Search results for: computer aided detection and diagnosis

Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

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A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

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Authors: Michael Leontiev, Danil Zhilikov, Dmitry Lobanov, Lenar Klimov, Vyacheslav Chertan, Daniel Bobrov, Vladislav Maslov, Vasilii Vologdin, Ksenia Balabaeva

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Pharmaceutical manufacturing is a complex process, where each stage requires a high level of safety and sterility. Personal Protective Equipment (PPE) is used for this purpose. Despite all the measures of control, the human factor (improper PPE wearing) causes numerous losses to human health and material property. This research proposes a solid computer vision system for ensuring safety in pharmaceutical laboratories. For this, we have tested a wide range of state-of-the-art object detection methods. Composing previously obtained results in this sphere with our own approach to this problem, we have reached a high accuracy ([email protected]) ranging from 0.77 up to 0.98 in detecting all the elements of a common set of PPE used in pharmaceutical laboratories. Our system is a step towards safe medicine production.

Keywords: sterility and safety in pharmaceutical development, personal protective equipment, computer vision, object detection, monitoring in pharmaceutical development, PPE

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Authors: Ibrahim Aly, Manal Ahmed, Mahmoud M. El-Shall

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Filariasis is a parasitic disease caused by small roundworms. The filarial worms are transmitted and spread by blood-feeding black flies and mosquitoes. Lymphatic filariasis (Elephantiasis) is caused by Wuchereriabancrofti, Brugiamalayi, and Brugiatimori. Elimination of Lymphatic filariasis necessitates an increasing demand for valid, reliable, and rapid diagnostic kits. Nanodiagnostics involve the use of nanotechnology in clinical diagnosis to meet the demands for increased sensitivity, specificity, and early detection in less time. The aim of this study was to evaluate the nano-based enzymelinked immunosorbent assay (ELISA) and novel nano-chip card as a rapid test for detection of filarial antigen in serum samples of human filariasis in comparison with traditional -ELISA. Serum samples were collected from an infected human with filarial gathered across Egypt's governorates. After receiving informed consenta total of 45 blood samples of infected individuals residing in different villages in Gharbea governorate, which isa nonendemic region for bancroftianfilariasis, healthy persons living in nonendemic locations (20 persons), as well as sera from 20 other parasites, affected patients were collected. The microfilaria was checked in thick smears of 20 µl night blood samples collected during 20-22 hrs. All of these individuals underwent the following procedures: history taking, clinical examination, and laboratory investigations, which included examination of blood samples for microfilaria using thick blood film and serological tests for detection of the circulating filarial antigen using polyclonal antibody- ELISA, nano-based ELISA, and nano-chip card. In the present study, a recently reported polyoclonal antibody specific to tegumental filarial antigen was used in developing nano-chip card and nano-ELISA compared to traditional ELISA for the detection of circulating filarial antigen in sera of patients with bancroftianfilariasis. The performance of the ELISA was evaluated using 45 serum samples. The ELISA was positive with sera from microfilaremicbancroftianfilariasis patients (n = 36) with a sensitivity of 80 %. Circulating filarial antigen was detected in 39/45 patients who were positive for circulating filarial antigen using nano-ELISA with a sensitivity of 86.6 %. On the other hand, 42 out of 45 patients were positive for circulating filarial antigen using nano-chip card with a sensitivity of 93.3%.In conclusion, using a novel nano-chip assay could potentially be a promising alternative antigen detection test for bancroftianfilariasis.

Keywords: lymphatic filariasis, nanotechnology, rapid diagnosis, elisa technique

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Authors: Dayo Samuel Banjo, Connice Trimmingham, Niloofar Yousefi, Nitin Agarwal

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Technological advancement and its omnipresent connection have pushed humans past the boundaries and limitations of a computer screen, physical state, or geographical location. It has provided a depth of avenues that facilitate human-computer interaction that was once inconceivable such as audio and body language detection. Given the complex modularities of emotions, it becomes vital to study human-computer interaction, as it is the commencement of a thorough understanding of the emotional state of users and, in the context of social networks, the producers of multimodal information. This study first acknowledges the accuracy of classification found within multimodal emotion detection systems compared to unimodal solutions. Second, it explores the characterization of multimedia content produced based on their emotions and the coherence of emotion in different modalities by utilizing deep learning models to classify emotion across different modalities.

Keywords: affective computing, deep learning, emotion recognition, multimodal

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Authors: Deepanjali Gurav, Kun Qian

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In-vitro metabolic diagnosis relies on designed materials-based analytical platforms for detection of selected metabolites in biological samples, which has a key role in disease detection and therapeutic evaluation in clinics. However, the basic challenge deals with developing a simple approach for metabolic analysis in bio-samples with high sample complexity and low molecular abundance. In this work, we report a designer plasmonic nanoshells based platform for direct detection of small metabolites in clinical samples for in-vitro metabolic diagnostics. We first synthesized a series of plasmonic core-shell particles with tunable nanoshell structures. The optimized plasmonic nanoshells as new matrices allowed fast, multiplex, sensitive, and selective LDI MS (Laser desorption/ionization mass spectrometry) detection of small metabolites in 0.5 μL of bio-fluids without enrichment or purification. Furthermore, coupling with isotopic quantification of selected metabolites, we demonstrated the use of these plasmonic nanoshells for disease detection and therapeutic evaluation in clinics. For disease detection, we identified patients with postoperative brain infection through glucose quantitation and daily monitoring by cerebrospinal fluid (CSF) analysis. For therapeutic evaluation, we investigated drug distribution in blood and CSF systems and validated the function and permeability of blood-brain/CSF-barriers, during therapeutic treatment of patients with cerebral edema for pharmacokinetic study. Our work sheds light on the design of materials for high-performance metabolic analysis and precision diagnostics in real cases.

Keywords: plasmonic nanoparticles, metabolites, fingerprinting, mass spectrometry, in-vitro diagnostics

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Authors: Mohamed A. Alhammad, Hadia A. Abou-Donia, Mona H. Hashish, Mohamed N. Massoud

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Background: Rotavirus is the leading etiologic agent of severe diarrheal disease in infants and young children worldwide. The present study was aimed 1) to detect rotavirus infection as a cause of diarrhoea among children under 5 years of age using the two serological methods (ELISA and LA) and the PCR technique (2) to evaluate the three methodologies used for human RV detection in stool samples. Materials and Methods: This study was carried out on 247 children less than 5 years old, diagnosed clinically as acute gastroenteritis and attending Alexandria University Children Hospital at EL-Shatby. Rotavirus antigen was screened by ELISA and LA tests in all stool samples, whereas only 100 samples were subjected to RT-PCR method for detection of rotavirus RNA. Results: Out of the 247 studied cases with diarrhoea, rotavirus antigen was detected in 83 (33.6%) by ELISA and 73 (29.6%) by LA, while the 100 cases tested by RT-PCR showed that 44% of them had rotavirus RNA. Rotavirus diarrhoea was significantly presented with a marked seasonal peak during autumn and winter (61.4%). Conclusion: The present study confirms the huge burden of rotavirus as a major cause of acute diarrhoea in Egyptian infants and young children. It was concluded that; LA is equal in sensitivity to ELISA, ELISA is more specific than LA, and RT-PCR is more specific than ELISA and LA in diagnosis of rotavirus infection.

Keywords: rotavirus, diarrhea, immunoenzyme techniques, latex fixation tests, RT-PCR

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Authors: Jeewan Rai, Kinzang, Yeshi Jigme Choden

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Accumulation of dust on solar panels impacts the overall efficiency and the amount of energy they produce. While various techniques exist for detecting dust to schedule cleaning, many of these methods use MATLAB image processing tools and other licensed software, which can be financially burdensome. This study will investigate the efficiency of a free open-source computer vision library using the YOLO algorithm. The proposed approach has been tested on images of solar panels with varying dust levels through an experiment setup. The experimental findings illustrated the effectiveness of using the YOLO-based image classification method and the overall dust detection approach with an accuracy of 90% in distinguishing between clean and dusty panels. This open-source solution provides a cost effective and accessible alternative to commercial image processing tools, offering solutions for optimizing solar panel maintenance and enhancing energy production.

Keywords: YOLO, openCV, dust detection, solar panels, computer vision, image processing

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: neural network, motion detection, signature detection, convolutional neural network

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Authors: Huang Yajing

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Context: Computer-aided language learning (CALL) in China has undergone significant development over the past few decades, with distinct stages marking its evolution. This paper aims to provide a comprehensive review of the practice and research in this field in China, tracing its journey from the early stages of audio-visual education to the current multimedia network integration stage. Research Aim: The study aims to analyze the historical progression of CALL in China, identify key developments in the field, and provide recommendations for enhancing CALL practices in the future. Methodology: The research employs document analysis and literature review to synthesize existing knowledge on CALL in China, drawing on a range of sources to construct a detailed overview of the evolution of CALL practices and research in the country. Findings: The review highlights the significant advancements in CALL in China, showcasing the transition from traditional audio-visual educational approaches to the current integrated multimedia network stage. The study identifies key milestones, technological advancements, and theoretical influences that have shaped CALL practices in China. Theoretical Importance: The evolution of CALL in China reflects not only technological progress but also shifts in educational paradigms and theories. The study underscores the significance of cognitive psychology as a theoretical underpinning for CALL practices, emphasizing the learner's active role in the learning process. Data Collection and Analysis Procedures: Data collection involved extensive review and analysis of documents and literature related to CALL in China. The analysis was carried out systematically to identify trends, developments, and challenges in the field. Questions Addressed: The study addresses the historical development of CALL in China, the impact of technological advancements on teaching practices, the role of cognitive psychology in shaping CALL methodologies, and the future outlook for CALL in the country. Conclusion: The review provides a comprehensive overview of the evolution of CALL in China, highlighting key stages of development and emerging trends. The study concludes by offering recommendations to further enhance CALL practices in the Chinese context.

Keywords: English education, educational technology, computer-aided language teaching, applied linguistics

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Authors: Sheema Shuja Khattak, Gule Saman, Imran Khan, Abdus Salam

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Image segmentation plays an important role in medical imaging applications. Therefore, accurate methods are needed for the successful segmentation of medical images for diagnosis and detection of various diseases. In this paper, we have used maximum entropy to achieve image segmentation. Maximum entropy has been calculated using Shannon, Renyi, and Tsallis entropies. This work has novelty based on the detection of skin lesion caused by the bite of a parasite called Sand Fly causing the disease is called Cutaneous Leishmaniasis.

Keywords: shannon, maximum entropy, Renyi, Tsallis entropy

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Authors: M. Dasgupta, S. Banerjee

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Content Based Image Retrieval (CBIR) coupled with Case Based Reasoning (CBR) is a paradigm that is becoming increasingly popular in the diagnosis and therapy planning of medical ailments utilizing the digital content of medical images. This paper presents a survey of some of the promising approaches used in the detection of abnormalities in retina images as well in mammographic screening and detection of regions of interest in MRI scans of the brain. We also describe our proposed algorithm to detect hard exudates in fundus images of the retina of Diabetic Retinopathy patients.

Keywords: case based reasoning, exudates, retina image, similarity based retrieval

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Authors: E. Sobhani-Tehrani, K. Khorasani, N. Meskin

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This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPE) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. Two NPE structures including series-parallel and parallel are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the NPEs to systems with partial-state measurement.

Keywords: hybrid fault diagnosis, dynamic neural networks, nonlinear systems, fault tolerant observer

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Authors: Kenan Morani, Esra Kaya Ayana

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This study aimed to develop a novel pipeline for COVID-19 detection using a large and rigorously annotated database of computed tomography (CT) images. The pipeline consists of UNet-based segmentation, lung extraction, and a classification part, with the addition of optional slice removal techniques following the segmentation part. In this work, a batch normalization was added to the original UNet model to produce lighter and better localization, which is then utilized to build a full pipeline for COVID-19 diagnosis. To evaluate the effectiveness of the proposed pipeline, various segmentation methods were compared in terms of their performance and complexity. The proposed segmentation method with batch normalization outperformed traditional methods and other alternatives, resulting in a higher dice score on a publicly available dataset. Moreover, at the slice level, the proposed pipeline demonstrated high validation accuracy, indicating the efficiency of predicting 2D slices. At the patient level, the full approach exhibited higher validation accuracy and macro F1 score compared to other alternatives, surpassing the baseline. The classification component of the proposed pipeline utilizes a convolutional neural network (CNN) to make final diagnosis decisions. The COV19-CT-DB dataset, which contains a large number of CT scans with various types of slices and rigorously annotated for COVID-19 detection, was utilized for classification. The proposed pipeline outperformed many other alternatives on the dataset.

Keywords: classification, computed tomography, lung extraction, macro F1 score, UNet segmentation

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Authors: Yousef Farhaoui

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An IDS is a tool which is used to improve the level of security. We present in this paper different architectures of IDS. We will also discuss measures that define the effectiveness of IDS and the very recent works of standardization and homogenization of IDS. At the end, we propose a new model of IDS called BiIDS (IDS Based on the two principles of detection) for securing web servers and applications by the Intrusion Detection System (IDS).

Keywords: intrusion detection, architectures, characteristic, tools, security, web server

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Authors: Alireza Pour Ebrahimi, Vahid Abasi

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Recently by the extension of internet usage, Research on the intrusion detection system takes a significant importance. Many of improvement systems prevent internal and external network attacks by providing security through firewalls and antivirus. In recently years, intrusion detection systems gradually turn from host-based systems and depend on O.S to the distributed systems which are running on multiple O.S. In this work, by considering the diversity of computer networks whit respect to structure, architecture, resource, services, users and also security goals requirement a fully distributed collaborative intrusion detection system based on peer to peer architecture is suggested. in this platform each partner device (matched device) considered as a peer-to-peer network. All transmitted information to network are visible only for device that use security scanning of a source. Experimental results show that the distributed architecture is significantly upgradeable in respect to centralized approach.

Keywords: network, intrusion detection system, peer to peer, internal and external network

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Authors: Moftah BinHasan

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Induction motors (IM) are vital components in industrial processes whose failure may yield to an unexpected interruption at the industrial plant, with highly incurred consequences in costs, product quality, and safety. Among different detection approaches proposed in the literature, that based on stator current monitoring termed as Motor Current Signature Analysis (MCSA) is the most preferred. MCSA is advantageous due to its non-invasive properties. The popularity of motor current signature analysis comes from being that the current consists of motor harmonics, around the supply frequency, which show some properties related to different situations of healthy and faulty conditions. One of the techniques used with machine line current resorts to spectrum analysis. Besides discussing the fundamentals of MCSA and its applications in the condition monitoring arena, this paper shows a summary of the most frequent faults and their consequence signatures on the stator current spectrum of an induction motor. In addition, this article presents different case studies of induction motor fault diagnosis. These faults were seeded in the machine which was run for more than an hour for each test before the results were recorded for the faulty situations. These results are then compared with those for the healthy cases that were recorded earlier.

Keywords: induction motor, condition monitoring, fault diagnosis, MCSA, rotor, stator, bearing, eccentricity

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Authors: Pratham Madnur, Prathamkumar Shetty, Sneha Varur, Gouri Parashetti

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In order to overcome conventional obstacles, this research paper investigates novel approaches for cricket pitch and ball detection that make use of cutting-edge technologies. The research integrates OpenCV for pitch inspection and modifies the YOLOv8 model for cricket ball detection in order to overcome the shortcomings of manual pitch assessment and traditional ball detection techniques. To ensure flexibility in a range of pitch environments, the pitch detection method leverages OpenCV’s color space transformation, contour extraction, and accurate color range defining features. Regarding ball detection, the YOLOv8 model emphasizes the preservation of minor object details to improve accuracy and is specifically trained to the unique properties of cricket balls. The methods are more reliable because of the careful preparation of the datasets, which include novel ball and pitch information. These cutting-edge methods not only improve cricket analytics but also set the stage for flexible methods in more general sports technology applications.

Keywords: OpenCV, YOLOv8, cricket, custom dataset, computer vision, sports

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Authors: Wajid Mumtaz, Aamir Saeed Malik, Syed Saad Azhar Ali, Mohd Azhar Mohd Yasin

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In this paper, a technique based on electroencephalogram (EEG) analysis is presented, aiming for diagnosing major depressive disorder (MDD) among a potential population of MDD patients and healthy controls. EEG is recognized as a clinical modality during applications such as seizure diagnosis, index for anesthesia, detection of brain death or stroke. However, its usability for psychiatric illnesses such as MDD is less studied. Therefore, in this study, for the sake of diagnosis, 2 groups of study participants were recruited, 1) MDD patients, 2) healthy people as controls. EEG data acquired from both groups were analyzed involving inter-hemispheric asymmetry and composite permutation entropy index (CPEI). To automate the process, derived quantities from EEG were utilized as inputs to classifier such as logistic regression (LR) and support vector machine (SVM). The learning of these classification models was tested with a test dataset. Their learning efficiency is provided as accuracy of classifying MDD patients from controls, their sensitivities and specificities were reported, accordingly (LR =81.7 % and SVM =81.5 %). Based on the results, it is concluded that the derived measures are indicators for diagnosing MDD from a potential population of normal controls. In addition, the results motivate further exploring other measures for the same purpose.

Keywords: major depressive disorder, diagnosis based on EEG, EEG derived features, CPEI, inter-hemispheric asymmetry

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Authors: A. Kachmazova, A. Shadiev, Y. Teterin, P. Yartcev

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Introduction: Biliary calculi disease (LCS) still occupies the leading position among urgent diseases of the abdominal cavity, manifesting itself from asymptomatic course to life-threatening states. Nowadays arsenal of diagnostic methods for choledocholithiasis is quite wide: ultrasound, hepatobiliscintigraphy (HBSG), magnetic resonance imaging (MRI), endoscopic retrograde cholangiography (ERCP). Among them, transabdominal ultrasound (TA ultrasound) is the most accessible and routine diagnostic method. Nowadays ERCG is the "gold" standard in diagnosis and one-stage treatment of biliary tract obstruction. However, transpapillary techniques are accompanied by serious postoperative complications (postmanipulative pancreatitis (3-5%), endoscopic papillosphincterotomy bleeding (2%), cholangitis (1%)), the lethality being 0.4%. GBSG and MRI are also quite informative methods in the diagnosis of choledocholithiasis. Small size of concrements, their localization in intrapancreatic and retroduodenal part of common bile duct significantly reduces informativity of all diagnostic methods described above, that demands additional studying of this problem. Materials and Methods: 890 patients with the diagnosis of cholelithiasis (calculous cholecystitis) were admitted to the Sklifosovsky Scientific Research Institute of Hospital Medicine in the period from August, 2020 to June, 2021. Of them 115 people with mechanical jaundice caused by concrements in bile ducts. Results: Final EUS diagnosis was made in all patients (100,0%). In all patients in whom choledocholithiasis diagnosis was revealed or confirmed after EUS, ERCP was performed urgently (within two days from the moment of its detection) as the X-ray operation room was provided; it confirmed the presence of concrements. All stones were removed by lithoextraction using Dormia basket. The postoperative period in these patients had no complications. Conclusions: EUS is the most informative and safe diagnostic method, which allows to detect choledocholithiasis in patients with discrepancies between clinical-laboratory and instrumental methods of diagnosis in shortest time, that in its turn will help to decide promptly on the further tactics of patient treatment. We consider it reasonable to include EUS in the diagnostic algorithm for choledocholithiasis. Disclosure: Nothing to disclose.

Keywords: endoscopic ultrasonography, choledocholithiasis, common bile duct, concrement, ERCP

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Authors: A. Harras, A. Tsuji, K. Terada

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Tracking of moving people has gained a matter of great importance due to rapid technological advancements in the field of computer vision. The objective of this study is to design a motion based detection and tracking multiple walking pedestrians randomly in different directions. In our proposed method, Gaussian mixture model (GMM) is used to determine moving persons in image sequences. It reacts to changes that take place in the scene like different illumination; moving objects start and stop often, etc. Background noise in the scene is eliminated through applying morphological operations and the motions of tracked people which is determined by using the Kalman filter. The Kalman filter is applied to predict the tracked location in each frame and to determine the likelihood of each detection. We used a benchmark data set for the evaluation based on a side wall stationary camera. The actual scenes from the data set are taken on a street including up to eight people in front of the camera in different two scenes, the duration is 53 and 35 seconds, respectively. In the case of walking pedestrians in close proximity, the proposed method has achieved the detection ratio of 87%, and the tracking ratio is 77 % successfully. When they are deferred from each other, the detection ratio is increased to 90% and the tracking ratio is also increased to 79%.

Keywords: automatic detection, tracking, pedestrians, counting

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Authors: Chung-Ming Lo, Chung-Chien Lee

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In many countries, the lifetime prevalence of shoulder pain is up to 70%. In America, the health care system spends 7 billion per year about the healthy issues of shoulder pain. With respect to the origin, up to 70% of shoulder pain is attributed to rotator cuff lesions This study proposed a computer-aided diagnosis (CAD) system to assist radiologists classifying rotator cuff lesions with less operator dependence. Quantitative features were extracted from the shoulder ultrasound images acquired using an ALOKA alpha-6 US scanner (Hitachi-Aloka Medical, Tokyo, Japan) with linear array probe (scan width: 36mm) ranging from 5 to 13 MHz. During examination, the postures of the examined patients are standard sitting position and are followed by the regular routine. After acquisition, the shoulder US images were drawn out from the scanner and stored as 8-bit images with pixel value ranging from 0 to 255. Upon the sonographic appearance, the boundary of each lesion was delineated by a physician to indicate the specific pattern for analysis. The three lesion categories for classification were composed of 20 cases of tendon inflammation, 18 cases of calcific tendonitis, and 18 cases of supraspinatus tear. For each lesion, second-order statistics were quantified in the feature extraction. The second-order statistics were the texture features describing the correlations between adjacent pixels in a lesion. Because echogenicity patterns were expressed via grey-scale. The grey-scale co-occurrence matrixes with four angles of adjacent pixels were used. The texture metrics included the mean and standard deviation of energy, entropy, correlation, inverse different moment, inertia, cluster shade, cluster prominence, and Haralick correlation. Then, the quantitative features were combined in a multinomial logistic regression classifier to generate a prediction model of rotator cuff lesions. Multinomial logistic regression classifier is widely used in the classification of more than two categories such as the three lesion types used in this study. In the classifier, backward elimination was used to select a feature subset which is the most relevant. They were selected from the trained classifier with the lowest error rate. Leave-one-out cross-validation was used to evaluate the performance of the classifier. Each case was left out of the total cases and used to test the trained result by the remaining cases. According to the physician’s assessment, the performance of the proposed CAD system was shown by the accuracy. As a result, the proposed system achieved an accuracy of 86%. A CAD system based on the statistical texture features to interpret echogenicity values in shoulder musculoskeletal ultrasound was established to generate a prediction model for rotator cuff lesions. Clinically, it is difficult to distinguish some kinds of rotator cuff lesions, especially partial-thickness tear of rotator cuff. The shoulder orthopaedic surgeon and musculoskeletal radiologist reported greater diagnostic test accuracy than general radiologist or ultrasonographers based on the available literature. Consequently, the proposed CAD system which was developed according to the experiment of the shoulder orthopaedic surgeon can provide reliable suggestions to general radiologists or ultrasonographers. More quantitative features related to the specific patterns of different lesion types would be investigated in the further study to improve the prediction.

Keywords: shoulder ultrasound, rotator cuff lesions, texture, computer-aided diagnosis

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Authors: Ji-Hoon Hong, Dong-Hee Kim, Nam-Uk Kim, Tai-Myoung Chung

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Smartphone users are increasing rapidly. Accordingly, many companies are running BYOD (Bring Your Own Device: Policies to bring private-smartphones to the company) policy to increase work efficiency. However, smartphones are always under the threat of malware, thus the company network that is connected smartphone is exposed to serious risks. Most smartphone malware detection techniques are to perform an independent detection (perform the detection of a single target application). In this paper, we analyzed a variety of intrusion detection techniques. Based on the results of analysis propose an agent using the network IDS.

Keywords: android malware detection, software-defined network, interaction environment, android malware detection, software-defined network, interaction environment

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Authors: Mohmmad A. Obeidat, Amjed Al Fahoum, Ayman M. Mansour

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The recognition of an abnormal activity of the brain functionality is a vital issue. To determine the type of the abnormal activity either a brain image or brain signal are usually considered. Imaging localizes the defect within the brain area and relates this area with somebody functionalities. However, some functions may be disturbed without affecting the brain as in epilepsy. In this case, imaging may not provide the symptoms of the problem. A cheaper yet efficient approach that can be utilized to detect abnormal activity is the measurement and analysis of the electroencephalogram (EEG) signals. The main goal of this work is to come up with a new method to facilitate the classification of the abnormal and disorder activities within the brain directly using EEG signal processing, which makes it possible to be applied in an on-line monitoring system.

Keywords: EEG, wavelet, epilepsy, detection

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Authors: S. Zolghadri, M. Naderi, H. Yousefnia, B. Alirzapour, A. R. Jalilian, A. Ramazani

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Nowadays, 68Ga-DOTATOC has been known as a potential agent for the detection of neuroendocrine tumours and it has indicated higher sensitivity compared with the 111In-Octeroetide. The aim of this study was to evaluate the effectiveness of this new agent in the diagnosis of adenocarcinoma breast cancer. 68Ga-DOTATOC was prepared with the radiochemical purity of higher than 98% and by the specific activity of 39.6 TBq/mmol. 37 MBq of the complex was injected intravenously into the BULB/c mice with adenocarcinoma breast cancer. PET/CT images were acquired after 30, 60 and 90 min post injection demonstrated significant accumulation in the tumour sites. Also, considerable activity was observed in the kidney and bladder as the main routs of excretion. Generally, the results showed that 68Ga-DOTATOC can be considered as a suitable complex for diagnosis of the adenocarcinoma breast cancer using PET procedure.

Keywords: adenocarcinoma breast cancer, 68Ga, octreotide, imaging

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Authors: Boy Subirosa Sabarguna, Aria Kekalih, Irzan Nurman

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Introduction: Software in the form of Clinical Decision Support System could help students in understanding the mind set of decision-making in diagnosis and treatment at the stage of general practitioners. This could accelerate and ease the learning process which previously took place by using books and experience. Method: Gather 1000 members of the National Medical Multimedia Digital Community (NM2DC) who use the “flowchart of diagnosis and treatment” software, and analyse factors related to: display, speed in learning, convenience in learning, helpfulness and usefulness in the learning process, by using the Likert Scale through online questionnaire which will further be processed using percentage. Results and Discussions: Out of the 1000 members of NM2DC, apparently: 97.0% of the members use the software and 87.5% of them are students. In terms of the analysed factors related to: display, speed in learning, convenience in learning, helpfulness and usefulness of the software’s usage, the results indicate a 90.7% of fairly good performance. Therefore, the “Flowchart of Diagnosis and Treatment” software has helped students in understanding the decision-making of diagnosis and treatment. Conclusion: the use of “Flowchart of Diagnosis and Treatment” software indicates a positive role in helping students understand decision-making of diagnosis and treatment.

Keywords: usage, software, diagnosis and treatment, medical education

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Authors: W. J. Zhang, Y. Q. Du, M. L. Wang

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Noninvasive diagnostics of diseases via breath analysis has attracted considerable scientific and clinical interest for many years and become more and more promising with the rapid advancement in nanotechnology and biotechnology. The volatile organic compounds (VOCs) in exhaled breath, which are mainly blood borne, particularly provide highly valuable information about individuals’ physiological and pathophysiological conditions. Additionally, breath analysis is noninvasive, real-time, painless and agreeable to patients. We have developed a wireless sensor array based on single-stranded DNA (ssDNA)-decorated single-walled carbon nanotubes (SWNT) for the detection of a number of physiological indicators in breath. Eight DNA sequences were used to functionalize SWNT sensors to detect trace amount of methanol, benzene, dimethyl sulfide, hydrogen sulfide, acetone and ethanol, which are indicators of heavy smoking, excessive drinking, and diseases such as lung cancer, breast cancer, cirrhosis and diabetes. Our tests indicated that DNA functionalized SWNT sensors exhibit great selectivity, sensitivity, reproducibility, and repeatability. Furthermore, different molecules can be distinguished through pattern recognition enabled by this sensor array. Thus, the DNA-SWNT sensor array has great potential to be applied in chemical or bimolecular detection for the noninvasive diagnostics of diseases and health monitoring.

Keywords: breath analysis, diagnosis, DNA-SWNT sensor array, noninvasive

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Authors: Marko Batic, Nikola Tomasevic, Sanja Vranes

Abstract:

One of the strongholds in the ubiquitous efforts related to the energy conservation and energy efficiency improvement is represented by the retrofit of high energy consumers in buildings. In general, HVAC systems represent the highest energy consumers in buildings. However they usually suffer from mal-operation and/or malfunction, causing even higher energy consumption than necessary. Various Fault Detection and Diagnosis (FDD) systems can be successfully employed for this purpose, especially when it comes to the application at a single device/unit level. In the case of more complex systems, where multiple devices are operating in the context of the same building, significant energy efficiency improvements can only be achieved through application of comprehensive FDD systems relying on additional higher level knowledge, such as their geographical location, served area, their intra- and inter- system dependencies etc. This paper presents a comprehensive FDD system that relies on the utilization of common knowledge repository that stores all critical information. The discussed system is deployed as a test-bed platform at the two at Fiumicino and Malpensa airports in Italy. This paper aims at presenting advantages of implementation of the knowledge base through the utilization of ontology and offers improved functionalities of such system through examples of typical queries and reasoning that enable derivation of high level energy conservation measures (ECM). Therefore, key SPARQL queries and SWRL rules, based on the two instantiated airport ontologies, are elaborated. The detection of high level irregularities in the operation of airport heating/cooling plants is discussed and estimation of energy savings is reported.

Keywords: airport ontology, knowledge management, ontology modeling, reasoning

Procedia PDF Downloads 537

Authors: Zhang Xiaodong, Tang Zixin, Liu Xin

Abstract:

In order to avoid self-collision of space manipulators during operation process, a real-time detection method is proposed in this paper. The manipulator is fitted into a cylinder enveloping surface, and then the detection algorithm of collision between cylinders is analyzed. The collision model of space manipulator self-links can be detected by using this algorithm in real-time detection during the operation process. To ensure security of the operation, a safety threshold is designed. The simulation and experiment results verify the effectiveness of the proposed algorithm for a 7-DOF space manipulator.

Keywords: space manipulator, collision detection, self-collision, the real-time collision detection

Procedia PDF Downloads 469

Authors: Fatima Mokeddem

Abstract:

The phonocardiogram signal (PCG) is a physiological signal that reflects heart mechanical activity, is a promising tool for curious researchers in this field because it is full of indications and useful information for medical diagnosis. PCG segmentation is a basic step to benefit from this signal. Therefore, this paper presents an algorithm that serves the separation of heart sounds and heart murmurs in case they exist in order to use them in several applications and heart sounds analysis. The separation process presents here is founded on three essential steps filtering, envelope detection, and heart sounds segmentation. The algorithm separates the PCG signal into S1 and S2 and extract cardiac murmurs.

Keywords: phonocardiogram signal, filtering, Envelope, Detection, murmurs, heart sounds

Procedia PDF Downloads 140

Authors: A. I. Khalafalla, K. A. Al-Busada, I. M. El-Sabagh

Abstract:

Pox and pox-like diseases of camels are a group of exanthematous skin conditions that have become increasingly important economically. They may be caused by three distinct viruses: camelpox virus (CMPV), camel contagious ecthyma virus (CCEV) and camel papillomavirus (CAPV). These diseases are difficult to differentiate based on clinical presentation in disease outbreaks. Molecular methods such as PCR targeting species-specific genes have been developed and used to identify CMPV and CCEV, but not simultaneously in a single tube. Recently, multiplex PCR has gained reputation as a convenient diagnostic method with cost- and time–saving benefits. In the present communication, we describe the development, optimization and validation a multiplex PCR assays able to detect simultaneously the genome of the three viruses in one single test allowing for rapid and efficient molecular diagnosis. The assay was developed based on the evaluation and combination of published and new primer sets, and was applied to the detection of 110 tissue samples. The method showed high sensitivity, and the specificity was confirmed by PCR-product sequencing. In conclusion, this rapid, sensitive and specific assay is considered a useful method for identifying three important viruses in specimens from camels and as part of a molecular diagnostic regime.

Keywords: multiplex PCR, diagnosis, pox and pox-like diseases, camels

Procedia PDF Downloads 466