Search results for: network distributed diagnosis

Authors: Ehsan Khademi, Mahdi Jalili

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Despite the special features of bipartite networks, they are common in many systems. Real-world bipartite networks may show community structure, similar to what one can find in one-mode networks. However, the interpretation of the community structure in bipartite networks is different as compared to one-mode networks. In this manuscript, we compare a number of available methods that are frequently used to discover community structure of bipartite networks. These networks are categorized into two broad classes. One class is the methods that, first, transfer the network into a one-mode network, and then apply community detection algorithms. The other class is the algorithms that have been developed specifically for bipartite networks. These algorithms are applied on a model network with prescribed community structure.

Keywords: community detection, bipartite networks, co-clustering, modularity, network projection, complex networks

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Authors: Francesco Buccafurri, Celeste Romolo

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Nowadays phishing is the most frequent starting point of cyber-attack vectors. Phishing is implemented both via email and social network messages. While a wide scientific literature exists which addresses the problem of contrasting email spam-phishing, no specific countermeasure has been so far proposed for phishing included into private messages of social network platforms. Unfortunately, the problem is severe. This paper proposes an approach against social network phishing, based on a non invasive collaborative information-sharing approach which leverages blockchain. The detection method works by filtering candidate messages, by distilling them by means of a distance-preserving hash function, and by publishing hashes over a public blockchain through a trusted smart contract (thus avoiding denial of service attacks). Phishing detection exploits social information embedded into social network profiles to identify similar messages belonging to disjoint contexts. The main contribution of the paper is to introduce a new approach to contrasting the problem of social network phishing, which, despite its severity, received little attention by both research and industry.

Keywords: phishing, social networks, information sharing, blockchain

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Authors: Jose L. Oliver, Taras Agryzkov, Leandro Tortosa, Jose F. Vicent, Javier Santacruz

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This paper aims to demonstrate how a topological study of an urban street network can be used as a tool to be applied to some heritage conservation areas in a city. In the last decades, we find different kinds of approaches in the discipline of Architecture and Urbanism based in the so-called Sciences of Complexity. In this context, this paper uses mathematics from the Network Theory. Hence, it proposes a methodology based in obtaining information from a graph, which is created from a network of urban streets. Then, it is used an algorithm that establishes a ranking of importance of the nodes of that network, from its topological point of view. The results are applied to a heritage area in a particular city, confronting the data obtained from the mathematical model, with the ones from the field work in the case study. As a result of this process, we may conclude the necessity of implementing some actions in the area, and where those actions would be more effective for the whole heritage site.

Keywords: graphs, heritage cities, spatial analysis, urban networks

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Authors: Shah Abbas

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Rheumatoid arthritis is a systemic, inflammatory autoimmune disease, affecting an overall 1% of the global population. Despite being tremendous efforts by scientists, early diagnosis of RA still has not been achieved. In the current study, a Graphene oxide (GO) based electrochemical sensor has been developed for early diagnosis of RA through Cyclic voltammetry. Chitosan (CHI), a CPnatural polymer has also been incorporated along with GO in order to enhance the biocompatibility and functionalization potential of the biosensor. CCPs are known antigens for Anti Citrullinated Peptide Antibodies (ACPAs) which can be detected in serum even 14 years before the appearance of symptoms, thus they are believed to be an ideal target for the early diagnosis of RA. This study has yielded some promising results regarding the binding and detection of ACPAs through changes in the electrochemical properties of biosensing material. The cyclic voltammogram of this biosensor reflects the binding of ACPAs to the biosensor surface, due to its shifts observed in the current flow (cathodic current) as compared to the when no ACPAs bind as it is absent in RA negative patients.

Keywords: rheumatoid arthritis, peptide sensor, graphene oxide, anti citrullinated peptide antibodies, cyclic voltammetry

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Authors: Abdulkader Helwan

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Iris cancer, so called intraocular melanoma is a cancer that starts in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris cancer detection system since the available techniques used currently are still not efficient. The combination of the image processing and artificial neural networks has a great efficiency for the diagnosis and detection of the iris cancer. Image processing techniques improve the diagnosis of the cancer by enhancing the quality of the images, so the physicians diagnose properly. However, neural networks can help in making decision; whether the eye is cancerous or not. This paper aims to develop an intelligent system that stimulates a human visual detection of the intraocular melanoma, so called iris cancer. The suggested system combines both image processing techniques and neural networks. The images are first converted to grayscale, filtered, and then segmented using prewitt edge detection algorithm to detect the iris, sclera circles and the cancer. The principal component analysis is used to reduce the image size and for extracting features. Those features are considered then as inputs for a neural network which is capable of deciding if the eye is cancerous or not, throughout its experience adopted by many training iterations of different normal and abnormal eye images during the training phase. Normal images are obtained from a public database available on the internet, “Mile Research”, while the abnormal ones are obtained from another database which is the “eyecancer”. The experimental results for the proposed system show high accuracy 100% for detecting cancer and making the right decision.

Keywords: iris cancer, intraocular melanoma, cancerous, prewitt edge detection algorithm, sclera

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Authors: Tayyibah Shah Alam, Joe Thomas, Nayantara Shenoy

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Presenting a case that we would like to share, the answer is straight forward but the path taken to get to the diagnosis is where it gets interesting. A 31-year-old lady presented to the Rheumatology Outpatient department with left-sided chest pain associated with left-sided elbow joint pain intensifying over the last 2 days. She had been having a prolonged history of chest pain with minimal intensity since 2016. The pain is intermittent in nature. Aggravated while exerting, lifting heavy weights and lying down. Relieved while sitting. Her physical examination and laboratory tests were within normal limits. An electrocardiogram (ECG) showed normal sinus rhythm and a chest X-ray with no significant abnormality was noted. The primary suspicion was recurrent costochondritis. Cardiac blood inflammatory markers and Echo were normal, ruling out ACS. CT chest and MRI Thorax contrast showed small ill-defined STIR hyperintensity with thin peripheral enhancement in the anterior mediastinum in the left side posterior to the 5th costal cartilage and anterior to the pericardium suggestive of changes in the fat-focal panniculitis. Confirming the diagnosis as Epicardial fat necrosis. She was started on Colchicine and Nonsteroidal anti-inflammatory drugs for 2-3 weeks, following which a repeat CT showed resolution of the lesion and improvement in her. It is often under-recognized or misdiagnosed. CT scan was collectively used to establish the diagnosis. Making the correct diagnosis prospectively alleviates unnecessary testing in favor of conservative management.

Keywords: EFN, panniculitis, unknown etiology, recurrent chest pain

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Authors: Seema Singh Parmar, Shweta Chauhan

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Aims & Objectives: To evaluate the presence of various psychiatric disorders in patients reporting with a headache as the only symptom. Methodology: 200 patients with the chief complain of a headache who visited the psychiatric OPD of a tertiary care were investigated. Out of them 50 who had pure psychiatric illness without any other neurological disease were investigated, and their diagnosis was made. Independent sample t-tests were applied to generate results. Results: The most common psychiatric diagnosis seen in the sample was Depression (64%) out of which 47% showed features of Depression with anxious distress. Other psychiatric disorders seen were Generalized Anxiety Disorder, Panic Attacks, Somatic Symptom Disorder and Obsessive Compulsive Disorder. For pure psychiatry, headache related illnesses female to male ratio was 1.64. Conclusion: The increasing frequency of psychiatric disorders among patients who only visit the doctor seeking treat a headache shows the need for better identification of psychiatric disorders because proper diagnosis and target of psychiatric treatment shall give complete relief to the patient’s symptomatology.

Keywords: anxiety disorders, depression, headache, panic attacks

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Authors: Zhaofeng Li, Jun Kang Chow, Yu-Hsing Wang

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This paper attempts to bridge the soil properties and the mechanical response of soil in the discrete element method (DEM) simulation. The artificial neural network (ANN) was therefore adopted, aiming to reproduce the stress-strain-volumetric response when soil properties are given. 31 biaxial shearing tests with varying soil parameters (e.g., initial void ratio and interparticle friction coefficient) were generated using the DEM simulations. Based on these 45 sets of training data, a three-layer neural network was established which can output the entire stress-strain-volumetric curve during the shearing process from the input soil parameters. Beyond the training data, 2 additional sets of data were generated to examine the validity of the network, and the stress-strain-volumetric curves for both cases were well reproduced using this network. Overall, the ANN was found promising in predicting the soil behavior and reducing repetitive simulation work.

Keywords: artificial neural network, discrete element method, soil properties, stress-strain-volumetric response

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Authors: Vrince Vimal, Madhav J. Nigam

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Wireless sensor networks have enticed much of the spotlight from researchers all around the world, owing to its extensive applicability in agricultural, industrial and military fields. Energy conservation node deployment stratagems play a notable role for active implementation of Wireless Sensor Networks. Clustering is the approach in wireless sensor networks which improves energy efficiency in the network. The clustering algorithm needs to have an optimum size and number of clusters, as clustering, if not implemented properly, cannot effectively increase the life of the network. In this paper, an algorithm has been proposed to address connectivity issues with the aim of ensuring the uniform energy consumption of nodes in every part of the network. The results obtained after simulation showed that the proposed algorithm has an edge over existing algorithms in terms of throughput and networks lifetime.

Keywords: Wireless Sensor network (WSN), Random Deployment, Clustering, Isolated Nodes, Networks Lifetime

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Authors: James Boag

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The needs of people with a diagnosis of dementia and their carers and families are physical, psychosocial, and psychological and begin at the time of diagnosis. There is frequently a lack of emotional support and counselling. Care- giving support is required from the presentation of the first symptoms of dementia until death. Alzheimer's disease begins decades before the clinical symptoms begin to appear, and in many cases, it remains undiagnosed, or diagnosed too late for any possible interventions to have any effect. However, if an incorrect diagnosis is given, it may result in a person being treated, without effect, for a type of dementia they do not have and delaying the interventions they should have received. Being diagnosed with dementia can cause emotional distress to the person, and physical and emotional support is needed, which will become more important as the disease progresses. The severity of the patient's dementia and their symptoms has a bearing of the impact on the carer and the support needed. A lack of insight and /or a denial of the diagnosis, grief, reacting to anticipated future losses, and coping methods to maximise the disease outcome, are things that should be addressed. Because of the stigma, it is important for carers not to lose contact with family and others because social isolation leads to depression and burnout. The impact on a carer's well- being and quality of life can be influenced by the severity of the illness, its type of dementia, its symptoms, healthcare support, financial and social status, career, age, health, residential setting, and relationship to the patient. Carer burnout due to lack of support leads to people diagnosed with dementia being put into residential care prematurely. Often dementia is not recognised as a terminal illness, limiting the ability of the person diagnosed with dementia and their carers to work on advance care planning and getting access to palliative and other support. Many carers have been satisfied with the physical support they were given in their everyday life, however, it was agreed that there was an immense unmet need for psychosocial support, especially after diagnosis and approaching end of life. Providing continuity and coordination of care is important. Training is necessary for providers to understand that every case is different, and they should understand the complexities. Grief, the emotional response to loss, is suffered during the progression of the disease and long afterwards, and carers should continue to be supported after the death of the person they were caring for.

Keywords: dementia, caring, challenges, needs

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Authors: Kevin D. Manalo, Jumelyn L. Torres, Noel B. Linsangan

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This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network.

Keywords: field programmable gate array, multilayer perceptron neural network, verilog, zigbee

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Authors: Hayder H. Kareem, Ayad K. Hussein, Aseel A. Alkatib

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Groundwater accumulated at geological formations constitutes a worldwide vital water resource component which can be used to supply agriculture, industry, and domestic uses. The subsurface environment is affected by human activities; consequently, planning and sustainable management of aquifers require serious attention, especially as the world is exposed to the problem of global warming. Establishing accurate and efficient groundwater models will provide confident results for the behavior of the aquifer's system. The new approach, 'Distributed Value Property Zones,' available in Visual MODFLOW, is used to reconstruct the subsurface zones of the Al-Najaf region aquifer, and then its effect is compared with those manual and automated (PEST) approaches. Results show that the model has become more accurate with the use of the new approach, as the calibration and results analyses revealed. The assessment of the Al-Najaf region groundwater aquifer has revealed a degree of insufficiency of the required pumping demand, which reflects dry areas in both of the aquifer's layers. In addition, with pumping, the Euphrates River loses water of 7458 m³/day to the aquifer, while without pumping, it gains 28837 m³/day from the rainfall's recharge. The distributed value property zones approach achieves a precise groundwater model to assess the state of the Al-Najaf region aquifer.

Keywords: Al-Najaf region, distributed value property zones approach, hydraulic conductivity, groundwater modelling using visual MODFLOW

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Authors: Kefaya Qaddoum

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The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: neural network, conformal prediction, cancer classification, regression

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Authors: Nesrine Berber, Hafid Haffaf, Abdel Madjid Meghabar

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In the last decade, technology has continued to grow and has changed the structure of our society. Today, new technologies including the information and communication (ICT) play a main role which importance continues to grow, now it's become indispensable to the economic, social and cultural. Thus, ICT technology has proven to be as a promising intervention in the area of road transport. The supervision model of class of train of intelligent and autonomous vehicles leads us to give some defintions about IAV and the different technologies used for communication between them. Our aim in this work is to present an hypergraph modeling a class of train of Intelligent and Autonomous Vehicles (IAV).

Keywords: intelligent transportation system, intelligent autonomous vehicles, Ad Hoc network, wireless technologies, hypergraph modeling, supervision

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Authors: Syed W. Hasan, Zhiqun Tian

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Wet spinning provides a facile and economic route to fabricate graphene nanofibers (GFs) on mass scale. Nevertheless, the pristine GFs exhibit significantly low electrical and mechanical properties owing to stacked graphene sheets and weak inter-atomic bonding. In this report, we present highly conductive Ag-decorated-GFs (Ag/GFs). The SEM micrographs show Ag nanoparticles (NPs) (dia ~10 nm) are homogeneously distributed throughout the cross-section of the fiber. The Ag NPs provide a conductive network for the electrons flow raising the conductivity to 1.8(10^4) S/m which is 4 times higher than the pristine GFs. Our results surpass the conductivities of graphene fibers doped with CNTs, Nanocarbon, fullerene, and Cu. The chemical and structural attributes of Ag/GFs are further elucidated through XPS, AFM and Raman spectroscopy.

Keywords: Ag nanoparticles, Conductive fibers, Graphene, Wet spinning

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Authors: Earleen Jane Fuentes, Regeene Melarese Lim, Franklin Benjamin Tapia, Alexis Pantola

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Mobile Ad-hoc Network (MANET) is an infrastructure-less network of mobile devices, also known as nodes. These nodes heavily rely on each other’s resources such as memory, computing power, and energy. Thus, some nodes may become selective in forwarding packets so as to conserve their resources. These nodes are called misleading nodes. Several reputation-based techniques (e.g. CORE, CONFIDANT, LARS, SORI, OCEAN) and acknowledgment-based techniques (e.g. TWOACK, S-TWOACK, EAACK) have been proposed to detect such nodes. These techniques do not appropriately punish misleading nodes. Hence, this paper addresses the limitations of these techniques using a system called MINDRA.

Keywords: acknowledgment-based techniques, mobile ad-hoc network, selfish nodes, reputation-based techniques

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Authors: Megha Chavda, Parth Thummar, Rahul Ghetia

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This paper presents the voltage and current control of microgrid accompanied by the synchronization of microgrid with the main utility grid in both islanded and grid-connected modes. Distributed Energy Resources (DERs) satisfy the wide-spread power demand of consumer by behaving as a micro source for a low voltage (LV) grid or microgrid. Synchronization of the microgrid with the main utility grid is done using PLL and PWM gate pulse generation technique is used for the Voltage Source Converter. Potential Function method achieves the voltage and current control of this microgrid in both islanded and grid-connected modes. A low voltage grid consisting of three distributed generators (DG) is considered for the study and is simulated in time-domain using PSCAD/EMTDC software. The simulation results depict the appropriateness of voltage and current control of microgrid and synchronization of microgrid with the medium voltage (MV) grid.

Keywords: microgrid, distributed energy resources, voltage and current control, voltage source converter, pulse width modulation, phase locked loop

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Authors: Berredjem Hajira, Benlaifa Meriem, Becheker Imene, Bardi Rafika, Djebar Med Reda

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Recent acquired or reactivation T.gondii infection is a serious complication in HIV patients. Classical serological diagnosis relies on the detection of anti-Toxoplasma immunoglobulin ; however, serology may be unreliable in HIV immunodeficient patients who fail to produce significant titers of specific antibodies. PCR assays allow a rapid diagnosis of Toxoplasma infection. In this study, we compared the value of the PCR for diagnosing active toxoplasmosis in cerebrospinal fluid and blood samples from HIV patients. Anti-Toxoplasma antibodies IgG and IgM titers were determined by ELISA. In parallel, nested PCR targeting B1 gene and conventional PCR-ELISA targeting P30 gene were used to detect T. gondii DNA in 25 blood samples and 12 cerebrospinal fluid samples from patients in whom toxoplasmic encephalitis was confirmed by clinical investigations. A total of 15 negative controls were used. Serology did not contribute to confirm toxoplasmic infection, as IgG and IgM titers decreased early. Only 8 out 25 blood samples and 5 out 12 cerebrospinal fluid samples PCRs yielded a positive result. 5 patients with confirmed toxoplasmosis had positive PCR results in either blood or cerebrospinal fluid samples. However, conventional nested B1 PCR gave best results than the P30 gene one for the detection of T.gondii DNA in both samples. All samples from control patients were negative. This study demonstrates the unusefulness of the serological tests and the high sensitivity and specificity of PCR in the diagnosis of toxoplasmic encephalitis in HIV patients.

Keywords: cerebrospinal fluid, HIV, Toxoplasmosis, PCR

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Authors: Windy Rakhmawati, Suryani Suryani, Sri Hendrawati, Nenden Nur Asriyani Maryam

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Tuberculosis (TB) is one of the leading causes of death in the world. Fear of COVID-19 has made people reluctant to visit health facilities, leading to disruptions to childhood TB control programs, which may increase household transmission and delay diagnosis and treatment. This study aimed to describe parents' knowledge, attitudes, and health-seeking behaviour towards childhood TB during the COVID-19 pandemic. This cross-sectional study was performed on 392 parents with TB children in three provinces with the highest proportion of TB cases in Indonesia. This study was conducted from February to December 2022. The inclusion criteria of respondents were parents with a child aged 0-14 years old with TB diagnosis who live with their parents. Data were collected using the Knowledge, Attitude, and Practice (KAP) survey guidelines from the World Health Organization and analyzed descriptively, as well as Spearman’s correlation. Overall, 392 parents of children with TB had poor knowledge (51.8%) including about causes, risk factors, transmission, symptoms, treatment, and prevention, which about 52.3%, 55.1%, 61.2%, 69.6%, 100%, 59.2%, respectively. Parents' health service-seeking behaviour towards Child TB was not normally distributed (P < 0.05) with knowledge test results (.000) and Seeking Health Services (.000). Health-seeking behaviour of parents in pediatric TB care was self-medication or self-treatment (86.2%), Traditional health seeking behaviour (4.8%), and modern health seeking behaviour (8.9%). The correlation between knowledge and seeking health services (Sig= .609) means there is no correlation between knowledge about TB and parents' health-seeking behaviour. Furthermore, 60.2% of the respondents would be shocked if their child had TB. More than half of the families in this study have poor knowledge and did self-medication or self-treatment regarding health-seeking behaviour for TB disease. Therefore, health workers, especially nurses, must provide TB-related education and health promotion and emphasize the importance of early detection. Health workers can also optimize their role in caring for and providing care to patients by increasing their trust in health workers, which will impact health-seeking behaviour in the future.

Keywords: attitude, child, health seeking behaviour, knowledge, tuberculosis

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Authors: Tamene Keneni, Tibebu Yohannes

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The prevalence of autism spectrum disorder (ASD) in general and autism in particular is on the rise globally, and the need for evidence-based intervention and care for children with autism has grown, too. However, evidence on autism is scanty in developing countries, including Ethiopia. With the aim to help fill the gap and paucity in research into the issue, the main purpose of this study is to explore, better understand, and document the experiences and perceptions of parents of children with autism. To this end, we used a qualitative survey to collect data from a convenient sample of parents raising a child with autism. The data collected were subjected to qualitative analysis that yielded several themes and subthemes, including late diagnosis, parents’ reactions to diagnosis, sources of information during and after diagnosis, differing reactions to having a child with autism from siblings, extended family members, and the larger community, attribution of autism to several causes by the community, lack of recognition and open discussion of autism and lack of appropriated public educational and health care services for children with autism and their parents. The themes and subthemes identified were discussed in light of existing literature, and implications for practice were drawn.

Keywords: ASD, autism, children with autism, raising children with autism

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Authors: Frederic Jumelle, Kelvin So, Didan Deng

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In this paper, we are introducing a model of artificial general intelligence (AGI), the functional neural network (FNN), for modeling human decision-making processes. The FNN is composed of multiple artificial mirror neurons (AMN) racing in the network. Each AMN has a similar structure programmed independently by the users and composed of an intention wheel, a motor core, and a sensory core racing at a specific velocity. The mathematics of the node’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education, and medicine, including the opportunity to design an intelligent learning agent with application in AGI. We believe that FNN has a promising potential to transform the way we can compute decision-making and lead to a new generation of AI chips for seamless human-machine interactions (HMI).

Keywords: neural computing, human machine interation, artificial general intelligence, decision processing

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Authors: Sneha Shankar, Orlando Buendia, Will Evans

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Alpha-1 antitrypsin deficiency (AATD) is a rare, genetic, and multisystemic condition. Underdiagnosis is common, leading to chronic pulmonary and hepatic complications, increased resource utilization, and additional costs to the healthcare system. Currently, there is limited evidence of the direct medical costs of AATD diagnosis in the UK. This study explores the economic impact of AATD patients during the 3 years before diagnosis and to identify the major cost drivers using primary and secondary care electronic health record (EHR) data. The 3 years before diagnosis time period was chosen based on the ability of our tool to identify patients earlier. The AATD algorithm was created using published disease criteria and applied to 148 known AATD patients’ EHR found in a primary care database of 936,148 patients (413,674 Biobank and 501,188 in a single primary care locality). Among 148 patients, 9 patients were flagged earlier by the tool and, on average, could save 3 (1-6) years per patient. We analysed 101 of the 148 AATD patients’ primary care journey and 20 patients’ Hospital Episode Statistics (HES) data, all of whom had at least 3 years of clinical history in their records before diagnosis. The codes related to laboratory tests, clinical visits, referrals, hospitalization days, day case, and inpatient admissions attributable to AATD were examined in this 3-year period before diagnosis. The average cost per patient was calculated, and the direct medical costs were modelled based on the mean prevalence of 100 AATD patients in a 500,000 population. A deterministic sensitivity analysis (DSA) of 20% was performed to determine the major cost drivers. Cost data was obtained from the NHS National tariff 2020/21, National Schedule of NHS Costs 2018/19, PSSRU 2018/19, and private care tariff. The total direct medical cost of one hundred AATD patients three years before diagnosis in primary and secondary care in the UK was £3,556,489, with an average direct cost per patient of £35,565. A vast majority of this total direct cost (95%) was associated with inpatient admissions (£3,378,229). The DSA determined that the costs associated with tier-2 laboratory tests and inpatient admissions were the greatest contributors to direct costs in primary and secondary care, respectively. This retrospective study shows the role of EHRs in calculating direct medical costs and the potential benefit of new technologies for the early identification of patients with AATD to reduce the economic burden in primary and secondary care in the UK.

Keywords: alpha-1 antitrypsin deficiency, costs, digital health, early diagnosis

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Authors: Neda Seyyedi, Reza Berangi

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Voice over IP (VOIP) network, also known as Internet telephony, is growing increasingly having occupied a large part of the communications market. With the growth of each technology, the related security issues become of particular importance. Taking advantage of this technology in different environments with numerous features put at our disposal, there arises an increasing need to address the security threats. Being IP-based and playing a signaling role in VOIP networks, Session Initiation Protocol (SIP) lets the invaders use weaknesses of the protocol to disable VOIP service. One of the most important threats is denial of service attack, a branch of which in this article we have discussed as flooding attacks. These attacks make server resources wasted and deprive it from delivering service to authorized users. Distributed denial of service attacks and attacks with a low rate can mislead many attack detection mechanisms. In this paper, we introduce a mechanism which not only detects distributed denial of service attacks and low rate attacks, but can also identify the attackers accurately. We detect and prevent flooding attacks in SIP protocol using Shannon (FDP-S), Renyi (FDP-R) and Tsallis (FDP-T) entropy. We conducted an experiment to compare the percentage of detection and rate of false alarm messages using any of the Shannon, Renyi and Tsallis entropy as a measure of disorder. Implementation results show that, according to the parametric nature of the Renyi and Tsallis entropy, by changing the parameters, different detection percentages and false alarm rates will be gained with the possibility to adjust the sensitivity of the detection mechanism.

Keywords: VOIP networks, flooding attacks, entropy, computer networks

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Authors: Ahmad Shayeq Azizi, Yuji Toda

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In Afghanistan, floods are the most frequent and recurrent events among other natural disasters. On the other hand, lack of monitoring data is a severe problem, which increases the difficulty of making the appropriate flood countermeasures of flood forecasting. This study is carried out to simulate the flood inundation in Harirud River Basin by application of distributed hydrological model, Integrated Flood Analysis System (IFAS) and 2D hydrodynamic model, International River Interface Cooperative (iRIC) based on satellite rainfall combined with historical peak discharge and global accessed data. The results of the simulation can predict the inundation area, depth and velocity, and the hardware countermeasures such as the impact of levee installation can be discussed by using the present method. The methodology proposed in this study is suitable for the area where hydrological and geographical data including river survey data are poorly observed.

Keywords: distributed hydrological model, flood inundation, hydrodynamic model, ungauged basins

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Authors: Hong Yu, Ion Matei

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Carbon fiber reinforced composites (CFRP) used as aircraft structure are subject to lightning strike, putting structural integrity under risk. Indirect damage may occur after a lightning strike where the internal structure can be damaged due to excessive heat induced by lightning current, while the surface of the structures remains intact. Three damage modes may be observed after a lightning strike: fiber breakage, inter-ply delamination and intra-ply cracks. The assessment of internal damage states in composite is challenging due to complicated microstructure, inherent uncertainties, and existence of multiple damage modes. In this work, a model based approach is adopted to diagnose faults in carbon composites after lighting strikes. A resistor network model is implemented to relate the overall electrical and thermal conduction behavior under simulated lightning current waveform to the intrinsic temperature dependent material properties, microstructure and degradation of materials. A fault detection and identification (FDI) module utilizes the physics based model and a particle filtering algorithm to identify damage mode as well as calculate the probability of structural failure. Extensive simulation results are provided to substantiate the proposed fault diagnosis methodology with both single fault and multiple faults cases. The approach is also demonstrated on transient resistance data collected from a IM7/Epoxy laminate under simulated lightning strike.

Keywords: carbon composite, fault detection, fault identification, particle filter

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Authors: Han Bing, Yin Zhenjie

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In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

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Authors: Abhisek Dash

Abstract:

This study had three neural networks, one for number segmentation, one for number detection and one for number recognition all of which are coupled to one another. All networks were trained on the MNIST dataset and were convolutional. It was assumed that the images had lighter background and darker foreground. The segmentation network took 28x28 images as input and had sixteen outputs. Segmentation training starts when a dark pixel is encountered. Taking a window(7x7) over that pixel as focus, the eight neighborhood of the focus was checked for further dark pixels. The segmentation network was then trained to move in those directions which had dark pixels. To this end the segmentation network had 16 outputs. They were arranged as “go east”, ”don’t go east ”, “go south east”, “don’t go south east”, “go south”, “don’t go south” and so on w.r.t focus window. The focus window was resized into a 28x28 image and the network was trained to consider those neighborhoods which had dark pixels. The neighborhoods which had dark pixels were pushed into a queue in a particular order. The neighborhoods were then popped one at a time stitched to the existing partial image of the number one at a time and trained on which neighborhoods to consider when the new partial image was presented. The above process was repeated until the image was fully covered by the 7x7 neighborhoods and there were no more uncovered black pixels. During testing the network scans and looks for the first dark pixel. From here on the network predicts which neighborhoods to consider and segments the image. After this step the group of neighborhoods are passed into the detection network. The detection network took 28x28 images as input and had two outputs denoting whether a number was detected or not. Since the ground truth of the bounds of a number was known during training the detection network outputted in favor of number not found until the bounds were not met and vice versa. The recognition network was a standard CNN that also took 28x28 images and had 10 outputs for recognition of numbers from 0 to 9. This network was activated only when the detection network votes in favor of number detected. The above methodology could segment connected and overlapping numbers. Additionally the recognition unit was only invoked when a number was detected which minimized false positives. It also eliminated the need for rules of thumb as segmentation is learned. The strategy can also be extended to other characters as well.

Keywords: convolutional neural networks, OCR, text detection, text segmentation

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Authors: Shu-Min Tsao, Chung-Ming Lo, Shao-Chun Chen

Abstract:

Most diabetes patients tend to suffer from its complication of retina diseases. Therefore, early detection and early treatment are important. In clinical examinations, using color fundus image was the most convenient and available examination method. According to the exudates appeared in the retinal image, the status of retina can be confirmed. However, the routine screening of diabetic retinopathy by color fundus images would bring time-consuming tasks to physicians. This study thus proposed a computer-aided exudate diagnosis for the screening of diabetic retinopathy. After removing vessels and optic disc in the retinal image, six quantitative features including region number, region area, and gray-scale values etc… were extracted from the remaining regions for classification. As results, all six features were evaluated to be statistically significant (p-value < 0.001). The accuracy of classifying the retinal images into normal and diabetic retinopathy achieved 82%. Based on this system, the clinical workload could be reduced. The examination procedure may also be improved to be more efficient.

Keywords: computer-aided diagnosis, diabetic retinopathy, exudate, image processing

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Authors: Saeed Pourhassan, Nastaran Maghbouli

Abstract:

1) Background It has been hypothesized that, in individuals with diabetes mellitus, the peripheral nerve is swollen due to sorbitol over-accumulation. Additionally growing evidence supported electro diagnostic study of diabetes induced neuropathy as a method having some challenges. 2) Objective To examine the performance of sonographic cross-sectional area (CSA) measurements in the diagnosis of diabetic polyneuropathy (DPN). 3) Data Sources Electronic databases, comprising PubMed and EMBASE and Google scholar, were searched for the appropriate studies before Jan 1, 2020. 4) Study Selection Eleven trials comparing different peripheral nerve CSA measurements between participants with and without DPN were included. 5) Data Extraction Study design, participants' demographic characteristics, diagnostic reference of DPN, and evaluated peripheral nerves and methods of CSA measurement. 6) Data Synthesis Among different peripheral nerves, Tibial nerve diagnostic odds ratios pooled from five studies (713 participants) were 4.46 (95% CI, 0.35–8.57) and the largest one with P<0.0001, I²:64%. Median nerve CSA at wrist and mid-arm took second and third place with ORs= 2.82 (1.50-4.15), 2.02(0.26-3.77) respectively. The sensitivities and specificities pooled from two studies for Sural nerve were 0.78 (95% CI, 0.68–0.89), and 0.68 (95% CI, 0.53–0.74). Included studies for other nerves were limited to one study. The largest sensitivity was for Sural nerve and the largest specificity was for Tibial nerve. 7) Conclusions The peripheral nerves CSA measured by ultrasound imaging is useful for the diagnosis of DPN and is most significantly different between patients and participants without DPN at the Tibial nerve. Because the Tibial nerve CSA in healthy participants, at various locations, rarely exceeds 24 mm2, this value can be considered as a cutoff point for diagnosing DPN.

Keywords: diabetes, diagnosis, polyneuropathy, ultrasound

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Authors: Nada Slimane, Foued Theljani, Faouzi Bouani

Abstract:

The paper addresses the problem of fault diagnosis for systems operating in several modes (normal or faulty) based on states assessment. We use, for this purpose, a methodology consisting of three main processes: 1) sequential data clustering, 2) linear model regression and 3) state filtering. Typically, Kalman Filter (KF) is an algorithm that provides estimation of unknown states using a sequence of I/O measurements. Inevitably, although it is an efficient technique for state estimation, it presents two main weaknesses. First, it merely predicts states without being able to isolate/classify them according to their different operating modes, whether normal or faulty modes. To deal with this dilemma, the KF is endowed with an extra clustering step based fully on sequential version of the k-means algorithm. Second, to provide state estimation, KF requires state space models, which can be unknown. A linear regularized regression is used to identify the required models. To prove its effectiveness, the proposed approach is assessed on a simulated benchmark.

Keywords: clustering, diagnosis, Kalman Filtering, k-means, regularized regression

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