is because the subject matter of news reports and feature articles focuses on humans and the events around them; as a result, readers need to be provided with some form of detail and background information in order to identify as well as follow the discourse. Also, the non-referential relation of absolute synonymy and speaker synonymy no doubt have fewer occurrences and percentages in the analysis. This is tied to a major feature of the language of the media: simplicity. The paper concludes that appositions is mainly used for the purpose of providing the reader with much detail. In this way, the writer transmits information which helps him not only to give detailed yet concise descriptions but also in some way help the reader to follow the discourse.

Keywords: apposition, discourse, newspaper, Nigeria, reference

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Authors: Layla El Gaini, Majdouline Belaqziz, Meriem Outaki, Mariam Minhaj

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In this study, it introduce and validate a high-performance liquid chromatography method with diode-array detection (HPLC-DAD) specifically designed for the accurate quantification of caffeic acid in phenolic extracts obtained from olive mill wastewater. The separation process of caffeic acid was effectively achieved through the use of an Acclaim Polar Advantage column (5µm, 250x4.6mm). A meticulous multi-step gradient mobile phase was employed, comprising water acidified with phosphoric acid (pH 2.3) and acetonitrile, to ensure optimal separation. The diode-array detection was adeptly conducted within the UV–VIS spectrum, spanning a range of 200–800 nm, which facilitated precise analytical results. The method underwent comprehensive validation, addressing several essential analytical parameters, including specificity, repeatability, linearity, as well as the limits of detection and quantification, alongside measurement uncertainty. The generated linear standard curves displayed high correlation coefficients, underscoring the method's efficacy and consistency. This validated approach is not only robust but also demonstrates exceptional reliability for the focused analysis of caffeic acid within the intricate matrices of wastewater, thus offering significant potential for applications in environmental and analytical chemistry.

Keywords: high-performance liquid chromatography (HPLC-DAD), caffeic acid analysis, olive mill wastewater phenolics, analytical method validation

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Authors: Mahdi Pirmoradian, Olayinka Adigun, Christos Politis

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Cooperative spectrum sensing is a crucial challenge in cognitive radio networks. Cooperative sensing can increase the reliability of spectrum hole detection, optimize sensing time and reduce delay in cooperative networks. In this paper, an efficient central capacity optimization algorithm is proposed to minimize cooperative sensing time in a homogenous sensor network using OR decision rule subject to the detection and false alarm probabilities constraints. The evaluation results reveal significant improvement in the sensing time and normalized capacity of the cognitive sensors.

Keywords: cooperative networks, normalized capacity, sensing time

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Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

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Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

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Authors: H. Zidoum, A. AlShareedah, S. Al Sawafi, A. Al-Ansari, B. Al Lawati

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Systemic lupus erythematosus (SLE) is an autoimmune disease with genetic and environmental components. SLE is characterized by a wide variability of clinical manifestations and a course frequently subject to unpredictable flares. Despite recent progress in classification tools, the early diagnosis of SLE is still an unmet need for many patients. This study proposes an interpretable disease classification model that combines the high and efficient predictive performance of CatBoost and the model-agnostic interpretation tools of Shapley Additive exPlanations (SHAP). The CatBoost model was trained on a local cohort of 219 Omani patients with SLE as well as other control diseases. Furthermore, the SHAP library was used to generate individual explanations of the model's decisions as well as rank clinical features by contribution. Overall, we achieved an AUC score of 0.945, F1-score of 0.92 and identified four clinical features (alopecia, renal disorders, cutaneous lupus, and hemolytic anemia) along with the patient's age that was shown to have the greatest contribution on the prediction.

Keywords: feature selection, classification, systemic lupus erythematosus, model interpretation, SHAP, Catboost

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Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Maleh Yassine, Ezzati Abdellah

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Wireless Sensor Networks (WSNs) are currently used in different industrial and consumer applications, such as earth monitoring, health related applications, natural disaster prevention, and many other areas. Security is one of the major aspects of wireless sensor networks due to the resource limitations of sensor nodes. However, these networks are facing several threats that affect their functioning and their life. In this paper we present security attacks in wireless sensor networks, and we focus on a review and analysis of the recent Intrusion Detection schemes in WSNs.

Keywords: wireless sensor networks, security attack, denial of service, IDS, cluster-based model, signature based IDS, hybrid IDS

Procedia PDF Downloads 371

Authors: Eftychia Kessopoulou, Styliani Xanthopoulou, Ypatia Theodorakioglou, George Tsiotras, Katerina Gotzamani

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Public sector managers frequently herald that implementing best practices as a set of standards, may lead to superior organizational performance. However, recent research questions the objectification of best practices, highlighting: a) the inability of public sector organizations to develop innovative administrative practices, as well as b) the adoption of stereotypical renowned practices inculcated in the public sector by international governance bodies. The process through which organizations construe what a best practice is, still remains a black box that is yet to be investigated, given the trend of continuous changes in public sector performance, as well as the burgeoning interest of sharing popular administrative practices put forward by international bodies. This study aims to describe and understand how organizational best practices are constructed by public sector performance management teams, like benchmarkers, during the benchmarking-mediated performance improvement process and what mechanisms enable this construction. A critical realist action research methodology is employed, starting from a description of various approaches on best practice nature when a benchmarking-mediated performance improvement initiative, such as the Common Assessment Framework, is applied. Firstly, we observed the benchmarker’s management process of best practices in a public organization, so as to map their theories-in-use. As a second step we contextualized best administrative practices by reflecting the different perspectives emerged from the previous stage on the design and implementation of an interview protocol. We used this protocol to conduct 30 semi-structured interviews with “best practice” process owners, in order to examine their experiences and performance needs. Previous research on best practices has shown that needs and intentions of benchmarkers cannot be detached from the causal mechanisms of the various contexts in which they work. Such causal mechanisms can be found in: a) process owner capabilities, b) the structural context of the organization, and c) state regulations. Therefore, we developed an interview protocol theoretically informed in the first part to spot causal mechanisms suggested by previous research studies and supplemented it with questions regarding the provision of best practice support from the government. Findings of this work include: a) a causal account of the nature of best administrative practices in the Greek public sector that shed light on explaining their management, b) a description of the various contexts affecting best practice conceptualization, and c) a description of how their interplay changed the organization’s best practice management.

Keywords: benchmarking, action research, critical realism, best practices, public sector

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Ravi Kant, Banshi D. Gupta

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The unbridled application of insecticides to enhance agricultural yield has become a matter of grave concern to both the environment and the human health and, thus pose a potential threat to sustainable development. Fenitrothion is an extensively used organophosphate insecticide whose residues are reported to be extremely toxic for birds, humans and aquatic life. A sensitive, swift and accurate detection protocol for fenitrothion is, thus, highly demanded. In this work, we report an SPR based fiber optic sensor for the detection of fenitrothion, where a nanocomposite arrangement of Ta2O5 and reduced graphene oxide (rGO) (Ta₂O₅: rGO) decorated on silver coated unclad core region of an optical fiber forms the sensing channel. A nanocomposite arrangement synergistically integrates the properties of involved components and consequently furnishes a conducive framework for sensing applications. The modification of the dielectric function of the sensing layer on exposure to fenitrothion solutions of diverse concentration forms the sensing mechanism. This modification is reflected in terms of the shift in resonance wavelength. Experimental variables such as the concentration of rGO in the nanocomposite configuration, dip time of silver coated fiber optic probe for deposition of sensing layer and influence of pH on the performance of the sensor have been optimized to extract the best performance of the sensor. SPR studies on the optimized sensing probe reveal the high sensitivity, wide operating range and good reproducibility of the fabricated sensor, which unveil the promising utility of Ta₂O₅: rGO nanocomposite framework for developing an efficient detection methodology for fenitrothion. FOSPR approach in cooperation with nanomaterials projects the present work as a beneficial approach for fenitrothion detection by imparting numerous useful advantages such as sensitivity, selectivity, compactness and cost-effectiveness.

Keywords: surface plasmon resonance, optical fiber, sensor, fenitrothion

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Authors: Danielle Shackley, Yetunde Folajimi

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As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

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Authors: Mrinal Kanti Bhowmik, Shawli Bardhan Jr., Debotosh Bhattacharjee

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Human skin containing temperature value more than absolute zero, discharges infrared radiation related to the frequency of the body temperature. The difference in infrared radiation from the skin surface reflects the abnormality present in human body. Considering the difference, detection and forecasting the temperature variation of the skin surface is the main objective of using Medical Infrared Thermography(MIT) as a diagnostic tool for pain detection. Medical Infrared Thermography(MIT) is a non-invasive imaging technique that records and monitors the temperature flow in the body by receiving the infrared radiated from the skin and represent it through thermogram. The intensity of the thermogram measures the inflammation from the skin surface related to pain in human body. Analysis of thermograms provides automated anomaly detection associated with suspicious pain regions by following several image processing steps. The paper represents a rigorous study based survey related to the processing and analysis of thermograms based on the previous works published in the area of infrared thermal imaging for detecting inflammatory pain diseases like arthritis, spondylosis, shoulder impingement, etc. The study also explores the performance analysis of thermogram processing accompanied by thermogram acquisition protocols, thermography camera specification and the types of pain detected by thermography in summarized tabular format. The tabular format provides a clear structural vision of the past works. The major contribution of the paper introduces a new thermogram acquisition standard associated with inflammatory pain detection in human body to enhance the performance rate. The FLIR T650sc infrared camera with high sensitivity and resolution is adopted to increase the accuracy of thermogram acquisition and analysis. The survey of previous research work highlights that intensity distribution based comparison of comparable and symmetric region of interest and their statistical analysis assigns adequate result in case of identifying and detecting physiological disorder related to inflammatory diseases.

Keywords: acquisition protocol, inflammatory pain detection, medical infrared thermography (MIT), statistical analysis

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Authors: Andrea Trevino Gavito, Diego Klabjan, Sanjiv Shah

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Echocardiograms serve as pivotal resources for clinicians in diagnosing cardiac conditions, offering non-invasive insights into a heart’s structure and function. When echocardiographic studies are conducted, no standardized labeling of the acquired views is performed. Employing machine learning algorithms for automated echocardiogram view detection has emerged as a promising solution to enhance efficiency in echocardiogram use for diagnosis. However, existing approaches predominantly rely on supervised learning, necessitating labor-intensive expert labeling. In this paper, we introduce a fully unsupervised echocardiographic view detection framework that leverages convolutional autoencoders to obtain lower dimensional representations and the K-means algorithm for clustering them into view-related groups. Our approach focuses on discriminative patches from echocardiographic frames. Additionally, we propose a trainable inverse average layer to optimize the decoding of average operations. By integrating both public and proprietary datasets, we obtain a marked improvement in model performance when compared to utilizing a proprietary dataset alone. Our experiments show boosts of 15.5% in accuracy and 9.0% in the F-1 score for frame-based clustering, 25.9% in accuracy, and 19.8% in the F-1 score for view-based clustering. Our research highlights the potential of unsupervised learning methodologies and the utilization of open-sourced data in addressing the complexities of echocardiogram interpretation, paving the way for more accurate and efficient cardiac diagnoses.

Keywords: artificial intelligence, machine learning, unsupervised learning, self-supervised representation learning, echocardiography, echocardiographic view detection

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Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

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Authors: Marwa Qaraqe, Muhammad Ismail, Erchin Serpedin

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

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

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Authors: Branimir Jurun, Elza Jurun

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Focus of this paper is description and functioning manner of the instrument for wave pressure metering. Moreover, an essential component of this paper is the proposal of a metering unit for the direct wave pressure measurement determined by the shape and surface of the instrument including the number of sensors and angle between them. Namely, far applied instruments by means of height, length, direction, wave time period and other components determine wave pressure on a particular area. This instrument, allows the direct measurement i.e. measurement without additional calculation, of the wave pressure expressed in a standardized unit of measure. That way the instrument has a standardized form, surface, number of sensors and the angle between them. In addition, it is made with the status that follows the wave and always is on the water surface. Database quality which is listed by the instrument is made possible by using the Arduino chip. This chip is programmed for receiving by two data from each of the sensors each second. From these data by a pre-defined manner a unique representative value is estimated. By this procedure all relevant wave pressure measurement results are directly and immediately registered. Final goal of establishing such a rich database is a comprehensive statistical analysis that ranges from multi-criteria analysis across different modeling and parameters testing to hypothesis accepting relating to the widest variety of man-made activities such as filling of beaches, security cages for aquaculture, bridges construction.

Keywords: instrument, metering, water, waves

Procedia PDF Downloads 254

Authors: Nataša Todorović, Jovana Nikolov, Ivana Stojković, Milan Vraneš, Jovana Panić, Slobodan Gadžurić

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The detection of ²¹⁰Pb levels in aquatic environments evokes interest in various scientific studies. Its precise determination is important not only for the radiological assessment of drinking waters but also ²¹⁰Pb, and ²¹⁰Po distribution in the marine environment are significant for the assessment of the removal rates of particles from the ocean and particle fluxes during transport along the coast, as well as particulate organic carbon export in the upper ocean. Measurement techniques for ²¹⁰Pb determination, gamma spectrometry, alpha spectrometry, or liquid scintillation counting (LSC) are either time-consuming or demand expensive equipment or complicated chemical pre-treatments. However, one other possibility is to measure ²¹⁰Pb on an LS counter if it is in equilibrium with its progeny ²¹⁰Bi - through the Cherenkov counting method. It is unaffected by the chemical quenching and assumes easy sample preparation but has the drawback of lower counting efficiencies than standard LSC methods, typically from 10% up to 20%. The aim of the presented research in this paper is to investigate the possible increment of detection efficiency of Cherenkov counting during ²¹⁰Pb/²¹⁰Bi detection on an LS counter Quantulus 1220. Considering naturally low levels of ²¹⁰Pb in aqueous samples, the addition of ionic liquids to the counting vials with the analysed samples has the benefit of detection limit’s decrement during ²¹⁰Pb quantification. Our results demonstrated that ionic liquid, 1-butyl-3-methylimidazolium salicylate, is more efficient in Cherenkov counting efficiency increment than the previously explored 2-hydroxypropan-1-amminium salicylate. Consequently, the impact of a few other ionic liquids that were synthesized with the same cation group (1-butyl-3-methylimidazolium benzoate, 1-butyl-3-methylimidazolium 3-hydroxybenzoate, and 1-butyl-3-methylimidazolium 4-hydroxybenzoate) was explored in order to test their potential influence on Cherenkov counting efficiency. It was confirmed that, among the explored ones, only ionic liquids in the form of salicylates exhibit a wavelength shifting effect. Namely, the addition of small amounts (around 0.8 g) of 1-butyl-3-methylimidazolium salicylate increases the detection efficiency from 16% to >70%, consequently reducing the detection threshold by more than four times. Moreover, the addition of ionic liquids could find application in the quantification of other radionuclides besides ²¹⁰Pb/²¹⁰Bi via Cherenkov counting method.

Keywords: liquid scintillation counting, ionic liquids, Cherenkov counting, ²¹⁰PB/²¹⁰BI in water

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Authors: Alaa A. Almarzuki, Nora A. Farraj, Aisha M. Alshiky, Omar A. Batarfi

Abstract:

The number of internet users is dramatically increased every year. Most of these users are exposed to the dangers of attackers in one way or another. The reason for this lies in the presence of many weaknesses that are not known for native users. In addition, the lack of user awareness is considered as the main reason for falling into the attackers’ snares. Cross Site Request Forgery (CSRF) has placed in the list of the most dangerous threats to security in OWASP Top Ten for 2013. CSRF is an attack that forces the user’s browser to send or perform unwanted request or action without user awareness by exploiting a valid session between the browser and the server. When CSRF attack successes, it leads to many bad consequences. An attacker may reach private and personal information and modify it. This paper aims to detect and prevent a specific type of CSRF, called reflected CSRF. In a reflected CSRF, a malicious code could be injected by the attackers. This paper explores how CSRF Detection Extension prevents the reflected CSRF by checking browser specific information. Our evaluation shows that the proposed solution succeeds in preventing this type of attack.

Keywords: CSRF, CSRF detection extension, attackers, attacks

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Authors: Haidy Weliam Megaly Gouda

Abstract:

Dermatology is often a neglected specialty in low-resource settings despite the high morbidity associated with skin disease. This becomes even more significant when associated with HIV infection, as dermatological conditions are more common and aggressive in HIV-positive patients. African countries have the highest HIV infection rates, and skin conditions are frequently misdiagnosed and mismanaged because of a lack of dermatological training and educational material. The frequent lack of diagnostic tests in the African setting renders basic clinical skills all the more vital. This project aimed to improve the diagnosis and treatment of skin disease in the HIV population in a district hospital in Malawi. A basic dermatological clinical tool was developed and produced in collaboration with local staff and based on available literature and data collected from clinics. The aim was to improve diagnostic accuracy and provide guidance for the treatment of skin disease in HIV-positive patients. A literature search within Embassy, Medline and Google Scholar was performed and supplemented through data obtained from attending 5 Antiretroviral clinics. From the literature, conditions were selected for inclusion in the resource if they were described as specific, more prevalent, or extensive in the HIV population or have more adverse outcomes if they develop in HIV patients. Resource-appropriate treatment options were decided using Malawian Ministry of Health guidelines and textbooks specific to African dermatology. After the collection of data and discussion with local clinical and pharmacy staff, a list of 15 skin conditions was included, and a booklet was created using the simple layout of a picture, a diagnostic description of the disease and treatment options. Clinical photographs were collected from local clinics (with full consent of the patient) or from the book ‘Common Skin Diseases in Africa’ (permission granted if fully acknowledged and used in a not-for-profit capacity). This tool was evaluated by the local staff alongside an educational teaching session on skin disease. This project aimed to reduce uncertainty in diagnosis and provide guidance for appropriate treatment in HIV patients by gathering information into one practical and manageable resource. To further this project, we hope to review the effectiveness of the tool in practice.

Keywords: prevalence and pattern of skin diseases, impact on quality of life, rural Nepal, interventions, quality switched ruby laser, skin color river blindness, clinical signs, circularity index, grey level run length matrix, grey level co-occurrence matrix, local binary pattern, object detection, ring detection, shape identification

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Authors: Ahmed Ashit

Abstract:

Image fusion is a significant and efficient image processing method used for detecting different types of tumors. This method has been used as an effective combination technique for obtaining high quality images that combine anatomy and physiology of an organ. It is the main key in the huge biomedical machines for diagnosing cancer such as PET-CT machine. This thesis aims to develop an image analysis system for the detection of the eye tumor. Different image processing methods are used to extract the tumor and then mark it on the original image. The images are first smoothed using median filtering. The background of the image is subtracted, to be then added to the original, results in a brighter area of interest or tumor area. The images are adjusted in order to increase the intensity of their pixels which lead to clearer and brighter images. once the images are enhanced, the edges of the images are detected using canny operators results in a segmented image comprises only of the pupil and the tumor for the abnormal images, and the pupil only for the normal images that have no tumor. The images of normal and abnormal images are collected from two sources: “Miles Research” and “Eye Cancer”. The computerized experimental results show that the developed image fusion based eye tumor detection system is capable of detecting the eye tumor and segment it to be superimposed on the original image.

Keywords: image fusion, eye tumor, canny operators, superimposed

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Authors: Andreas Livera, Spyros Theocharides, Michalis Florides, Charalambos Anastassiou

Abstract:

A main challenge in the quest for ensuring quality of operation, especially for photovoltaic (PV) systems, is to safeguard the reliability and optimal performance by detecting and diagnosing potential failures and performance losses at early stages or before the occurrence through real-time monitoring, supervision, fault detection, and predictive maintenance. The purpose of this work is to present the functionalities and results related to the development and validation of a software platform for PV assets diagnosis and maintenance. The platform brings together proprietary hardware sensors and software algorithms to enable the early detection and prediction of the most common and critical faults in PV systems. It was validated using field measurements from operating PV systems. The results showed the effectiveness of the platform for detecting faults and losses (e.g., inverter failures, string disconnections, and potential induced degradation) at early stages, forecasting PV power production while also providing recommendations for maintenance actions. Increased PV energy yield production and revenue can be thus achieved while also minimizing operation and maintenance (O&M) costs.

Keywords: failure detection and prediction, operation and maintenance, performance monitoring, photovoltaic, platform, recommendations, predictive maintenance

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Authors: Sadam Alwadi

Abstract:

Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

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Authors: Nareshkumar Harale, B. B. Meshram

Abstract:

The continued exponential growth of successful cyber intrusions against today’s businesses has made it abundantly clear that traditional perimeter security measures are no longer adequate and effective. We evolved the network trust architecture from trust-untrust to Zero-Trust, With Zero Trust, essential security capabilities are deployed in a way that provides policy enforcement and protection for all users, devices, applications, data resources, and the communications traffic between them, regardless of their location. Information exchange over the Internet, in spite of inclusion of advanced security controls, is always under innovative, inventive and prone to cyberattacks. TCP/IP protocol stack, the adapted standard for communication over network, suffers from inherent design vulnerabilities such as communication and session management protocols, routing protocols and security protocols are the major cause of major attacks. With the explosion of cyber security threats, such as viruses, worms, rootkits, malwares, Denial of Service attacks, accomplishing efficient and effective intrusion detection and prevention is become crucial and challenging too. In this paper, we propose a design and analysis model for next generation network intrusion detection and protection system as part of layered security strategy. The proposed system design provides intrusion detection for wide range of attacks with layered architecture and framework. The proposed network intrusion classification framework deals with cyberattacks on standard TCP/IP protocol, routing protocols and security protocols. It thereby forms the basis for detection of attack classes and applies signature based matching for known cyberattacks and data mining based machine learning approaches for unknown cyberattacks. Our proposed implemented software can effectively detect attacks even when malicious connections are hidden within normal events. The unsupervised learning algorithm applied to network audit data trails results in unknown intrusion detection. Association rule mining algorithms generate new rules from collected audit trail data resulting in increased intrusion prevention though integrated firewall systems. Intrusion response mechanisms can be initiated in real-time thereby minimizing the impact of network intrusions. Finally, we have shown that our approach can be validated and how the analysis results can be used for detecting and protection from the new network anomalies.

Keywords: network intrusion detection, network intrusion prevention, association rule mining, system analysis and design

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Authors: Amina Farooq, Nauman Zafar Butt

Abstract:

This paper is about methods and tools for counting particles of interest, such as cells. A microfluidic system with interconnected electronics on a flexible substrate, inlet-outlet ports and interface schemes, sensitive and selective detection of cells specificity, and processing of cell counting at polymer interfaces in a microscale biosensor for use in the detection of target biological and non-biological cells. The development of fluidic channels, planar fluidic contact ports, integrated metal electrodes on a flexible substrate for impedance measurements, and a surface modification plasma treatment as an intermediate bonding layer are all part of the fabrication process. Magnetron DC sputtering is used to deposit a double metal layer (Ti/Pt) over the polypropylene film. Using a photoresist layer, specified and etched zones are established. Small fluid volumes, a reduced detection region, and electrical impedance measurements over a range of frequencies for cell counts improve detection sensitivity and specificity. The procedure involves continuous flow of fluid samples that contain particles of interest through the microfluidic channels, counting all types of particles in a portion of the sample using the electrical differential counter to generate a bipolar pulse for each passing cell—calculating the total number of particles of interest originally in the fluid sample by using MATLAB program and signal processing. It's indeed potential to develop a robust and economical kit for cell counting in whole-blood samples using these methods and similar devices.

Keywords: impedance, biochip, cell counting, microfluidics

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Authors: Siddhant Rao

Abstract:

Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming. It usually requires a trained pathologist to manually examine histopathological images and identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the three factors that characterize tumor belligerence described by the Nottingham Grading System: mitotic count. Other computational approaches to mitotic figure counting and detection do not demonstrate ample recall or precision to be clinically viable. Our models outperformed all previous participants in the ICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challenge along with recently published works. Our model achieved an F- measure score of 0.955, a 6.11% improvement in accuracy from the most accurate of the previously proposed models.

Keywords: breast cancer, mitotic count, machine learning, convolutional neural networks

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