Search results for: automated diagnoses

Authors: T. Ahilan, V. Aswin Adityan, S. Kailash

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UAV’s are small remote operated or automated aerial surveillance systems without a human pilot aboard. UAV’s generally finds its use in military and special operation application, a recent growing trend in UAV’s finds its application in several civil and non military works such as inspection of power or pipelines. The objective of this paper is the augmentation of a UAV in order to replace the existing expensive sonar (sound navigation and ranging) based equipment amongst small scale fisherman, for whom access to sonar equipment are restricted due to limited economic resources. The surveillance equipment’s present in the UAV will relay data and GPS location onto a receiver on the fishing boat using RF signals, using which the location of the schools of fishes can be found. In addition to this, an emergency beacon system is present for rescue operations and drone recovery.

Keywords: UAV, Surveillance, RF signals, fishing, sonar, GPS, video stream, school of fish

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Authors: Anne Giles

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Trauma is generally defined as an experience, or multiple experiences, overwhelming a person's ability to cope. Over time, many people recover from the neurobiological, physical, and emotional effects of trauma on their own. For some people, however, troubling symptoms develop over time that can result in distress and disability. This cluster of symptoms is classified as Post-traumatic Stress Disorder (PTSD). People who meet the criteria for PTSD and other trauma-related disorder diagnoses often hold a set of understandable but unfounded beliefs about traumatic events that cause undue suffering. Becoming aware of unhelpful beliefs—termed "cognitive distortions"—and challenging them is the realm of Cognitive Behavior Therapy (CBT). A form of CBT found by researchers to be especially effective for PTSD is Cognitive Processing Therapy (CPT). Through the compassionate use of CPT, people identify, examine, challenge, and relinquish unhelpful beliefs, thereby reducing symptoms and suffering. Widely-held cultural beliefs can interfere with the progress of recovery from trauma-related disorders. Although highly revered, largely unquestioned, and often stabilizing, cultural beliefs can be founded in simplistic, dichotomous thinking, i.e., things are all right, or all wrong, all good, or all bad. The reality, however, is nuanced and complex. After studying examples of cultural beliefs from China and the United States and how these might interfere with trauma recovery, trauma counselors can help clients derive criteria for preserving helpful beliefs, discover, examine, and jettison unhelpful beliefs, reduce trauma symptoms, and live their lives more freely and fully.

Keywords: cognitive processing therapy (CPT), cultural beliefs, post-traumatic stress disorder (PTSD), trauma recovery

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Authors: B. H. Aitchanov, Sh. K. Aitchanova, O. A. Baimuratov

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This paper is focused on dynamic pulse-frequency modulation (DPFM) control systems. Currently, the control law based on DPFM control signals is widely used in direct digital control subsystems introduced in the automated control systems of technological processes. Statistical analysis of automatic control systems is reduced to its construction of functional relationships between the statistical characteristics of the errors processes and input processes. Structural and dynamic Volterra models of digital pulse-frequency control systems can be used to develop methods for generating the dependencies, differing accuracy, requiring the amount of information about the statistical characteristics of input processes and computing labor intensity of their use.

Keywords: digital dynamic pulse-frequency control systems, dynamic pulse-frequency modulation, control object, discrete filter, impulse device, microcontroller

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Authors: Jamison Duckworth, Shankarachary Ragi

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Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

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Authors: Li Hui, Riyadh Hindi

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Concrete structures frequently suffer from crack formation, a critical issue that can significantly reduce their lifespan by allowing damaging agents to enter. Traditional methods of crack detection depend on manual visual inspections, which heavily relies on the experience and expertise of inspectors using tools. In this study, a more efficient, computer vision-based approach is introduced by using the lightweight MobileNetV2 neural network. A dataset of 40,000 images was used to develop a specialized crack evaluation algorithm. The analysis indicates that MobileNetV2 matches the accuracy of traditional CNN methods but is more efficient due to its smaller size, making it well-suited for mobile device applications. The effectiveness and reliability of this new method were validated through experimental testing, highlighting its potential as an automated solution for crack detection in concrete structures.

Keywords: Concrete crack, computer vision, deep learning, MobileNetV2 neural network

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Authors: Maulik Parmar, Sumeet Sandhu

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The online fashion industry experiences high product return rates. Many returns are because of size/fit mismatches -the size scale on labels can vary across brands, the size parameters may not capture all fit measurements, or the product may have manufacturing defects. Warehouse quality check of garment sizes can be semi-automated to improve speed and accuracy. This paper presents an approach for automatically measuring garment sizes from a single image of the garment -using Deep Learning to learn garment keypoints. The paper focuses on the waist size measurement of jeans and can be easily extended to other garment types and measurements. Experimental results show that this approach can greatly improve the speed and accuracy of today’s manual measurement process.

Keywords: convolutional neural networks, deep learning, distortion, garment measurements, image warping, keypoints

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Authors: Noha Seddik, Sherine Youssef, Mohamed Kholeif

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The automated epileptic seizure detection research field has emerged in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this study, a Support Vector Machine (SVM) that uses Weighted Permutation Entropy (WPE) as the input feature is proposed for classifying normal and seizure EEG records. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG.

Keywords: electroencephalogram (EEG), epileptic seizure detection, weighted permutation entropy (WPE), support vector machine (SVM)

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Authors: Hitesh Tahbildar

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The cost of test data generation for a program is computationally very high. In general case, no algorithm to generate test data for all types of algorithms has been found. The cost of generating test data for different types of algorithm is different. Till date, people are emphasizing the need to generate test data for different types of programming constructs rather than different types of algorithms. The test data generation methods have been implemented to find heuristics for different types of algorithms. Some algorithms that includes divide and conquer, backtracking, greedy approach, dynamic programming to find the minimum cost of test data generation have been tested. Our experimental results say that some of these types of algorithm can be used as a necessary condition for selecting heuristics and programming constructs are sufficient condition for selecting our heuristics. Finally we recommend the different heuristics for test data generation to be selected for different types of algorithms.

Keywords: ongest path, saturation point, lmax, kL, kS

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Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary

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Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM

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Authors: Kolade Olawande Owoeye, George R. S. Weir

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The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification.

Keywords: extremist, web pages, classification, semantics, posit

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Authors: D. R. Ambika, K. R. Radhika, D. Seshachalam

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Unconstrained authentication is an important component for personal automated systems and human-computer interfaces. Existing solutions mostly use face as the primary object of analysis. The performance of face-based systems is largely determined by the extent of deformation caused in the facial region and amount of useful information available in occluded face images. Periocular region is a useful portion of face with discriminative ability coupled with resistance to deformation. A reliable portion of periocular area is available for occluded images. The present work demonstrates that joint representation of periocular texture and periocular structure provides an effective expression and poses invariant representation. The proposed methodology provides an effective and compact description of periocular texture and shape. The method is tested over four benchmark datasets exhibiting varied acquisition conditions.

Keywords: periocular authentication, Zernike moments, LBP variance, shape and texture fusion

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The educational system faces a significant concern with regards to Dyslexia and Dysgraphia, which are learning disabilities impacting reading and writing abilities. This is particularly challenging for children who speak the Sinhala language due to its complexity and uniqueness. Commonly used methods to detect the risk of Dyslexia and Dysgraphia rely on subjective assessments, leading to limited coverage and time-consuming processes. Consequently, delays in diagnoses and missed opportunities for early intervention can occur. To address this issue, the project developed a hybrid model that incorporates various deep learning techniques to detect the risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16, and YOLOv8 models were integrated to identify handwriting issues. The outputs of these models were then combined with other input data and fed into an MLP model. Hyperparameters of the MLP model were fine-tuned using Grid Search CV, enabling the identification of optimal values for the model. This approach proved to be highly effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention. The Resnet50 model exhibited a training accuracy of 0.9804 and a validation accuracy of 0.9653. The VGG16 model achieved a training accuracy of 0.9991 and a validation accuracy of 0.9891. The MLP model demonstrated impressive results with a training accuracy of 0.99918, a testing accuracy of 0.99223, and a loss of 0.01371. These outcomes showcase the high accuracy achieved by the proposed hybrid model in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, dyslexia, dysgraphia, deep learning, learning disabilities, data science

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The problem of Dyslexia and Dysgraphia, two learning disabilities that affect reading and writing abilities, respectively, is a major concern for the educational system. Due to the complexity and uniqueness of the Sinhala language, these conditions are especially difficult for children who speak it. The traditional risk detection methods for Dyslexia and Dysgraphia frequently rely on subjective assessments, making it difficult to cover a wide range of risk detection and time-consuming. As a result, diagnoses may be delayed and opportunities for early intervention may be lost. The project was approached by developing a hybrid model that utilized various deep learning techniques for detecting risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16 and YOLOv8 were integrated to detect the handwriting issues, and their outputs were fed into an MLP model along with several other input data. The hyperparameters of the MLP model were fine-tuned using Grid Search CV, which allowed for the optimal values to be identified for the model. This approach proved to be effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention of these conditions. The Resnet50 model achieved an accuracy of 0.9804 on the training data and 0.9653 on the validation data. The VGG16 model achieved an accuracy of 0.9991 on the training data and 0.9891 on the validation data. The MLP model achieved an impressive training accuracy of 0.99918 and a testing accuracy of 0.99223, with a loss of 0.01371. These results demonstrate that the proposed hybrid model achieved a high level of accuracy in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, Dyslexia, Dysgraphia, deep learning, learning disabilities, data science

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

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

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

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Authors: Ayesha Anjum, Arbaz Basha

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Active Galactic Nuclei (AGN) are the actively accreting centers of the galaxies that host supermassive black holes. AGN emits radiation in all wavelengths and also shows variability across all the wavelength bands. The analysis of flux variability tells us about the morphology of the site of emission radiation. Some of the major classifications of AGN are (a) Blazars, with featureless spectra. They are subclassified as BLLacertae objects, Flat Spectrum Radio Quasars (FSRQs), and others; (b) Seyferts with prominent emission line features are classified into Broad Line, Narrow Line Seyferts of Type 1 and Type 2 (c) quasars, and other types. Sloan Digital Sky Survey (SDSS) is an optical telescope based in Mexico that has observed and classified billions of objects based on automated photometric and spectroscopic methods. A sample of blazars is obtained from the third Fermi catalog. For variability analysis, we searched for light curves for these objects in Wide-Field Infrared Survey Explorer (WISE) and Near Earth Orbit WISE (NEOWISE) in two bands: W1 (3.4 microns) and W2 (4.6 microns), reducing the final sample to 256 objects. These objects are also classified into 155 BLLacs, 99 FSRQs, and 2 Narrow Line Seyferts, namely, PMNJ0948+0022 and PKS1502+036. Mid-infrared variability studies of these objects would be a contribution to the literature. With this as motivation, the present work is focused on studying a final sample of 256 objects in general and the Seyferts in particular. Owing to the fact that the classification is automated, SDSS has miclassified these objects into quasars, galaxies, and stars. Reasons for the misclassification are explained in this work. The variability analysis of these objects is done using the method of flux amplitude variability and excess variance. The sample consists of observations in both W1 and W2 bands. PMN J0948+0022 is observed between MJD from 57154.79 to 58810.57. PKS 1502+036 is observed between MJD from 57232.42 to 58517.11, which amounts to a period of over six years. The data is divided into different epochs spanning not more than 1.2 days. In all the epochs, the sources are found to be variable in both W1 and W2 bands. This confirms that the object is variable in mid-infrared wavebands in both long and short timescales. Also, the sources are observed for color variability. Objects either show a bluer when brighter trend (BWB) or a redder when brighter trend (RWB). The possible claim for the object to be BWB (present objects) is that the longer wavelength radiation emitted by the source can be suppressed by the high-energy radiation from the central source. Another result is that the smallest radius of the emission source is one day since the epoch span used in this work is one day. The mass of the black holes at the centers of these sources is found to be less than or equal to 108 solar masses, respectively.

Keywords: active galaxies, variability, Seyfert galaxies, SDSS, WISE

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Authors: A. Kavya Sri, K. Sai Vineela, R. Vanitha, S. Rohith

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Large pieces of ice that break from the glaciers are known as icebergs. The threat that icebergs pose to navigation, production of offshore oil and gas services, and underwater pipelines makes their detection crucial. In this project, an automated iceberg tracking method using deep learning techniques and satellite images of icebergs is to be developed. With a temporal resolution of 12 days and a spatial resolution of 20 m, Sentinel-1 (SAR) images can be used to track iceberg drift over the Southern Ocean. In contrast to multispectral images, SAR images are used for analysis in meteorological conditions. This project develops a web-based graphical user interface to detect and track icebergs using sentinel-1 images. To track the movement of the icebergs by using temporal images based on their latitude and longitude values and by comparing the center and area of all detected icebergs. Testing the accuracy is done by precision and recall measures.

Keywords: synthetic aperture radar (SAR), icebergs, deep learning, spatial resolution, temporal resolution

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Authors: Abigail Vallance

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Current research shows a correlation between declining mental health in the United States and the effect of COVID-19 on young adults and adolescents. Anxiety and depression are the two most common psychiatric illnesses, which are also the leading impediments to academic success. Spending six hours a day or more using computers is associated with higher risks of depression, with this time constraint pervasive even in present-day academia. Along with many hours on the computer, common issues COVID-19 had on students’ academic performance during online school included technical difficulties, poor support services, and difficulty adapting to online learning. Given the volume of requirements with unrealistic deadlines, and despite experiencing COVID-19, students showed an increase in their levels of anxiety. Besides the prevalent effect of COVID-19 on mental health, many studies show a correlation between mental health, COVID-19, academia, and sports performance. Academic research showed that negative self-talk, in relation to one’s self-efficacy, correlated with negative academic performance. Research showed that students who reported negative self-efficacy when test-taking led to negative test results. Furthermore, in sports performance, negative effects were found when athletes engage in negative self-talk. Overall, motivational self-talk, by oneself and through teammates and coaches, correlated with better performance than regular self-talk in sports. In relation to sports performance, the COVID-19 pandemic canceled complete sports seasons for millions of adolescents across the country. Many student-athletes use their sport to release emotions and escape from their mental health, but this was taken away. The purpose of this study is to address the current increase in mental health diagnoses in adolescents, including suicide rates after the COVID-19 pandemic began in 2020.This literature analysis is actively being studied.

Keywords: self-talk, COVID-19, mental health, adolescents

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Authors: Arwa Khannoussi, Alexandru-Liviu Olteanu, Pritesh Narayan, Catherine Dezan, Jean-Philippe Diguet, Patrick Meyer, Jacques Petit-Frere

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Due to the nature of autonomous Unmanned Aerial Vehicles (UAV) missions, it is important that the decisions of a UAV stay consistent with the priorities of an operator, while at the same time allowing them to be easily audited and explained. We propose a multi-layer decision engine that integrates the operator (human) preferences by using the Multi-Criteria Decision Aiding (MCDA) methods. A software implementation of a UAV simulator and of the decision engine is presented to highlight the advantage of using such techniques on high-level decisions. We demonstrate that, with such a preference-based decision engine, the decisions of the UAV are compatible with the priorities of the operator, which in turn increases her/his confidence in its autonomous behavior.

Keywords: autonomous UAV, multi-criteria decision aiding, multi-layers decision engine, operator's preferences, traceable decisions, UAV simulation

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Authors: M. Rojo, M. Blanco, T. Lacruz, S. Solano, L. Beltran, M. Graell, A. R. Sepulveda.

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There is an association between an early accumulation of Stressful Life Events (SLE) during childhood and various physical and psychological health complications. However, there are only a few studies on this topic in children and adolescents with overweight or obesity. The general aim of the study was to evaluate the accumulation and type of SLE in 200 children from 8 to 12 years old and analyze the relationship with their emotional well-being and weight status (obesity, overweight and normal weight). The children and their families completed an interview. The evaluated variables that are included in this study are sociodemographic measures, medical/psychological history, anthropometric measures (BMI, z-BMI), and psychological variables (children's clinical interview K-SADS-PL(Schedule for Affective Disorders and Schizophrenia for School-Age Children Present and Lifetime Version) and battery of questionnaires). Results: Children with overweight and obesity accumulate more stressful events from an early age and have a significantly higher percentage of psychiatric diagnoses, compared to their peers with normal weight. Presenting a child psychiatric disorder is related to greater z-BMI and the total number of SLE (p < 0.001). A higher z-BMI is also related to a greater number of stressful events during childhood. There is also a positive and significant relationship between the total number of SLE and worse emotional well-being (higher levels of anxious and depressive symptoms and low self-esteem of children) (p < 0.01). Conclusion: Children with overweight and obesity grow up in a family, school, and social context where more stressors are accumulated. This is also directly associated with worse emotional well-being. It is necessary to implement multidisciplinary prevention and intervention strategies in different changes (school, family, and health). This study is included in a project funded by the Ministry of Innovation and Science (PSI2011-23127).

Keywords: childhood obesity, emotional well-being, psychopathology, stressful life events

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Authors: Sharon Hovav, Ilya Levner, Oren Nahum, Istvan Szabo

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In modern large-scale logistic centers (e.g., big automated warehouses), complex logistic operations performed by human staff (pickers) need to be coordinated with the operations of automated facilities (robots, conveyors, cranes, lifts, flow racks, etc.). The efficiency of advanced logistic centers strongly depends on optimizing picking technologies in synch with the facility/product layout, as well as on optimal distribution of material flows (products) in the system. The challenge is to develop a mathematical operations research (OR) tool that will optimize system cost-effectiveness. In this work, we propose a model that describes an automatic logistic center consisting of a set of workstations located at several galleries (floors), with each station containing a known number of flow racks. The requirements of each product and the working capacity of stations served by a given set of workers (pickers) are assumed as predetermined. The goal of the model is to maximize system efficiency. The proposed model includes two echelons. The first is the setting of the (optimal) number of workstations needed to create the total processing/logistic system, subject to picker capacities. The second echelon deals with the assignment of the products to the workstations and flow racks, aimed to achieve maximal throughputs of picked products over the entire system given picker capacities and budget constraints. The solutions to the problems at the two echelons interact to balance the overall load in the flow racks and maximize overall efficiency. We have developed an operations research model within each echelon. In the first echelon, the problem of calculating the optimal number of workstations is formulated as a non-standard bin-packing problem with capacity constraints for each bin. The problem arising in the second echelon is presented as a constrained product-workstation-flow rack assignment problem with non-standard mini-max criteria in which the workload maximum is calculated across all workstations in the center and the exterior minimum is calculated across all possible product-workstation-flow rack assignments. The OR problems arising in each echelon are proved to be NP-hard. Consequently, we find and develop heuristic and approximation solution algorithms based on exploiting and improving local optimums. The LC model considered in this work is highly dynamic and is recalculated periodically based on updated demand forecasts that reflect market trends, technological changes, seasonality, and the introduction of new items. The suggested two-echelon approach and the min-max balancing scheme are shown to work effectively on illustrative examples and real-life logistic data.

Keywords: logistics center, product-workstation, assignment, maximum performance, load balancing, fast algorithm

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Authors: Shayma Mohammed, Adel Trabelsi

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Radiopharmacy is the art of preparing high-quality, radioactive, medicinal products for use in diagnosis and therapy. Radiopharmaceuticals unlike normal medicines, this dual aspect (radioactive, medical) makes their management highly critical. One of the most convincing applications of modern technologies is the ability to delegate the execution of repetitive tasks to programming scripts. Automation has found its way to the most skilled jobs, to improve the company's overall performance by allowing human workers to focus on more important tasks than document filling. This project aims to contribute to implement a comprehensive system to insure rigorous management of radiopharmaceuticals through the use of a platform that links the Nuclear Medicine Service Management System to the Nuclear Radio-pharmacy Management System in accordance with the recommendations of World Health Organization (WHO) and International Atomic Energy Agency (IAEA). In this project we attempt to build a web application that targets radiopharmacies, the platform is built atop the inherently compatible web stack which allows it to work in virtually any environment. Different technologies are used in this project (PHP, Symfony, MySQL Workbench, Bootstrap, Angular 7, Visual Studio Code and TypeScript). The operating principle of the platform is mainly based on two parts: Radiopharmaceutical Backoffice for the Radiopharmacian, who is responsible for the realization of radiopharmaceutical preparations and their delivery and Medical Backoffice for the Doctor, who holds the authorization for the possession and use of radionuclides and he/she is responsible for ordering radioactive products. The application consists of sven modules: Production, Quality Control/Quality Assurance, Release, General Management, References, Transport and Stock Management. It allows 8 classes of users: The Production Manager (PM), Quality Control Manager (QCM), Stock Manager (SM), General Manager (GM), Client (Doctor), Parking and Transport Manager (PTM), Qualified Person (QP) and Technical and Production Staff. Digital platform bringing together all players involved in the use of radiopharmaceuticals and integrating the stages of preparation, production and distribution, Web technologies, in particular, promise to offer all the benefits of automation while requiring no more than a web browser to act as a user client, which is a strength because the web stack is by nature multi-platform. This platform will provide a traceability system for radiopharmaceuticals products to ensure the safety and radioprotection of actors and of patients. The new integrated platform is an alternative to write all the boilerplate paperwork manually, which is a tedious and error-prone task. It would minimize manual human manipulation, which has proven to be the main source of error in nuclear medicine. A codified electronic transfer of information from radiopharmaceutical preparation to delivery will further reduce the risk of maladministration.

Keywords: automated system, management, radiopharmacy, technical papers

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Authors: Dorothy Chyung

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In the psychiatric interview, the mental status exam begins with an assessment of the patient's appearance, noting aspects such as grooming and hygiene. However, it is not well established whether further examination of a patient's attire can provide further useful information. The popular assumption is that those who are mentally unwell will manifest this in unusual clothing. In the moral treatment of the 19th century, proper clothing was also seen as a pivotal therapeutic concern. This project examines assumptions about clothing, both as a reflection of and treatment for psychopathology. The methodology considers the opinions expressed in 19th century art and journals, as well as asylum rules, in comparison to contemporary psychiatric practice and research evidence. Per moral treatment in the 19th century, self-discipline and a proper environment would cure insanity. Madness was evident in the opposite of these ideals—such as ragged or ‘improper’ clothing—and rules about attire delineated the most correct (i.e. sane) ways to dress. These rules applied not only for the patients but also for staff. Despite these ideals, accusations were made that asylums, in fact, dressed patients to look more mentally unwell and further removed patients’ agency. Current practice in psychiatric hospitals retains remnants of moral treatment. Patients are expected to dress ‘appropriately’ while retaining some choice to build self-esteem, with arguments about safety being used to justify the removal of choice. Meanwhile, staff is expected to dress professionally and as role models, based on the assumption that conservative dress is least pathological. Research on this subject is limited, and there is little evidence that discrete psychiatric diagnoses manifest in the particular dress, nor that conservative dress would result in a reduction in pathology. Dressing unusually has become a privilege granted only to those without association with mental illness.

Keywords: fashion, history of psychiatry, medical humanities, mental health treatment

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Authors: Priyanka Verma, Vijaya Dixit, Rishabh Bajpai

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This research paper is aimed at selecting appropriate material handling equipment among the given choices so that the automation level in material handling can be enhanced. This work is a practical case scenario of material handling systems in consumer electronic appliances manufacturing organization. The choices of material handling equipment among which the decision has to be made are Automated Guided Vehicle’s (AGV), Autonomous Mobile Robots (AMR), Overhead Conveyer’s (OC) and Battery Operated Trucks/Vehicle’s (BOT). There is a need of attaining a certain level of automation in order to reduce human interventions in the organization. This requirement of achieving certain degree of automation can be attained by material handling equipment’s mentioned above. The main motive for selecting above equipment’s for study was solely based on corporate financial strategy of investment and return obtained through that investment made in stipulated time framework. Since the low cost automation with respect to material handling devices has to be achieved hence these equipment’s were selected. Investment to be done on each unit of this equipment is less than 20 lakh rupees (INR) and the recovery period is less than that of five years. Fuzzy analytic hierarchic process (FAHP) is applied here for selecting equipment where the four choices are evaluated on basis of four major criteria’s and 13 sub criteria’s, and are prioritized on the basis of weight obtained. The FAHP used here make use of triangular fuzzy numbers (TFN). The inability of the traditional AHP in order to deal with the subjectiveness and impreciseness in the pair-wise comparison process has been improved in the FAHP. The range of values for general rating purposes for all decision making parameters is kept between 0 and 1 on the basis of expert opinions captured on shop floor. These experts were familiar with operating environment and shop floor activity control. Instead of generating exact value the FAHP generates the ranges of values to accommodate the uncertainty in decision-making process. The four major criteria’s selected for the evaluation of choices of material handling equipment’s available are materials, technical capabilities, cost and other features. The thirteen sub criteria’s listed under these following four major criteria’s are weighing capacity, load per hour, material compatibility, capital cost, operating cost and maintenance cost, speed, distance moved, space required, frequency of trips, control required, safety and reliability issues. The key finding shows that among the four major criteria selected, cost is emerged as the most important criteria and is one of the key decision making aspect on the basis of which material equipment selection is based on. While further evaluating the choices of equipment available for each sub criteria it is found that AGV scores the highest weight in most of the sub-criteria’s. On carrying out complete analysis the research shows that AGV is the best material handling equipment suiting all decision criteria’s selected in FAHP and therefore it is beneficial for the organization to carry out automated material handling in the facility using AGV’s.

Keywords: fuzzy analytic hierarchy process (FAHP), material handling equipment, subjectiveness, triangular fuzzy number (TFN)

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Authors: A. Ibovi Mouondayi, S. Zaher, K. Nassar, S. Janani

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Introduction: Osteoarticular ultrasound has become an essential tool for the investigation and monitoring of osteoarticular pathologies for rheumatologists. It is performed in the clinic, cheap to access than other imaging technics. Important anatomical sites of inflammation in inflammatory diseases such as synovium, tendon sheath, and enthesis are easily identifiable on ultrasound. Objective: The objective of this study was to evaluate the importance of ultrasound for rheumatologists in the development of diagnoses of inflammatory rheumatism in cases of uncertain clinical presentation. Material and Methods: This is a retrospective study conducted in our department and carried out over a period of 30 months from January 2020 to June 2022. We included all patients with inflammatory arthralgia without clinical arthritis. Patients' data were collected through a patient operating system. Results: A total of 35 patients were identified, made up of 4 men and 31 women, with a sex ratio M/F of 0.12. The average age of the patients was 48.8 years, with extremes ranging from 17 years to 83 years. All patients had inflammatory polyarthralgia for an average of 9.3 years. Only two patients had suspicious synovitis on clinical examination. 91.43% of patients had a positive inflammatory assessment with an average CRP of 22.2 mg/L. Rheumatoid factor (RF) was present in 45.7% of patients and anti-CCP in 48.57%, with respective averages of 294.43 and 314.63 international units/mL. Radiographic lesions were found in 54% of patients. Osteoarticular ultrasound was performed in all these patients. Subclinical synovitis was found in 60% of patients, including 23% Doppler positive. Tenosynovitis was found in 11% of patients. Enthesitis was objectified in 3% of patients. Rheumatoid arthritis (RA) was retained in 40% of patients; psoriatic arthritis in 6% of patients, hydroxyapatite arthritis, and osteoarthritis in 3% each. Conclusion: Osteoarticular ultrasound has been an essential tool in the practice of rheumatology in recent years. It is for diagnostic purposes in chronic inflammatory rheumatism as well as in degenerative rheumatism and crystal induced arthropathies, but also essential in the follow-up of patients in rheumatology.

Keywords: ultrasound, skeletal, rheumatoid arthritis, arthralgia

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Authors: B. S. Harish, M. B. Revanasiddappa

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Due to rapid growth of text documents in digital form, automated text classification has become an important research in the last two decades. The major challenge of text document representations are high dimension, sparsity, volume and semantics. Since the terms are only features that can be found in documents, selection of good terms (features) plays an very important role. In text classification, feature selection is a strategy that can be used to improve classification effectiveness, computational efficiency and accuracy. In this paper, we present a quantitative analysis of most widely used feature selection (FS) methods, viz. Term Frequency-Inverse Document Frequency (tf
idf ), Mutual Information (MI), Information Gain (IG), CHISquare (x2), Term Frequency-Relevance Frequency (tf
rf ), Term Strength (TS), Ambiguity Measure (AM) and Symbolic Feature Selection (SFS) to classify text documents. We evaluated all the feature selection methods on standard datasets like 20 Newsgroups, 4 University dataset and Reuters-21578.

Keywords: classifiers, feature selection, text classification

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Authors: Sogand Barghi

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The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

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Authors: Chowduru Ramachandra Sharma, Shatunjay Rawat

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A false positive state is when the IDS/IPS identifies an activity as an attack, but the activity is acceptable behavior in the system. False positives in a Network Intrusion Detection System ( NIDS ) is an issue because they desensitize the administrator. It wastes computational power and valuable resources when rules are not tuned properly, which is the main issue with anomaly NIDS. Furthermore, most false positives reduction techniques are not performed during the real-time of attempted intrusions; instead, they have applied afterward on collected traffic data and generate alerts. Of course, false positives detection in ‘offline mode’ is tremendously valuable. Nevertheless, there is room for improvement here; automated techniques still need to reduce False Positives in real-time. This paper uses the Snort signature detection model to redirect the alerted attacks to Honeypots and verify attacks.

Keywords: honeypot, TPOT, snort, NIDS, honeybird, iptables, netfilter, redirection, attack detection, docker, snare, tanner

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Authors: Ramuel Spirituel Mattathiah A. San Juan, Neil Ambasing

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Background: Reversal of vision metamorphopsia is a transient form of metamorphopsia described as an upside-down alteration of the visual field in the coronal plane. Patients would describe objects, such as cups, upside down, but the tea would not spill, and people would walk on their heads. It is extremely rare as a stable finding, lasting days or weeks. We report a case wherein this type of metamorphopsia occurred only in written words and lasted for six months. Objective: To the best of our knowledge, we report the first rare occurrence of reversal of vision metamorphopsia described as inverted words as the sole initial presentation of an underlying stroke. Case Presentation: We report a 59-year-old male with poorly controlled hypertension and diabetes mellitus who presented with a 3-day history of difficulty reading, described as the words were turned upside down as if the words were inverted horizontally then with the progression of deficits such as right homonymous hemianopia and achromatopsia, prosopagnosia. Cranial magnetic resonance imaging (MRI) revealed an acute infarct on the left posterior cerebral artery territory. Follow-up after six months revealed improvement of the visual field cut but with the persistence of the higher cortical function deficits. Conclusion: We report the first rare occurrence of metamorphopsia described as purely inverted words as the sole initial presentation of an underlying stroke. The differential diagnoses of a patient presenting with text reversal metamorphopsia should include stroke in the occipitotemporal areas. It further expands the landscape of metamorphopsias due to its exclusivity to written words and prolonged duration. Knowing these clinical features will help identify the lesion locus and improve subsequent stroke care, especially in time-bound management like intravenous thrombolysis.

Keywords: rare presentation, text reversal metamorphopsia, ischemic stroke, stroke

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Authors: Simon Samwel Msanjila

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Networked environments which provides platforms and environments for business organizations are configured in different forms depending on many factors including life time, member characteristics, communication structure, and business objectives, among others. With continuing advances in digital technologies the distance has become a less barrier for business minded collaboration among organizations. With the need and ease to make business collaborate nowadays organizations are sometimes forced to co-work with others that are either unknown or less known to them in terms of history and performance. A promising approach for sustaining established collaboration has been establishment of trust relationship among organizations based on assessed trustworthiness for each participating organization. It has been stated in research that trust in organization is dynamic and thus assessment of trust level must address such dynamic nature. This paper assess relevant aspects of trust and applies the concepts to propose a semi-automated system for assessing the Sustainability and Evolution of trust in organizations participating in specific objective in a networked organizations environment.

Keywords: trust evolution, trust sustainability, networked organizations, dynamic trust

Procedia PDF Downloads 406