Search results for: covering machine

Authors: Mikheil Kalmakhelidze

Abstract:

Record linkage (RL) problem has become more and more important in recent years due to the growing interest towards big data analysis. The problem can be formulated in a very simple way: Given two entries a and b of a database, decide whether they represent the same object or not. There are two classical deterministic and probabilistic ways of solving the RL problem. Using simple Bayes classifier in many cases produces useful results but sometimes they show to be poor. In recent years several successful approaches have been made towards solving specific RL problems by neural network algorithms including single layer perception, multilayer back propagation network etc. In our work, we model the RL problem for specific dataset of student applications in fuzzy description logic (FDL) where linkage of specific pair (a,b) depends on the truth value of corresponding formula A(a,b) in a canonical FDL model. As a main result, we build neural network for deciding truth value of FDL formulas in a canonical model and thus link RL problem to machine learning. We apply the approach to dataset with 10000 entries and also compare to classical RL solving approaches. The results show to be more accurate than standard probabilistic approach.

Keywords: description logic, fuzzy logic, neural networks, record linkage

Procedia PDF Downloads 274

Authors: Endri Rama, Genci Capi, Shigenori Kawahara

Abstract:

The mobile robot ability to navigate autonomously in its environment is very important. Even though the advances in technology, robot self-localization and goal directed navigation in complex environments are still challenging tasks. In this article, we propose a novel method for robot navigation based on rat’s brain signals (Local Field Potentials). It has been well known that rats accurately and rapidly navigate in a complex space by localizing themselves in reference to the surrounding environmental cues. As the first step to incorporate the rat’s navigation strategy into the robot control, we analyzed the rats’ strategies while it navigates in a multiple Y-maze, and recorded Local Field Potentials (LFPs) simultaneously from three brain regions. Next, we processed the LFPs, and the extracted features were used as an input in the artificial neural network to predict the rat’s next location, especially in the decision-making moment, in Y-junctions. We developed an algorithm by which the robot learned to imitate the rat’s decision-making by mapping the rat’s brain signals into its own actions. Finally, the robot learned to integrate the internal states as well as external sensors in order to localize and navigate in the complex environment.

Keywords: brain-machine interface, decision-making, mobile robot, neural network

Procedia PDF Downloads 298

Authors: A. F. Huseynov, N. T. Mardanov, J. Y. Nakhchivanski

Abstract:

This practical research is aimed to improve the management quality and efficiency of public administration agencies providing e-services. The monitoring system developed will provide continuous review of the websites compliance with the selected indicators, their evaluation based on the selected indicators and ranking of services according to the quality criteria. The responsible departments in the government agencies were surveyed; the questionnaire includes issues of management and feedback, e-services provided, and the application of information systems. By analyzing the main affecting factors and barriers, the recommendations will be given that lead to the relevant decisions to strengthen the state agencies competencies for the management and the provision of their services. Component 1. E-services monitoring system. Three separate monitoring activities are proposed to be executed in parallel: Continuous tracing of e-government sites using built-in web-monitoring program; this program generates several quantitative values which are basically related to the technical characteristics and the performance of websites. The expert assessment of e-government sites in accordance with the two general criteria. Criterion 1. Technical quality of the site. Criterion 2. Usability/accessibility (load, see, use). Each high-level criterion is in turn subdivided into several sub-criteria, such as: the fonts and the color of the background (Is it readable?), W3C coding standards, availability of the Robots.txt and the site map, the search engine, the feedback/contact and the security mechanisms. The on-line survey of the users/citizens – a small group of questions embedded in the e-service websites. The questionnaires comprise of the information concerning navigation, users’ experience with the website (whether it was positive or negative), etc. Automated monitoring of web-sites by its own could not capture the whole evaluation process, and should therefore be seen as a complement to expert’s manual web evaluations. All of the separate results were integrated to provide the complete evaluation picture. Component 2. Assessment of the agencies/departments efficiency in providing e-government services. - the relevant indicators to evaluate the efficiency and the effectiveness of e-services were identified; - the survey was conducted in all the governmental organizations (ministries, committees and agencies) that provide electronic services for the citizens or the businesses; - the quantitative and qualitative measures are covering the following sections of activities: e-governance, e-services, the feedback from the users, the information systems at the agencies’ disposal. Main results: 1. The software program and the set of indicators for internet sites evaluation has been developed and the results of pilot monitoring have been presented. 2. The evaluation of the (internal) efficiency of the e-government agencies based on the survey results with the practical recommendations related to the human potential, the information systems used and e-services provided.

Keywords: e-government, web-sites monitoring, survey, internal efficiency

Procedia PDF Downloads 305

Authors: Temidayo O. Omotehinwa, David O. Oyewola, Friday J. Agbo

Abstract:

This study aims to gather a proper perspective of the development landscape of Computer Science research in Nigeria. Therefore, a bibliometric analysis of 4,333 bibliographic records of Computer Science research in Nigeria in the last 31 years (1991-2021) was carried out. The bibliographic data were extracted from the Scopus database and analyzed using VOSviewer and the bibliometrix R package through the biblioshiny web interface. The findings of this study revealed that Computer Science research in Nigeria has a growth rate of 24.19%. The most developed and well-studied research areas in the Computer Science field in Nigeria are machine learning, data mining, and deep learning. The social structure analysis result revealed that there is a need for improved international collaborations. Sparsely established collaborations are largely influenced by geographic proximity. The funding analysis result showed that Computer Science research in Nigeria is under-funded. The findings of this study will be useful for researchers conducting Computer Science related research. Experts can gain insights into how to develop a strategic framework that will advance the field in a more impactful manner. Government agencies and policymakers can also utilize the outcome of this research to develop strategies for improved funding for Computer Science research.

Keywords: bibliometric analysis, biblioshiny, computer science, Nigeria, science mapping

Procedia PDF Downloads 112

Authors: Mohsin Ali Shaikh, Song Weiguo, Muhammad Kashan Surahio, Usman Shahid, Rehmat Karim

Abstract:

When the area of a structure that is threatened by a disaster affects personal safety, the effectiveness of disaster prevention, evacuation, and rescue operations can be summarized by three assessment indicators: personal safety, property preservation, and attribution of responsibility. These indicators are applicable regardless of the disaster that affects the building. People need to get out of the hazardous area and to a safe place as soon as possible because there's no other way to respond. The results of the tragedy are thus closely related to how quickly people are advised to evacuate and how quickly they are rescued. This study considers present fire prevention systems to address catastrophes and improve building safety. It proposes the methods of Prevention Level for Deployment in Advance and Spatial Transformation by Human-Machine Collaboration. We present and prototype a real-time fire protection system architecture for building disaster prevention, evacuation, and rescue operations. The design encourages the use of simulations to check the efficacy of evacuation, rescue, and disaster prevention procedures throughout the planning and design phase of the structure.

Keywords: prevention level, building information modeling, quality management system, simulated reality

Procedia PDF Downloads 70

Authors: Sharif Muddsair, Eisels Martin, Giesbrecht Eugenie

Abstract:

With development of society increase the demand for the movement of people also increases gradually. The various modes of the transport in different extent which expat impacts, which depends on mainly technical-operating conditions. The up-to-date telematics systems provide the transport industry a revolutionary. Appropriate use of these systems can help to substantially improve the efficiency. Vehicle monitoring and fleet tracking are among services used for improving efficiency and effectiveness of utility vehicle. There are many telematics systems which may contribute to eco-driving. Generally, they can be grouped according to their role in driving cycle. • Before driving - eco-route selection, • While driving – Advanced driver assistance, • After driving – remote analysis. Our point of interest is regulated in third point [after driving – remote analysis]. TS [Telematics-system] make it possible to record driving patterns in real time and analysis the data later on, So that driver- classification-specific hints [fast driver, slow driver, aggressive driver…)] are given to imitate eco-friendly driving style. Together with growing number of vehicle and development of information technology, telematics become an ‘active’ research subject in IT and the car industry. Telematics has gone a long way from providing navigation solution/assisting the driver to become an integral part of the vehicle. Today’s telematics ensure safety, comfort and become convenience of the driver.

Keywords: internet of things, iot, connected vehicle, cv, ts, telematics services, ml, machine learning

Procedia PDF Downloads 307

Authors: Jiawen Wang, Qijun Chen

Abstract:

The performance of most machine-learning methods for image inpainting depends on the quantity and quality of the training samples. However, it is very expensive or even impossible to obtain a great number of training samples in many scenarios. In this paper, an image inpainting model based on a generative adversary network (GAN) is constructed for the cases when the number of training samples is small. Firstly, a feature extraction network (F-net) is incorporated into the GAN network to utilize the available information of the inpainting image. The weighted sum of the extracted feature and the random noise acts as the input to the generative network (G-net). The proposed network can be trained well even when the sample size is very small. Secondly, in the phase of the completion for each damaged image, a genetic algorithm is designed to search an optimized noise input for G-net; based on this optimized input, the parameters of the G-net and F-net are further learned (Once the completion for a certain damaged image ends, the parameters restore to its original values obtained in the training phase) to generate an image patch that not only can fill the missing part of the damaged image smoothly but also has visual semantics.

Keywords: image inpainting, generative adversary nets, genetic algorithm, small-sample size

Procedia PDF Downloads 130

Authors: S. Chockalingam, U. Natarajan, D. M. Santhoshsarang

Abstract:

Particle damping is a promising vibration attenuating technique in boring tool than other type of damping with minimal effect on the strength, rigidity and stiffness ratio of the machine tool structure. Due to the cantilever nature of boring tool holder in operations, it suffers chatter when the slenderness ratio of the tool gets increased. In this study, Copper-Stainless steel (SS) particles were packed inside the boring tool which acts as a damper. Damper suppresses chatter generated during machining and also improves the machining efficiency of the tool with better slenderness ratio. In the first approach of particle damping, combined Cu-SS particles were packed inside the vibrating tool, whereas Copper and Stainless steel particles were selected separately and packed inside another tool and their effectiveness was analysed in this simulation. This study reveals that the efficiency of finite element simulation of the boring tools when equipped with particles such as copper, stainless steel and a combination of both. In this study, the newly modified boring tool holder with particle damping was simulated using ANSYS12.0 with and without particles. The aim of this study is to enhance the structural rigidity through particle damping thus avoiding the occurrence of resonance in the boring tool during machining.

Keywords: boring bar, copper-stainless steel, chatter, particle damping

Procedia PDF Downloads 461

Authors: Priyanka Mondal, Santosh K. Sarkar

Abstract:

The present study investigated the spatiotemporal distribution and ecological risk assessment of trace elements of surface sediments (top 0 - 5 cm; grain size ≤ 0.63 µm) in relevance to sediment quality characteristics along the Ganges River Estuary, India. Sediment samples were collected during ebb tide from intertidal regions covering seven sampling sites of diverse environmental stresses. The elements were analyzed with the help of ICPAES. This positive, mixohaline, macro-tidal estuary has global significance contributing ecological and economic services. Presence of fine-clayey particle (47.03%) enhances the adsorption as well as transportation of trace elements. There is a remarkable inter-metallic variation (mg kg-1 dry weight) in the distribution pattern in the following manner: Al (31801± 15943) > Fe (23337± 7584) > Mn (461±147) > S(381±235) > Zn(54 ±18) > V(43 ±14) > Cr(39 ±15) > As (34±15) > Cu(27 ±11) > Ni (24 ±9) > Se (17 ±8) > Co(11 ±3) > Mo(10 ± 2) > Hg(0.02 ±0.01). An overall trend of enrichment of majority of trace elements was very much pronounced at the site Lot 8, ~ 35km upstream of the estuarine mouth. In contrast, the minimum concentration was recorded at site Gangasagar, mouth of the estuary, with high energy profile. The prevalent variations in trace element distribution are being liable for a set of cumulative factors such as hydrodynamic conditions, sediment dispersion pattern and textural variations as well as non-homogenous input of contaminants from point and non-point sources. In order to gain insight into the trace elements distribution, accumulation, and their pollution status, geoaccumulation index (Igeo) and enrichment factor (EF) were used. The Igeo indicated that surface sediments were moderately polluted with As (0.60) and Mo (1.30) and strongly contaminated with Se (4.0). The EF indicated severe pollution of Se (53.82) and significant pollution of As (4.05) and Mo (6.0) and indicated the influx of As, Mo and Se in sediments from anthropogenic sources (such as industrial and municipal sewage, atmospheric deposition, agricultural run-off, etc.). The significant role of the megacity Calcutta in relevance to the untreated sewage discharge, atmospheric inputs and other anthropogenic activities is worthwhile to mention. The ecological risk for different trace elements was evaluated using sediment quality guidelines, effects range low (ERL), and effect range median (ERM). The concentration of As, Cu and Ni at 100%, 43% and 86% of the sampling sites has exceeded the ERL value while none of the element concentration exceeded ERM. The potential ecological risk index values revealed that As at 14.3% of the sampling sites would pose relatively moderate risk to benthic organisms. The effective role of finer clay particles for trace element distribution was revealed by multivariate analysis. The authors strongly recommend regular monitoring emphasizing on accurate appraisal of the potential risk of trace elements for effective and sustainable management of this estuarine environment.

Keywords: pollution assessment, sediment contamination, sediment quality, trace elements

Procedia PDF Downloads 257

Authors: Husam Kaid, Abdulrahman Al-Ahmari, Zhiwu Li

Abstract:

This paper develops a robust deadlock control technique for shared and unreliable resources in automated manufacturing systems (AMSs) based on structural analysis and colored Petri nets, which consists of three steps. The first step involves using strict minimal siphon control to create a live (deadlock-free) system that does not consider resource failure. The second step uses an approach based on colored Petri net, in which all monitors designed in the first step are merged into a single monitor. The third step addresses the deadlock control problems caused by resource failures. For all resource failures in the Petri net model a common recovery subnet based on colored petri net is proposed. The common recovery subnet is added to the obtained system at the second step to make the system reliable. The proposed approach is evaluated using an AMS from the literature. The results show that the proposed approach can be applied to an unreliable complex Petri net model, has a simpler structure and less computational complexity, and can obtain one common recovery subnet to model all resource failures.

Keywords: automated manufacturing system, colored Petri net, deadlocks, siphon

Procedia PDF Downloads 129

Authors: Rakesh Choudhary, Ajay Kumar, Deepak Sharma

Abstract:

The main goal of this paper is to investigate the challenges and benefits of IoT integration in biogas production. This overview explains how the inclusion of IoT can enhance biogas production efficiency. Therefore, such collected data can be explored by advanced analytics, including Artificial intelligence (AI) and Machine Learning (ML) algorithms, consequently improving bio-energy processes. To boost biogas generation efficiency, this report examines the use of IoT devices for real-time data collection on key parameters, e.g., pH, temperature, gas composition, and microbial growth. Real-time monitoring through big data has made it possible to detect diverse, complex trends in the process of producing biogas. The Informed by advanced analytics can also help in improving bio-energy production as well as optimizing operational conditions. Moreover, IoT allows remote observation, control and management, which decreases manual intervention needed whilst increasing process effectiveness. Such a paradigm shift in the incorporation of IoT technologies into biogas production systems helps to achieve higher productivity levels as well as more practical biomass quality biomethane through real-time monitoring-based proactive decision-making, thus driving continuous performance improvement.

Keywords: internet of things, biogas, renewable energy, sustainability, anaerobic digestion, real-time monitoring, optimization

Procedia PDF Downloads 21

Authors: Abe Degale D., Cheng Jian

Abstract:

When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively.

Keywords: violence detection, faster RCNN, transfer learning and, surveillance video

Procedia PDF Downloads 110

Authors: Ayliana Dharmawan, Heng Yong Sheng, Zhang Xiaojin, Tan Thai Lian

Abstract:

To facilitate discharge planning, doctors are presently required to assign an Estimated Discharge Date (EDD) for each patient admitted to the hospital. This assignment of the EDD is largely based on the doctor’s judgment. This can be difficult for cases which are complex or relatively new to the doctor. It is hypothesized that a data-driven approach would be able to facilitate the doctors to make accurate estimations of the discharge date. Making use of routinely collected data on inpatient discharges between January 2013 and May 2016, a predictive model was developed using machine learning techniques to predict the Length of Stay (and hence the EDD) of inpatients, at the point of admission. The predictive performance of the model was compared to that of the clinicians using accuracy measures. Overall, the best performing model was found to be able to predict EDD with an accuracy improvement in Average Squared Error (ASE) by -38% as compared to the first EDD determined by the present method. It was found that important predictors of the EDD include the provisional diagnosis code, patient’s age, attending doctor at admission, medical specialty at admission, accommodation type, and the mean length of stay of the patient in the past year. The predictive model can be used as a tool to accurately predict the EDD.

Keywords: inpatient, estimated discharge date, EDD, prediction, data-driven

Procedia PDF Downloads 174

Authors: Imine Zakaria, Meftah Sidi Mohamed El Amine

Abstract:

One of the most vital aerodynamic organs of a flying machine is the wing, which allows it to fly in the air efficiently. The flow around the wing is very sensitive to changes in the angle of attack. Beyond a value, there is a phenomenon of the boundary layer separation on the upper surface, which causes instability and total degradation of aerodynamic performance called a stall. However, controlling flow around an airfoil has become a researcher concern in the aeronautics field. There are two techniques for controlling flow around a wing to improve its aerodynamic performance: passive and active controls. Blowing and suction are among the active techniques that control the boundary layer separation around an airfoil. Their objective is to give energy to the air particles in the boundary layer separation zones and to create vortex structures that will homogenize the velocity near the wall and allow control. Blowing and suction have long been used as flow control actuators around obstacles. In 1904 Prandtl applied a permanent blowing to a cylinder to delay the boundary layer separation. In the present study, several numerical investigations have been developed to predict a turbulent flow around an aerodynamic profile. CFD code was used for several angles of attack in order to validate the present work with that of the literature in the case of a clean profile. The variation of the lift coefficient CL with the momentum coefficient

Keywords: CFD, control flow, lift, slot

Procedia PDF Downloads 200

Authors: Debdatta Mukherjee, Abhiman Das, Amit Garg

Abstract:

In recent years, there is a growing interest about corporate social responsibility of firms in both academic literature and business world. Since business forms a part of society incorporating socio-environment concerns into its value chain, activities are vital for ensuring mutual sustainability and prosperity. But, until now most of the works have been either descriptive or normative rather than positivist in tone. Even the few ones with a positivist approach have mostly studied the link between corporate financial performance and corporate social performance. However, these studies have been severely criticized by many eminent authors on grounds that they lack a theoretical basis for their findings. They have also argued that apart from corporate financial performance, there must be certain other crucial influences that are likely to determine corporate social performance of firms. In fact, several studies have indicated that firms operating in distinct national institutions show significant variations in the corporate social responsibility practices that they undertake. This clearly suggests that the institutional context of a country in which the firms operate is a key determinant of corporate social performance of firms. Therefore, this paper uses an institutional framework to understand why corporate social performance of firms vary across countries. It examines the impact of country level institutions on corporate social performance using a sample of 3240 global publicly-held firms across 33 countries covering the period 2010-2015. The country level institutions include public institutions, private institutions, markets and capacity to innovate. Econometric Analysis has been mainly used to assess this impact. A three way panel data analysis using fixed effects has been used to test and validate appropriate hypotheses. Most of the empirical findings confirm our hypotheses and the economic significance indicates the specific impact of each variable and their importance relative to others. The results suggest that institutional determinants like ethical behavior of private institutions, goods market, labor market and innovation capacity of a country are significantly related to the corporate social performance of firms. Based on our findings, few implications for policy makers from across the world have also been suggested. The institutions in a country should promote competition. The government should use policy levers for upgrading home demands, like setting challenging yet flexible safety, quality and environment standards, and framing policies governing buyer information, providing innovative recourses to low quality goods and services and promoting early adoption of new and technologically advanced products. Moreover, the institution building in a country should be such that they facilitate and improve the capacity of firms to innovate. Therefore, the proposed study argues that country level institutions impact corporate social performance of firms, empirically validates the same, suggest policy implications and attempts to contribute to an extended understanding of corporate social responsibility and corporate social performance in a multinational context.

Keywords: corporate social performance, corporate social responsibility, institutions, markets

Procedia PDF Downloads 167

Authors: Fei Gao, Rodolfo C. Raga Jr.

Abstract:

This research proposal will ascertain the major risk factors for diabetes and to design a predictive model for risk assessment. The project aims to improve diabetes early detection and management by utilizing data science techniques, which may improve patient outcomes and healthcare efficiency. The phase relation values of each attribute were used to analyze and choose the attributes that might influence the examiner's survival probability using Diabetes Health Indicators Dataset from Kaggle’s data as the research data. We compare and evaluate eight machine learning algorithms. Our investigation begins with comprehensive data preprocessing, including feature engineering and dimensionality reduction, aimed at enhancing data quality. The dataset, comprising health indicators and medical data, serves as a foundation for training and testing these algorithms. A rigorous cross-validation process is applied, and we assess their performance using five key metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). After analyzing the data characteristics, investigate their impact on the likelihood of diabetes and develop corresponding risk indicators.

Keywords: diabetes, risk factors, predictive model, risk assessment, data science techniques, early detection, data analysis, Kaggle

Procedia PDF Downloads 77

Authors: Khaled M. Alhawiti

Abstract:

This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition.

Keywords: speech recognition, acoustic phonetic, artificial intelligence, hidden markov models (HMM), statistical models of speech recognition, human machine performance

Procedia PDF Downloads 478

Authors: Duyen Lam, Thuong Hoang, Atul Sajjanhar, Feifei Chen

Abstract:

With the emerging interactive technology applications helping users connect progressively with cultural artefacts in new approaches, the cultural heritage sector gains significantly. The interactive apps’ issues can be tested via several techniques, including usability surveys and usability evaluations. The severe usability problems for museums’ interactive technologies commonly involve interactions, control, and navigation processes. This study confirms the low quality of being immersive for audio guides in navigating the exhibition and involving experience in the virtual environment, which are the most vital features of new interactive technologies such as AR and VR. In addition, our usability surveys and heuristic evaluations disclosed many usability issues of these interactive technologies relating to interaction functions. Additionally, we use the Wayback Machine to examine what interactive apps/technologies were deployed on these websites during the physical visits limited due to the COVID-19 pandemic lockdown. Based on those inputs, we propose the reflection framework to enhance the UX in the cultural heritage domain with detailed guidelines.

Keywords: framework, user experience, cultural heritage, interactive technology, museum, COVID-19 pandemic, usability survey, heuristic evaluation, guidelines

Procedia PDF Downloads 69

Authors: A. S. Vidyashree, C. M. Kalleshwaraswamy, R. Asokan, H. M. Mahadevaswamy

Abstract:

Termites are very vital ecological thespians in tropical ecosystem, having been designated as “ecosystem engineers”, due to their significant role in providing soil ecosystem services. Despite their importance, our understanding of a number of their basic biological processes in termites is extremely limited. Developing a better understanding of termite biology is closely dependent upon consistent species identification. At present, identification of termites is relied on soldier castes. But for many species, soldier caste is not reported, that creates confusion in identification. The use of molecular markers may be helpful in estimating phylogenetic relatedness between the termite species and estimating genetic differentiation among local populations within each species. To understand this, termites samples were collected from various places of Western Ghats covering four states namely Karnataka, Kerala, Tamil Nadu, Maharashtra during 2013-15. Termite samples were identified based on their morphological characteristics, molecular characteristics, or both. Survey on the termite fauna in Karnataka, Kerala, Maharashtra and Tamil Nadu indicated the presence of a 16 species belongs to 4 subfamilies under two families viz., Rhinotermitidae and Termitidae. Termititidae was the dominant family which was belonging to 4 genera and four subfamilies viz., Macrotermitinae, Amitermitinae, Nasutitermitinae and Termitinae. Amitermitinae had three species namely, Microcerotermes fletcheri, M. pakistanicus and Speculitermes sinhalensis. Macrotermitinae had the highest number of species belonging two genera, namely Microtermes and Odontotermes. Microtermes genus was with only one species i.e., Microtermes obesi. The genus Odontotermes was represented by the highest number of species (07), namely, O. obesus was the dominant (41 per cent) and the most widely distributed species in Karnataka, Karala, Maharashtra and Tamil nadu followed by O. feae (19 per cent), O.assmuthi (11 per cent) and others like O. bellahunisensis O. horni O. redemanni, O. yadevi. Nasutitermitinae was represented by two genera namely Nasutitermes anamalaiensis and Trinervitermes biformis. Termitinae subfamily was represented by Labiocapritermes distortus. Rhinotermitidae was represented by single subfamily Heterotermetinae. In Heterotermetinae, two species namely Heterotermes balwanthi and H. malabaricus were recorded. Genetic relationship among termites collected from various locations of Western Ghats of India was characterized based on mitochondrial DNA sequences (12S, 16S, and COII). Sequence analysis and divergence among the species was assessed. These results suggest that the use of both molecular and morphological approaches is crucial in ensuring accurate species identification. Efforts were made to understand their evolution and to address the ambiguities in morphological taxonomy. The implication of the study in revising the taxonomy of Indian termites, their characterization and molecular comparisons between the sequences are discussed.

Keywords: isoptera, mitochondrial DNA sequences, rhinotermitidae, termitidae, Western ghats

Procedia PDF Downloads 267

Authors: Priyadarsini Samal, Rajesh Singla

Abstract:

Many mobile games come with the benefits of entertainment by introducing stress to the human brain. In recognizing this mental stress, the brain-computer interface (BCI) plays an important role. It has various neuroimaging approaches which help in analyzing the brain signals. Electroencephalogram (EEG) is the most commonly used method among them as it is non-invasive, portable, and economical. Here, this paper investigates the pattern in brain signals when introduced with mental stress. Two healthy volunteers played a game whose aim was to search hidden words from the grid, and the levels were chosen randomly. The EEG signals during gameplay were recorded to investigate the impacts of stress with the changing levels from easy to medium to hard. A total of 16 features of EEG were analyzed for this experiment which includes power band features with relative powers, event-related desynchronization, along statistical features. Support vector machine was used as the classifier, which resulted in an accuracy of 93.9% for three-level stress analysis; for two levels, the accuracy of 92% and 98% are achieved. In addition to that, another game that was similar in nature was played by the volunteers. A suitable regression model was designed for prediction where the feature sets of the first and second game were used for testing and training purposes, respectively, and an accuracy of 73% was found.

Keywords: brain computer interface, electroencephalogram, regression model, stress, word search

Procedia PDF Downloads 188

Authors: Jacqueline Rose T. Alipo-on, Francesca Isabelle F. Escobar, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar Al Dahoul

Abstract:

Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases, which are considered one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis of ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heartbeat types. The dataset used in this work is the synthetic MIT-BIH Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: heartbeat classification, convolutional neural network, electrocardiogram signals, generative adversarial networks, long short-term memory, ResNet-50

Procedia PDF Downloads 130

Authors: Sonia Perez-Gamboa, Qingquan Sun, Yan Zhang

Abstract:

Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption.

Keywords: deep learning, LSTM, CNN, human activity recognition, inertial sensor

Procedia PDF Downloads 152

Authors: Md Serajul Islam

Abstract:

High Dose Rate (HDR) Brachytherapy is used for cancer patients. In our country’s prospect, we are using only cervices and breast cancer treatment by using HDR. The air kerma rate in air at a reference distance of less than a meter from the source is the recommended quantity for the specification of gamma ray source Ir-192 in brachytherapy. The absorbed dose for the patients is directly proportional to the air kerma rate. Therefore the air kerma rate should be determined before the first use of the source on patients by qualified medical physicist who is independent from the source manufacturer. The air kerma rate will then be applied in the calculation of the dose delivered to patients in their planning systems. In practice, high dose rate (HDR) Ir-192 afterloader machines are mostly used in brachytherapy treatment. Currently, HDR-Co-60 increasingly comes into operation too. The essential advantage of the use of Co-60 sources is its longer half-life compared to Ir-192. The use of HDRCo-60 afterloading machines is also quite interesting for developing countries. This work describes the dosimetry at HDR afterloading machines according to the protocols IAEA-TECDOC-1274 (2002) with the nuclides Ir-192 and Co-60. We have used 3 different measurement methods (with a ring chamber, with a solid phantom and in free air and with a well chamber) in dependence of each of the protocols. We have shown that the standard deviations of the measured air kerma rate for the Co-60 source are generally larger than those of the Ir-192 source. The measurements with the well chamber had the lowest deviation from the certificate value. In all protocols and methods, the deviations stood for both nuclides by a maximum of about 1% for Ir-192 and 2.5% for Co-60-Sources respectively.

Keywords: Ir-192 source, cancer, patients, cheap treatment cost

Procedia PDF Downloads 239

Authors: P. Serrano-Pérez, M. C. Rodríguez-Molina, C. Palo, E. Palo, A. Lacasa

Abstract:

Phytophthora nicotianae is the principal causal agent of root and crown rot disease of red pepper plants in Extremadura (Western Spain). There is a need to develop a biologically-based method of soil disinfestation that facilitates profitable and sustainable production without the use of chemical fumigants. Anaerobic Soil Disinfestation (ASD), as well know as biodisinfestation, has been shown to control a wide range of soil-borne pathogens and nematodes in numerous crop production systems. This method implies soil wetting, incorporation of a easily decomposable carbon-rich organic amendment and covering with plastic film for several weeks. ASD with rapeseed cake (var. Tocatta, a glucosinolates-free variety) used as C-source was assayed in spring 2014, before the pepper crop establishment. The field experiment was conducted at the Agricultural Research Centre Finca La Orden (Southwestern Spain) and the treatments were: rapeseed cake (RCP); rapeseed cake without plastic cover (RC); control non-amendment (CP) and control non-amendment without plastic cover (C). The experimental design was a randomized complete block design with four replicates and a plot size of 5 x 5 m. On 26 March, rapeseed cake (1 kg·m-2) was incorporated into the soil with a rotovator. Biological probes with the inoculum were buried at 15 and 30-cm depth (biological probes were previously prepared with 100 g of disinfected soil inoculated with chlamydospores (chlam) of P. nicotianae P13 isolate [100 chlam·g-1 of soil] and wrapped in agryl cloth). Sprinkler irrigation was run until field capacity and the corresponding plots were covered with transparent plastic (PE 0.05 mm). On 6 May plastics were removed, the biological probes were dug out and a bioassay was established. One pepper seedling at the 2 to 4 true-leaves stage was transplanted in the soil from each biological probe. Plants were grown in a climatic chamber and disease symptoms were recorded every week during 2 months. Fragments of roots and crown of symptomatic plants were analyzed on NARPH media and soil from rizospheres was analyzed using carnation petals as baits. Results of “survival” were expressed as the percentage of soil samples where P. nicotianae was detected and results of “infectivity” were expressed as the percentage of diseased plants. No differences were detected in deep effect. Infectivity of P. nicotianae chlamydospores was successfully reduced in RCP treatment (4.2% of infectivity) compared with the controls (41.7% of infectivity). The pattern of survival was similar to infectivity observed by the bioassay: 21% of survival in RCP; 79% in CP; 83% in C and 87% in RC. Although ASD may be an effective alternative to chemical fumigants to pest management, more research is necessary to show their impact on the microbial community and chemistry of the soil.

Keywords: biodisinfestation, BSD, soil fumigant alternatives, organic amendments

Procedia PDF Downloads 219

Authors: Vivek Krishna, Nikhil Jain, A. Mary Posonia A., Albert Mayan J

Abstract:

Terrorism and insurgency are significant global issues that require constant attention and effort from governments and scientists worldwide. To combat these threats, nations invest billions of dollars in developing new defensive technologies to protect civilians. Breakthroughs in vehicle automation have led to the use of sophisticated machines for many dangerous and critical anti-terrorist activities. Our concept of an "Unmanned Ground Vehicle" can carry out tasks such as border security, surveillance, mine detection, and active combat independently or in tandem with human control. The robot's movement can be wirelessly controlled by a person in a distant location or can travel to a pre-programmed destination autonomously in situations where personal control is not feasible. Our defence system comprises two units: the control unit that regulates mobility and the motion tracking unit. The remote operator robot uses the camera's live visual feed to manually operate both units, and the rover can automatically detect movement. The rover is operated by manpower who controls it using a joystick or mouse, and a wireless modem enables a soldier in a combat zone to control the rover via an additional controller feature.

Keywords: robotics, computer vision, Machine learning, Artificial intelligence, future of AI

Procedia PDF Downloads 126

Authors: Owen Harrington-Fernandez, Pilar Alderete-Diez

Abstract:

Up until recently, translation studies treated children’s literature as something of a marginal preoccupation, but the recent attention that this text type has attracted suggests that it may be fertile ground for research. This paper contributes to this new research avenue by applying a sociolinguistic theoretical framework to explore issues around the intersubjective co-construction of identity in the American English translation of the Spanish children’s story, Manolito Gafotas. The application of Bucholtz and Hall’s framework achieves two objectives: (1) it identifies shifts in the translation of the main character’s behaviour as culturally and morally motivated manipulations, and (2) it demonstrates how the context of translation becomes the very censorship machine that delegitimises the identity of the main character, and, concomitantly, the identity of the implied reader(s). If we take identity to be an intersubjective phenomenon, then it logicall follows that expurgating the identity of the main character necessarily shifts the identity of the implied reader(s) also. It is a double censorship of identity carried out under the auspices of an intellectual colonisation of a Spanish text. After reporting on the results of the analysis, the paper ends by raising the question of censorship in translation, and, more specifically, in children’s literature, in order to promote debate around this topic.

Keywords: censorship, identity, sociolinguistics, translation

Procedia PDF Downloads 261

Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

Abstract:

Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

Procedia PDF Downloads 112

Authors: Héctor Rivera-Gómez, Eva Selene Hernández-Gress, Oscar Montaño-Arango, Jose Ramon Corona-Armenta

Abstract:

This research studies the joint production, maintenance and subcontracting control policy for an unreliable deteriorating manufacturing system. Production activities are controlled by a derivation of the Hedging Point Policy, and given that the system is subject to deterioration, it reduces progressively its capacity to satisfy product demand. Multiple deterioration effects are considered, reflected mainly in the quality of the parts produced and the reliability of the machine. Subcontracting is available as support to satisfy product demand; also overhaul maintenance can be conducted to reduce the effects of deterioration. The main objective of the research is to determine simultaneously the production, maintenance and subcontracting rate which minimize the total incurred cost. A stochastic dynamic programming model is developed and solved through a simulation-based approach composed of statistical analysis and optimization with the response surface methodology. The obtained results highlight the strong interactions between production, deterioration and quality which justify the development of an integrated model. A numerical example and a sensitivity analysis are presented to validate our results.

Keywords: subcontracting, optimal control, deterioration, simulation, production planning

Procedia PDF Downloads 580

Authors: Zheng Lulin, Xiong Guoping

Abstract:

In recent years, agriculture, rural development, and peasants are among the top concerns and priorities of the Chinese Government. Several related issues have been paid many attentions by academic communities, including the impacts of corresponding policies on the rural villages, the mechanisms of these impacts, and the future development of rural society. However, most of the researchers focus on single rural policy instead of integral rural policy system. Hence, this dissertation focused on the mechanisms of policies’ influence on rural changes through a case study from Changfu Village in central Guangxi Province, China, to propose the optimized suggestions for rural development. Forty-three relevant pivotal policies of significant influence on rural development are summarized from literature and documents, covering five aspects of agricultural production, rural living security, open rural markets, rural household registration systems, and farmland transferring. Besides, having been live in this area for more than 20 years, researchers obtain the basic information about changing the social connection between citizens and villagers, the habitat of villagers by years of informal interviews. Furthermore, more than 200 questionnaires are given to villagers to analyze the changing of their personal and family information. The summary of rural policies revealed that the development trend of public rural policies followed the U-shape curve and these policies are characterized by economic intentions and operative economy. Report of questionnaires and interviews show that the development of rural economy was promoted greatly by public policies. Firstly, Social communication and rural culture were affected to a certain extent. Secondly, the educational level of rural individuals was significantly enhanced, whereas the quality of population had limited progress. Finally, the freedom of occupational choice for rural individuals into cities was greater than before, but still restricted by the class solidification of social background, resulting in more obstacles for rural individuals to settle down in cities. From what we discuss about, we may reach the conclusion on several perspectives: Firstly, the impact of the rural policies has a significant role in promoting the economy development of the rural area. However, separations between rural and urban area are still a major problem since rural policy contributed little to improve the rural population quality. Therefore, in the future, providing high quality educational facilities including teachers, libraries, and opportunities of broadening their knowledge base are key issues of future rural policy. Secondly, the development of rural economy would be a lack of driving force for further improvement owning to the fact that working hard couldn’t get more improvement. In the future, public policies should support the rural development of culture, technology, and personal qualities to create favorable social environment for the free increase of rural population.

Keywords: changing of rural area, rural development of China, rural policy, social environment

Procedia PDF Downloads 431

Authors: Noor Azwa Azreen Binti Abd. Aziz, Muhamad Zaim Bin Mohd Rozi

Abstract:

Moving towards the 4th Industrial Revolution, artificial intelligence (AI) is now more popular than ever. This subject is gaining significance every day and is continually expanding, often merging with other fields. Instead of merely being passive observers, there are benefits to understanding modern technology by delving into its inner workings. However, in a world teeming with digital information, the impact of AI on the spread of disinformation has garnered significant attention. The dissemination of inaccurate or misleading information is referred to as misinformation, posing a serious threat to democratic society, public debate, and individual decision-making. This article delves deep into the connection between AI and the dissemination of false information, exploring its potential, risks, and ethical issues as AI technology advances. The rise of AI has ushered in a new era in the dissemination of misinformation as AI-driven technologies are increasingly responsible for curating, recommending, and amplifying information on online platforms. While AI holds the potential to enhance the detection and mitigation of misinformation through natural language processing and machine learning, it also raises concerns about the amplification and propagation of false information. AI-powered deepfake technology, for instance, can generate hyper-realistic videos and audio recordings, making it increasingly challenging to discern fact from fiction.

Keywords: artificial intelligence, digital information, disinformation, ethical issues, misinformation

Procedia PDF Downloads 95