Search results for: vehicular ad hoc networks
2100 O-LEACH: The Problem of Orphan Nodes in the LEACH of Routing Protocol for Wireless Sensor Networks
Authors: Wassim Jerbi, Abderrahmen Guermazi, Hafedh Trabelsi
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The optimum use of coverage in wireless sensor networks (WSNs) is very important. LEACH protocol called Low Energy Adaptive Clustering Hierarchy, presents a hierarchical clustering algorithm for wireless sensor networks. LEACH is a protocol that allows the formation of distributed cluster. In each cluster, LEACH randomly selects some sensor nodes called cluster heads (CHs). The selection of CHs is made with a probabilistic calculation. It is supposed that each non-CH node joins a cluster and becomes a cluster member. Nevertheless, some CHs can be concentrated in a specific part of the network. Thus, several sensor nodes cannot reach any CH. to solve this problem. We created an O-LEACH Orphan nodes protocol, its role is to reduce the sensor nodes which do not belong the cluster. The cluster member called Gateway receives messages from neighboring orphan nodes. The gateway informs CH having the neighboring nodes that not belong to any group. However, Gateway called (CH') attaches the orphaned nodes to the cluster and then collected the data. O-Leach enables the formation of a new method of cluster, leads to a long life and minimal energy consumption. Orphan nodes possess enough energy and seeks to be covered by the network. The principal novel contribution of the proposed work is O-LEACH protocol which provides coverage of the whole network with a minimum number of orphaned nodes and has a very high connectivity rates.As a result, the WSN application receives data from the entire network including orphan nodes. The proper functioning of the Application requires, therefore, management of intelligent resources present within each the network sensor. The simulation results show that O-LEACH performs better than LEACH in terms of coverage, connectivity rate, energy and scalability.Keywords: WSNs; routing; LEACH; O-LEACH; Orphan nodes; sub-cluster; gateway; CH’
Procedia PDF Downloads 3672099 Seismic Perimeter Surveillance System (Virtual Fence) for Threat Detection and Characterization Using Multiple ML Based Trained Models in Weighted Ensemble Voting
Authors: Vivek Mahadev, Manoj Kumar, Neelu Mathur, Brahm Dutt Pandey
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Perimeter guarding and protection of critical installations require prompt intrusion detection and assessment to take effective countermeasures. Currently, visual and electronic surveillance are the primary methods used for perimeter guarding. These methods can be costly and complicated, requiring careful planning according to the location and terrain. Moreover, these methods often struggle to detect stealthy and camouflaged insurgents. The object of the present work is to devise a surveillance technique using seismic sensors that overcomes the limitations of existing systems. The aim is to improve intrusion detection, assessment, and characterization by utilizing seismic sensors. Most of the similar systems have only two types of intrusion detection capability viz., human or vehicle. In our work we could even categorize further to identify types of intrusion activity such as walking, running, group walking, fence jumping, tunnel digging and vehicular movements. A virtual fence of 60 meters at GCNEP, Bahadurgarh, Haryana, India, was created by installing four underground geophones at a distance of 15 meters each. The signals received from these geophones are then processed to find unique seismic signatures called features. Various feature optimization and selection methodologies, such as LightGBM, Boruta, Random Forest, Logistics, Recursive Feature Elimination, Chi-2 and Pearson Ratio were used to identify the best features for training the machine learning models. The trained models were developed using algorithms such as supervised support vector machine (SVM) classifier, kNN, Decision Tree, Logistic Regression, Naïve Bayes, and Artificial Neural Networks. These models were then used to predict the category of events, employing weighted ensemble voting to analyze and combine their results. The models were trained with 1940 training events and results were evaluated with 831 test events. It was observed that using the weighted ensemble voting increased the efficiency of predictions. In this study we successfully developed and deployed the virtual fence using geophones. Since these sensors are passive, do not radiate any energy and are installed underground, it is impossible for intruders to locate and nullify them. Their flexibility, quick and easy installation, low costs, hidden deployment and unattended surveillance make such systems especially suitable for critical installations and remote facilities with difficult terrain. This work demonstrates the potential of utilizing seismic sensors for creating better perimeter guarding and protection systems using multiple machine learning models in weighted ensemble voting. In this study the virtual fence achieved an intruder detection efficiency of over 97%.Keywords: geophone, seismic perimeter surveillance, machine learning, weighted ensemble method
Procedia PDF Downloads 742098 Evaluating the Perception of Roma in Europe through Social Network Analysis
Authors: Giulia I. Pintea
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The Roma people are a nomadic ethnic group native to India, and they are one of the most prevalent minorities in Europe. In the past, Roma were enslaved and they were imprisoned in concentration camps during the Holocaust; today, Roma are subject to hate crimes and are denied access to healthcare, education, and proper housing. The aim of this project is to analyze how the public perception of the Roma people may be influenced by antiziganist and pro-Roma institutions in Europe. In order to carry out this project, we used social network analysis to build two large social networks: The antiziganist network, which is composed of institutions that oppress and racialize Roma, and the pro-Roma network, which is composed of institutions that advocate for and protect Roma rights. Measures of centrality, density, and modularity were obtained to determine which of the two social networks is exerting the greatest influence on the public’s perception of Roma in European societies. Furthermore, data on hate crimes on Roma were gathered from the Organization for Security and Cooperation in Europe (OSCE). We analyzed the trends in hate crimes on Roma for several European countries for 2009-2015 in order to see whether or not there have been changes in the public’s perception of Roma, thus helping us evaluate which of the two social networks has been more influential. Overall, the results suggest that there is a greater and faster exchange of information in the pro-Roma network. However, when taking the hate crimes into account, the impact of the pro-Roma institutions is ambiguous, due to differing patterns among European countries, suggesting that the impact of the pro-Roma network is inconsistent. Despite antiziganist institutions having a slower flow of information, the hate crime patterns also suggest that the antiziganist network has a higher impact on certain countries, which may be due to institutions outside the political sphere boosting the spread of antiziganist ideas and information to the European public.Keywords: applied mathematics, oppression, Roma people, social network analysis
Procedia PDF Downloads 2682097 Reading and Writing Memories in Artificial and Human Reasoning
Authors: Ian O'Loughlin
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Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary.Keywords: artificial reasoning, human memory, machine learning, neural networks
Procedia PDF Downloads 2652096 Neural Networks and Genetic Algorithms Approach for Word Correction and Prediction
Authors: Rodrigo S. Fonseca, Antônio C. P. Veiga
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Aiming at helping people with some movement limitation that makes typing and communication difficult, there is a need to customize an assistive tool with a learning environment that helps the user in order to optimize text input, identifying the error and providing the correction and possibilities of choice in the Portuguese language. The work presents an Orthographic and Grammatical System that can be incorporated into writing environments, improving and facilitating the use of an alphanumeric keyboard, using a prototype built using a genetic algorithm in addition to carrying out the prediction, which can occur based on the quantity and position of the inserted letters and even placement in the sentence, ensuring the sequence of ideas using a Long Short Term Memory (LSTM) neural network. The prototype optimizes data entry, being a component of assistive technology for the textual formulation, detecting errors, seeking solutions and informing the user of accurate predictions quickly and effectively through machine learning.Keywords: genetic algorithm, neural networks, word prediction, machine learning
Procedia PDF Downloads 1862095 Decision Support System for Fetus Status Evaluation Using Cardiotocograms
Authors: Oyebade K. Oyedotun
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The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts.Keywords: decision support, cardiotocogram, classification, neural networks
Procedia PDF Downloads 3252094 Study of Energy Efficient and Quality of Service Based Routing Protocols in Wireless Sensor Networking
Authors: Sachin Sharma
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A wireless sensor network (WSN) consists of a large number of sensor nodes which are deployed over an area to perform local computations based on information gathered from the surroundings. With the increasing demand for real-time applications in WSN, real-time critical events anticipate an efficient quality-of-service (QoS) based routing for data delivery from the network infrastructure. Hence, maximizing the lifetime of the network through minimizing the energy is an important challenge in WSN; sensors cannot be easily replaced or recharged due to their ad-hoc deployment in a hazardous environment. Considerable research has been focused on developing robust energy efficient QoS based routing protocols. The main focus of this article is primarily on periodical cycling schemes which represent the most compatible technique for energy saving and we also focus on the data-driven approaches that can be used to improve the energy efficiency. Finally, we will make a review on some communication protocols proposed for sensor networks.Keywords: energy efficient, quality of service, wireless sensor networks, MAC
Procedia PDF Downloads 3422093 Automatic Lead Qualification with Opinion Mining in Customer Relationship Management Projects
Authors: Victor Radich, Tania Basso, Regina Moraes
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Lead qualification is one of the main procedures in Customer Relationship Management (CRM) projects. Its main goal is to identify potential consumers who have the ideal characteristics to establish a profitable and long-term relationship with a certain organization. Social networks can be an important source of data for identifying and qualifying leads since interest in specific products or services can be identified from the users’ expressed feelings of (dis)satisfaction. In this context, this work proposes the use of machine learning techniques and sentiment analysis as an extra step in the lead qualification process in order to improve it. In addition to machine learning models, sentiment analysis or opinion mining can be used to understand the evaluation that the user makes of a particular service, product, or brand. The results obtained so far have shown that it is possible to extract data from social networks and combine the techniques for a more complete classification.Keywords: lead qualification, sentiment analysis, opinion mining, machine learning, CRM, lead scoring
Procedia PDF Downloads 772092 Poster : Incident Signals Estimation Based on a Modified MCA Learning Algorithm
Authors: Rashid Ahmed , John N. Avaritsiotis
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Many signal subspace-based approaches have already been proposed for determining the fixed Direction of Arrival (DOA) of plane waves impinging on an array of sensors. Two procedures for DOA estimation based neural networks are presented. First, Principal Component Analysis (PCA) is employed to extract the maximum eigenvalue and eigenvector from signal subspace to estimate DOA. Second, minor component analysis (MCA) is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix. In this paper, we will modify a Minor Component Analysis (MCA(R)) learning algorithm to enhance the convergence, where a convergence is essential for MCA algorithm towards practical applications. The learning rate parameter is also presented, which ensures fast convergence of the algorithm, because it has direct effect on the convergence of the weight vector and the error level is affected by this value. MCA is performed to determine the estimated DOA. Preliminary results will be furnished to illustrate the convergences results achieved.Keywords: Direction of Arrival, neural networks, Principle Component Analysis, Minor Component Analysis
Procedia PDF Downloads 4472091 Social Discussion Networks during the Covid-19 Pandemic: A Study of College Students Core Discussion Groups
Authors: Regan Harper, Song Yang, Douglas Adams
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During the historically unprecedent time of Covid-19 pandemic, we survey college students with social issue generators to measure their core discussion groups. For the total 191 students, we elicit 847 conversation partners (alters) with our five social issue generators such as school closing, facemasks, collegiate sports, race and policing, and social inequality, producing an average of 4.43 alters per respondent. The core discussion groups of our sample are very gender balanced, with female alters slightly outnumbering male alters. However, the core discussion groups are racially homogenous, consisting of mostly white students (around or above 80 percent). Explanatory analyses reveal that gender and race of respondents significantly impact the size, gender composition, and racial composition of their core discussion networks. We discuss those major findings and implications of future studies in our conclusion section.Keywords: core discussion groups, social issue generators, ego-centric network, Covid-19 pandemic
Procedia PDF Downloads 762090 A Multi Agent Based Protection Scheme for Smart Distribution Network in Presence of Distributed Energy Resources
Authors: M. R. Ebrahimi, B. Mahdaviani
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Conventional electric distribution systems are radial in nature, supplied at one end through a main source. These networks generally have a simple protection system usually implemented using fuses, re-closers, and over-current relays. Recently, great attention has been paid to applying Distributed energy resources (DERs) throughout electric distribution systems. Presence of such generation in a network leads to losing coordination of protection devices. Therefore, it is desired to develop an algorithm which is capable of protecting distribution systems that include DER. On the other hand smart grid brings opportunities to the power system. Fast advancement in communication and measurement techniques accelerates the development of multi agent system (MAS). So in this paper, a new approach for the protection of distribution networks in the presence of DERs is presented base on MAS. The proposed scheme has been implemented on a sample 27-bus distribution network.Keywords: distributed energy resource, distribution network, protection, smart grid, multi agent system
Procedia PDF Downloads 6032089 Knowledge Representation Based on Interval Type-2 CFCM Clustering
Authors: Lee Myung-Won, Kwak Keun-Chang
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This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homogeneity between the clustered patterns from the IT2 contexts produced in the output space. Furthermore, we can obtain the automatic knowledge representation in the design of Radial Basis Function Networks (RBFN), Linguistic Model (LM), and Adaptive Neuro-Fuzzy Networks (ANFN) from the numerical input-output data pairs. We shall focus on a design of ANFN in this paper. The experimental results on an estimation problem of energy performance reveal that the proposed method showed a good knowledge representation and performance in comparison with the previous works.Keywords: IT2-FCM, IT2-CFCM, context-based fuzzy clustering, adaptive neuro-fuzzy network, knowledge representation
Procedia PDF Downloads 3172088 Modeling and Stability Analysis of Viral Propagation in Wireless Mesh Networking
Authors: Haowei Chen, Kaiqi Xiong
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This paper aims to answer how malware will propagate in Wireless Mesh Networks (WMNs) and how communication radius and distributed density of nodes affects the process of spreading. The above analysis is essential for devising network-wide strategies to counter malware. We answer these questions by developing an improved dynamical system that models malware propagation in the area where nodes were uniformly distributed. The proposed model captures both the spatial and temporal dynamics regarding the malware spreading process. Equilibrium and stability are also discussed based on the threshold of the system. If the threshold is less than one, the infected nodes disappear, and if the threshold is greater than one, the infected nodes asymptotically stabilize at the endemic equilibrium. Numerical simulations are investigated about communication radius and distributed density of nodes in WMNs, which allows us to draw various insights that can be used to guide security defense.Keywords: Bluetooth security, malware propagation, wireless mesh networks, stability analysis
Procedia PDF Downloads 912087 Nonlinear Aerodynamic Parameter Estimation of a Supersonic Air to Air Missile by Using Artificial Neural Networks
Authors: Tugba Bayoglu
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Aerodynamic parameter estimation is very crucial in missile design phase, since accurate high fidelity aerodynamic model is required for designing high performance and robust control system, developing high fidelity flight simulations and verification of computational and wind tunnel test results. However, in literature, there is not enough missile aerodynamic parameter identification study for three main reasons: (1) most air to air missiles cannot fly with constant speed, (2) missile flight test number and flight duration are much less than that of fixed wing aircraft, (3) variation of the missile aerodynamic parameters with respect to Mach number is higher than that of fixed wing aircraft. In addition to these challenges, identification of aerodynamic parameters for high wind angles by using classical estimation techniques brings another difficulty in the estimation process. The reason for this, most of the estimation techniques require employing polynomials or splines to model the behavior of the aerodynamics. However, for the missiles with a large variation of aerodynamic parameters with respect to flight variables, the order of the proposed model increases, which brings computational burden and complexity. Therefore, in this study, it is aimed to solve nonlinear aerodynamic parameter identification problem for a supersonic air to air missile by using Artificial Neural Networks. The method proposed will be tested by using simulated data which will be generated with a six degree of freedom missile model, involving a nonlinear aerodynamic database. The data will be corrupted by adding noise to the measurement model. Then, by using the flight variables and measurements, the parameters will be estimated. Finally, the prediction accuracy will be investigated.Keywords: air to air missile, artificial neural networks, open loop simulation, parameter identification
Procedia PDF Downloads 2702086 Embedded Hw-Sw Reconfigurable Techniques For Wireless Sensor Network Applications
Authors: B. Kirubakaran, C. Rajasekaran
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Reconfigurable techniques are used in many engineering and industrial applications for the efficient data transmissions through the wireless sensor networks. Nowadays most of the industrial applications are work for try to minimize the size and cost. During runtime the reconfigurable technique avoid the unwanted hang and delay in the system performance. In recent world Field Programmable Gate Array (FPGA) as one of the most efficient reconfigurable device and widely used for most of the hardware and software reconfiguration applications. In this paper, the work deals with whatever going to make changes in the hardware and software during runtime it’s should not affect the current running process that’s the main objective of the paper our changes be done in a parallel manner at the same time concentrating the cost and power transmission problems during data trans-receiving. Analog sensor (Temperature) as an input for the controller (PIC) through that control the FPGA digital sensors in generalized manner.Keywords: field programmable gate array, peripheral interrupt controller, runtime reconfigurable techniques, wireless sensor networks
Procedia PDF Downloads 4012085 Comparative Analysis of Data Gathering Protocols with Multiple Mobile Elements for Wireless Sensor Network
Authors: Bhat Geetalaxmi Jairam, D. V. Ashoka
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Wireless Sensor Networks are used in many applications to collect sensed data from different sources. Sensed data has to be delivered through sensors wireless interface using multi-hop communication towards the sink. The data collection in wireless sensor networks consumes energy. Energy consumption is the major constraints in WSN .Reducing the energy consumption while increasing the amount of generated data is a great challenge. In this paper, we have implemented two data gathering protocols with multiple mobile sinks/elements to collect data from sensor nodes. First, is Energy-Efficient Data Gathering with Tour Length-Constrained Mobile Elements in Wireless Sensor Networks (EEDG), in which mobile sinks uses vehicle routing protocol to collect data. Second is An Intelligent Agent-based Routing Structure for Mobile Sinks in WSNs (IAR), in which mobile sinks uses prim’s algorithm to collect data. Authors have implemented concepts which are common to both protocols like deployment of mobile sinks, generating visiting schedule, collecting data from the cluster member. Authors have compared the performance of both protocols by taking statistics based on performance parameters like Delay, Packet Drop, Packet Delivery Ratio, Energy Available, Control Overhead. Authors have concluded this paper by proving EEDG is more efficient than IAR protocol but with few limitations which include unaddressed issues likes Redundancy removal, Idle listening, Mobile Sink’s pause/wait state at the node. In future work, we plan to concentrate more on these limitations to avail a new energy efficient protocol which will help in improving the life time of the WSN.Keywords: aggregation, consumption, data gathering, efficiency
Procedia PDF Downloads 4882084 Mobile Network Users Amidst Ultra-Dense Networks in 5G Using an Improved Coordinated Multipoint (CoMP) Technology
Authors: Johnson O. Adeogo, Ayodele S. Oluwole, O. Akinsanmi, Olawale J. Olaluyi
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In this 5G network, very high traffic density in densely populated areas, most especially in densely populated areas, is one of the key requirements. Radiation reduction becomes one of the major concerns to secure the future life of mobile network users in ultra-dense network areas using an improved coordinated multipoint technology. Coordinated Multi-Point (CoMP) is based on transmission and/or reception at multiple separated points with improved coordination among them to actively manage the interference for the users. Small cells have two major objectives: one, they provide good coverage and/or performance. Network users can maintain a good quality signal network by directly connecting to the cell. Two is using CoMP, which involves the use of multiple base stations (MBS) to cooperate by transmitting and/or receiving at the same time in order to reduce the possibility of electromagnetic radiation increase. Therefore, the influence of the screen guard with rubber condom on the mobile transceivers as one major piece of equipment radiating electromagnetic radiation was investigated by mobile network users amidst ultra-dense networks in 5g. The results were compared with the same mobile transceivers without screen guards and rubber condoms under the same network conditions. The 5 cm distance from the mobile transceivers was measured with the help of a ruler, and the intensity of Radio Frequency (RF) radiation was measured using an RF meter. The results show that the intensity of radiation from various mobile transceivers without screen guides and condoms was higher than the mobile transceivers with screen guides and condoms when call conversation was on at both ends.Keywords: ultra-dense networks, mobile network users, 5g, coordinated multi-point.
Procedia PDF Downloads 912083 Instant Fire Risk Assessment Using Artifical Neural Networks
Authors: Tolga Barisik, Ali Fuat Guneri, K. Dastan
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Major industrial facilities have a high potential for fire risk. In particular, the indices used for the detection of hidden fire are used very effectively in order to prevent the fire from becoming dangerous in the initial stage. These indices provide the opportunity to prevent or intervene early by determining the stage of the fire, the potential for hazard, and the type of the combustion agent with the percentage values of the ambient air components. In this system, artificial neural network will be modeled with the input data determined using the Levenberg-Marquardt algorithm, which is a multi-layer sensor (CAA) (teacher-learning) type, before modeling the modeling methods in the literature. The actual values produced by the indices will be compared with the outputs produced by the network. Using the neural network and the curves to be created from the resulting values, the feasibility of performance determination will be investigated.Keywords: artifical neural networks, fire, Graham Index, levenberg-marquardt algoritm, oxygen decrease percentage index, risk assessment, Trickett Index
Procedia PDF Downloads 1322082 Effectiveness of New Digital Tools on Implementing Quality Management System: An Exploratory Study of French Companies
Authors: Takwa Belwakess
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With the wave of the digitization that invades the modern world, communication tools took their place in the world of business. As for organizations, being part of the digital era necessarily involves an evolution of the management style, mainly in processes management, knowing also as quality management system (QMS). For more than 50 years quality management standards have been adopted by organizations to prove their operational and financial performances. We believe that achieving a high-level of communication can lead to better quality management and greater customer satisfaction, which is essential to make sure long-term competitiveness. In this paper, a questionnaire survey was developed to investigate the use of collaboration tools such as Content Management System and Social Networks. Data from more than 100 companies based in France was analyzed, the results show that adopting new digital communication tools while applying quality management practices over a reasonable period, contributed to delivering a better implementation of the QMS for a better business performance.Keywords: communication tools, content management system, digital, effectiveness, French companies, quality management system, quality management practices, social networks
Procedia PDF Downloads 2602081 NFResNet: Multi-Scale and U-Shaped Networks for Deblurring
Authors: Tanish Mittal, Preyansh Agrawal, Esha Pahwa, Aarya Makwana
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Multi-Scale and U-shaped Networks are widely used in various image restoration problems, including deblurring. Keeping in mind the wide range of applications, we present a comparison of these architectures and their effects on image deblurring. We also introduce a new block called as NFResblock. It consists of a Fast Fourier Transformation layer and a series of modified Non-Linear Activation Free Blocks. Based on these architectures and additions, we introduce NFResnet and NFResnet+, which are modified multi-scale and U-Net architectures, respectively. We also use three differ-ent loss functions to train these architectures: Charbonnier Loss, Edge Loss, and Frequency Reconstruction Loss. Extensive experiments on the Deep Video Deblurring dataset, along with ablation studies for each component, have been presented in this paper. The proposed architectures achieve a considerable increase in Peak Signal to Noise (PSNR) ratio and Structural Similarity Index (SSIM) value.Keywords: multi-scale, Unet, deblurring, FFT, resblock, NAF-block, nfresnet, charbonnier, edge, frequency reconstruction
Procedia PDF Downloads 1272080 Health Impacts of Size Segregated Particulate Matter and Black Carbon in Industrial Area of Firozabad
Authors: Kalpana Rajouriya, Ajay Taneja
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Particulates are ubiquitous in the air environment and cause serious threats to human beings, such as lung cancer, Chronic obstructive pulmonary disease (COPD), and Asthma. Particulates mainly arise from industrial effluent, vehicular emission, and other anthropogenic activities. In the glass industrial city Firozabad, real-time monitoring (mass as well as a number) of size segregated Particulate Matter (PM) and black carbon was done by Aerosol Black Carbon Detector (ABCD) and GRIMM portable aerosol Spectrometer at two different sites in which one site is urban, and another is rural. The average mass concentration of size segregated PM during the study period (March & April 2022) was recorded as PM₁₀ (223.73 g/m-³), PM₅.₀ (44.955 g/m-³), PM₂.₅ (59.275 g/m-³), PM₁.₀ (33.02 g/m-³), PM₀.₅ (2.05 g/m-³), and PM₀.₂₅ (2.99 g/m- ³). In number mode, PM concentration was found as PM₁₀ (27.46g/m-³), PM₅.₀ (233.48g/m-³), PM₂.₅ (646.61g/m-³), PM₁.₀ (1134.94 g/m-³), PM₀.₅ (14056.04g/m-³), and PM₀.₂₅ (182906.4 g/m-³). The highest concentration of BC was found in Urban due to the emissions from diesel engines and wood burning while NO2 was highest at the rural sites. The concentrations of PM₁₀ and PM₂.₅ exceeded the NAAQS and WHO guidelines. The sensitive, exposed population may be at risk of developing health-related problems from exposure to size-segregated PM and BC.Keywords: particulate matter, black carbon, NO2, health risk
Procedia PDF Downloads 322079 Adaptive Energy-Aware Routing (AEAR) for Optimized Performance in Resource-Constrained Wireless Sensor Networks
Authors: Innocent Uzougbo Onwuegbuzie
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Wireless Sensor Networks (WSNs) are crucial for numerous applications, yet they face significant challenges due to resource constraints such as limited power and memory. Traditional routing algorithms like Dijkstra, Ad hoc On-Demand Distance Vector (AODV), and Bellman-Ford, while effective in path establishment and discovery, are not optimized for the unique demands of WSNs due to their large memory footprint and power consumption. This paper introduces the Adaptive Energy-Aware Routing (AEAR) model, a solution designed to address these limitations. AEAR integrates reactive route discovery, localized decision-making using geographic information, energy-aware metrics, and dynamic adaptation to provide a robust and efficient routing strategy. We present a detailed comparative analysis using a dataset of 50 sensor nodes, evaluating power consumption, memory footprint, and path cost across AEAR, Dijkstra, AODV, and Bellman-Ford algorithms. Our results demonstrate that AEAR significantly reduces power consumption and memory usage while optimizing path weight. This improvement is achieved through adaptive mechanisms that balance energy efficiency and link quality, ensuring prolonged network lifespan and reliable communication. The AEAR model's superior performance underlines its potential as a viable routing solution for energy-constrained WSN environments, paving the way for more sustainable and resilient sensor network deployments.Keywords: wireless sensor networks (WSNs), adaptive energy-aware routing (AEAR), routing algorithms, energy, efficiency, network lifespan
Procedia PDF Downloads 282078 Requirement Engineering for Intrusion Detection Systems in Wireless Sensor Networks
Authors: Afnan Al-Romi, Iman Al-Momani
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The urge of applying the Software Engineering (SE) processes is both of vital importance and a key feature in critical, complex large-scale systems, for example, safety systems, security service systems, and network systems. Inevitably, associated with this are risks, such as system vulnerabilities and security threats. The probability of those risks increases in unsecured environments, such as wireless networks in general and in Wireless Sensor Networks (WSNs) in particular. WSN is a self-organizing network of sensor nodes connected by wireless links. WSNs consist of hundreds to thousands of low-power, low-cost, multi-function sensor nodes that are small in size and communicate over short-ranges. The distribution of sensor nodes in an open environment that could be unattended in addition to the resource constraints in terms of processing, storage and power, make such networks in stringent limitations such as lifetime (i.e. period of operation) and security. The importance of WSN applications that could be found in many militaries and civilian aspects has drawn the attention of many researchers to consider its security. To address this important issue and overcome one of the main challenges of WSNs, security solution systems have been developed by researchers. Those solutions are software-based network Intrusion Detection Systems (IDSs). However, it has been witnessed, that those developed IDSs are neither secure enough nor accurate to detect all malicious behaviours of attacks. Thus, the problem is the lack of coverage of all malicious behaviours in proposed IDSs, leading to unpleasant results, such as delays in the detection process, low detection accuracy, or even worse, leading to detection failure, as illustrated in the previous studies. Also, another problem is energy consumption in WSNs caused by IDS. So, in other words, not all requirements are implemented then traced. Moreover, neither all requirements are identified nor satisfied, as for some requirements have been compromised. The drawbacks in the current IDS are due to not following structured software development processes by researches and developers when developing IDS. Consequently, they resulted in inadequate requirement management, process, validation, and verification of requirements quality. Unfortunately, WSN and SE research communities have been mostly impermeable to each other. Integrating SE and WSNs is a real subject that will be expanded as technology evolves and spreads in industrial applications. Therefore, this paper will study the importance of Requirement Engineering when developing IDSs. Also, it will study a set of existed IDSs and illustrate the absence of Requirement Engineering and its effect. Then conclusions are drawn in regard of applying requirement engineering to systems to deliver the required functionalities, with respect to operational constraints, within an acceptable level of performance, accuracy and reliability.Keywords: software engineering, requirement engineering, Intrusion Detection System, IDS, Wireless Sensor Networks, WSN
Procedia PDF Downloads 3182077 Adversarial Attacks and Defenses on Deep Neural Networks
Authors: Jonathan Sohn
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Deep neural networks (DNNs) have shown state-of-the-art performance for many applications, including computer vision, natural language processing, and speech recognition. Recently, adversarial attacks have been studied in the context of deep neural networks, which aim to alter the results of deep neural networks by modifying the inputs slightly. For example, an adversarial attack on a DNN used for object detection can cause the DNN to miss certain objects. As a result, the reliability of DNNs is undermined by their lack of robustness against adversarial attacks, raising concerns about their use in safety-critical applications such as autonomous driving. In this paper, we focus on studying the adversarial attacks and defenses on DNNs for image classification. There are two types of adversarial attacks studied which are fast gradient sign method (FGSM) attack and projected gradient descent (PGD) attack. A DNN forms decision boundaries that separate the input images into different categories. The adversarial attack slightly alters the image to move over the decision boundary, causing the DNN to misclassify the image. FGSM attack obtains the gradient with respect to the image and updates the image once based on the gradients to cross the decision boundary. PGD attack, instead of taking one big step, repeatedly modifies the input image with multiple small steps. There is also another type of attack called the target attack. This adversarial attack is designed to make the machine classify an image to a class chosen by the attacker. We can defend against adversarial attacks by incorporating adversarial examples in training. Specifically, instead of training the neural network with clean examples, we can explicitly let the neural network learn from the adversarial examples. In our experiments, the digit recognition accuracy on the MNIST dataset drops from 97.81% to 39.50% and 34.01% when the DNN is attacked by FGSM and PGD attacks, respectively. If we utilize FGSM training as a defense method, the classification accuracy greatly improves from 39.50% to 92.31% for FGSM attacks and from 34.01% to 75.63% for PGD attacks. To further improve the classification accuracy under adversarial attacks, we can also use a stronger PGD training method. PGD training improves the accuracy by 2.7% under FGSM attacks and 18.4% under PGD attacks over FGSM training. It is worth mentioning that both FGSM and PGD training do not affect the accuracy of clean images. In summary, we find that PGD attacks can greatly degrade the performance of DNNs, and PGD training is a very effective way to defend against such attacks. PGD attacks and defence are overall significantly more effective than FGSM methods.Keywords: deep neural network, adversarial attack, adversarial defense, adversarial machine learning
Procedia PDF Downloads 1892076 Analyze of Nanoscale Materials and Devices for Future Communication and Telecom Networks in the Gas Refinery
Authors: Mohamad Bagher Heidari, Hefzollah Mohammadian
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New discoveries in materials on the nanometer-length scale are expected to play an important role in addressing ongoing and future challenges in the field of communication. Devices and systems for ultra-high speed short and long range communication links, portable and power efficient computing devices, high-density memory and logics, ultra-fast interconnects, and autonomous and robust energy scavenging devices for accessing ambient intelligence and needed information will critically depend on the success of next-generation emerging nonmaterials and devices. This article presents some exciting recent developments in nonmaterials that have the potential to play a critical role in the development and transformation of future intelligent communication and telecom networks in the gas refinery. The industry is benefiting from nanotechnology advances with numerous applications including those in smarter sensors, logic elements, computer chips, memory storage devices, optoelectronics.Keywords: nonmaterial, intelligent communication, nanoscale, nanophotonic, telecom
Procedia PDF Downloads 3262075 In situ Polymerization and Properties of Biobased Polyurethane/Epoxy Interpenetrating Network Nanocomposites
Authors: Aiswarea Mathew, Smita Mohanty, Jr., S. K. Nayak
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Polyurethane networks based on castor oil (CO) as a renewable resource polyol were synthesized. Polyurethane/epoxy resin interpenetrating network nanocomposites containing modified montmorillonite organoclay (C30B-PU/EP nanocomposites) were prepared by an in situ intercalation method. The conventional spectroscopic characterization of the synthesized samples using FT-IR confirms the existence of the proposed castor oil based PU structure and also showed that strong interactions existed between C30B and EP/PU matrix. The dispersion degree of C30B in EP/PU matrix was characterized by X-Ray diffraction (XRD) method. Scanning electronic microscopy analysis showed that the interpenetrating process of PU and EP increases the exfoliation degree of C30B, and it improves the compatibility and the phase structure of polyurethane/epoxy resin interpenetrating polymer networks (PU/EP IPNs). The thermal stability improves compared to the polyurethane when the PU/EP IPN is formed. Mechanical properties including the Young’s modulus and tensile strength reflected marked improvement with addition of C30B.Keywords: castor oil, epoxy, montmorillonite, polyurethane
Procedia PDF Downloads 3952074 Frequency Distribution and Assertive Object Theory: An Exploration of the Late Bronze Age Italian Ceramic Landscape
Authors: Sara Fioretti
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In the 2nd millennium BCE, maritime networks became essential to the Mediterranean lifestyle, creating an interconnected world. This phenomenon of interconnected cultures has often been misinterpreted as an “effect” of the Mycenaean “influence” without considering the complexity and role of regional and cross-cultural exchanges. This paper explores the socio-economic relationships, in both cross-cultural and potentially inter-regional settings, present within the archaeological repertoire of the southern Italian Late Bronze Age (LBA 1600 -1140 BCE). The emergence of economic relations within the connectivity of the regional settlements is explored through ceramic contexts found in the case studies Punta di Zambrone, Broglio di Trebisacce, and Nuraghe Antigori. This work-in-progress research is situated in the shifting theoretical views of the last ten years that discuss the Late Bronze Age’s connectivity through Social Networks, Entanglement, and Assertive Objects combined with a comparative statistical study of ceramic frequency distribution. Applying these theoretical frameworks with a quantitative approach demonstrates the specific regional economic relationships that shaped the cultural interactions of the Late Bronze Age. Through this intersection of theory and statistical analysis, the case studies establish a small percentage of pottery as imported, whilst assertive productions have a relatively higher quantity. Overall, the majority still adheres to regional Italian traditions. Therefore, we can dissect the rhizomatic relationships cultivated by the Italian coasts and Mycenaeans and their roles within their networks through the intersection of theoretical and statistical analysis. This research offers a new perspective on the connectivity of the Late Bronze Age relational structures.Keywords: late bronze age, mediterranean archaeology, exchanges and trade, frequency distribution of ceramic assemblages
Procedia PDF Downloads 352073 Neural Network Approach For Clustering Host Community: Based on Perceptions Toward Tourism, Their Satisfaction Level and Demographic Attributes in Iran (Lahijan)
Authors: Nasibeh Mohammadpour, Ali Rajabzadeh, Adel Azar, Hamid Zargham Borujeni,
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Generally, various industries development depends on their stakeholders and beneficiaries supports. One of the most important stakeholders in tourism industry ( which has become one of the most important lucrative and employment-generating activities at the international level these days) are host communities in tourist destination which are affected and effect on this industry development. Recognizing host community and its segmentations can be important to get their support for future decisions and policy making. In order to identify these segments, in this study, clustering of the residents has been done by using some tools that are designed to encounter human complexities and have ability to model and generalize complex systems without any needs for the initial clusters’ seeds like classic methods. Neural networks can help to meet these expectations. The research have been planned to design neural networks-based mathematical model for clustering the host community effectively according to multi criteria, and identifies differences among segments. In order to achieve this goal, the residents’ segmentation has been done by demographic characteristics, their attitude towards the tourism development, the level of satisfaction and the type of their support in this field. The applied method is self-organized neural networks and the results have compared with K-means. As the results show, the use of Self- Organized Map (SOM) method provides much better results by considering the Cophenetic correlation and between clusters variance coefficients. Based on these criteria, the host community is divided into five sections with unique and distinctive features, which are in the best condition (in comparison other modes) according to Cophenetic correlation coefficient of 0.8769 and between clusters variance of 0.1412.Keywords: Artificial Nural Network, Clustering , Resident, SOM, Tourism
Procedia PDF Downloads 1752072 High-Capacity Image Steganography using Wavelet-based Fusion on Deep Convolutional Neural Networks
Authors: Amal Khalifa, Nicolas Vana Santos
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Steganography has been known for centuries as an efficient approach for covert communication. Due to its popularity and ease of access, image steganography has attracted researchers to find secure techniques for hiding information within an innocent looking cover image. In this research, we propose a novel deep-learning approach to digital image steganography. The proposed method, DeepWaveletFusion, uses convolutional neural networks (CNN) to hide a secret image into a cover image of the same size. Two CNNs are trained back-to-back to merge the Discrete Wavelet Transform (DWT) of both colored images and eventually be able to blindly extract the hidden image. Based on two different image similarity metrics, a weighted gain function is used to guide the learning process and maximize the quality of the retrieved secret image and yet maintaining acceptable imperceptibility. Experimental results verified the high recoverability of DeepWaveletFusion which outperformed similar deep-learning-based methods.Keywords: deep learning, steganography, image, discrete wavelet transform, fusion
Procedia PDF Downloads 782071 Design an Intelligent Fire Detection System Based on Neural Network and Particle Swarm Optimization
Authors: Majid Arvan, Peyman Beygi, Sina Rokhsati
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In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one.Keywords: intelligent fire detection, neural network, particle swarm optimization, fire sensor network
Procedia PDF Downloads 377