Search results for: co-regulatory networks

Authors: Fotis I. Giasemis, Alexandros Sopasakis

Abstract:

This study focuses on detecting traffic anomalies using generative models applied to real-time observations. By integrating a Graph Neural Network with an attention-based mechanism within the Spatiotemporal Generative Adversarial Network framework, we enhance the capture of both spatial and temporal dependencies in traffic data. Leveraging minute-by-minute observations from cameras distributed across Gothenburg, our approach provides a more detailed and precise anomaly detection system, effectively capturing the complex topology and dynamics of urban traffic networks.

Keywords: traffic, anomaly detection, GNN, GAN

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Authors: M. Tsebia, H. Bentarzi

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In the inter-connected power systems, a phenomenon called inter-area oscillation may be caused by several defects. In this paper, a study of the Maghreb countries inter-area power networks oscillation has been investigated. The inter-area oscillation monitoring can be enhanced by integrating Phasor Measurement Unit (PMU) technology installed in different places. The data provided by PMU and recorded by PDC will be used for the monitoring, analysis, and control purposes. The proposed approach has been validated by simulation using MATLAB/Simulink.

Keywords: PMU, inter-area oscillation, Maghrebian power system, Simulink

Procedia PDF Downloads 362

Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

Procedia PDF Downloads 59

Authors: A. Assad, T. Zayed

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Water networks (WNs) are responsible of providing adequate amounts of safe, high quality, water to the public. As other critical infrastructure systems, WNs are subjected to deterioration which increases the number of breaks and leaks and lower water quality. In Canada, 35% of water assets require critical attention and there is a significant gap between the needed and the implemented investments. Thus, the need for efficient rehabilitation programs is becoming more urgent given the paradigm of aging infrastructure and tight budget. The first step towards developing such programs is to formulate a Performance Index that reflects the current condition of water assets along with its criticality. While numerous studies in the literature have focused on various aspects of condition assessment and reliability, limited efforts have investigated the criticality of such components. Critical water mains are those whose failure cause significant economic, environmental or social impacts on a community. Inclusion of criticality in computing the performance index will serve as a prioritizing tool for the optimum allocating of the available resources and budget. In this study, several social, economic, and environmental factors that dictate the criticality of a water pipelines have been elicited from analyzing the literature. Expert opinions were sought to provide pairwise comparisons of the importance of such factors. Subsequently, Fuzzy Logic along with Analytical Network Process (ANP) was utilized to calculate the weights of several criteria factors. Multi Attribute Utility Theories (MAUT) was then employed to integrate the aforementioned weights with the attribute values of several pipelines in Montreal WN. The result is a criticality index, 0-1, that quantifies the severity of the consequence of failure of each pipeline. A novel contribution of this approach is that it accounts for both the interdependency between criteria factors as well as the inherited uncertainties in calculating the criticality. The practical value of the current study is represented by the automated tool, Excel-MATLAB, which can be used by the utility managers and decision makers in planning for future maintenance and rehabilitation activities where high-level efficiency in use of materials and time resources is required.

Keywords: water networks, criticality assessment, asset management, fuzzy analytical network process

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Authors: Amel Ltifi, Ahmed Zouinkhi, Mohamed Salim Bouhlel

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Security and trust management in Vehicular Ad-hoc NETworks (VANET) is a crucial research domain which is the scope of many researches and domains. Although, the majority of the proposed trust management systems for VANET are based on specific road infrastructure, which may not be present in all the roads. Therefore, road security should be managed by vehicles themselves. In this paper, we propose a new Self Organized Trust Management system (SOTM). This system has the responsibility to cut with the spread of false warnings in the network through four principal components: cooperation, trust management, communication and security.

Keywords: ative vehicle, cooperation, trust management, VANET

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Authors: Marcos Valdez Alexander Junior, Igor Aguilar-Alonso

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Malnutrition is the root of different diseases that universally affect everyone, diseases such as obesity and malnutrition. The objective of this research is to predict the calories of the food to be eaten, developing a smart mobile application to show the user if a meal is balanced. Due to the large percentage of obesity and malnutrition in Peru, the present work is carried out. The development of the intelligent application is proposed with a three-layer architecture, and for the prediction of the nutritional value of the food, the use of pre-trained models based on convolutional neural networks is proposed.

Keywords: volume estimation, calorie estimation, artificial vision, food nutrition

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Authors: Alberto Azevedo, Ricardo Martins, André B. Fortunato, Anabela Oliveira

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Water is under severe threat today because of the rising population, increased agricultural and industrial needs, and the intensifying effects of climate change. Due to sea-level rise, erosion, and demographic pressure, the coastal regions are of significant concern to the scientific community. The Water Monitoring Sentinel Cloud platform (WORSICA) service is focused on providing new tools for monitoring water in coastal and inland areas, taking advantage of remote sensing, in situ and tidal modeling data. WORSICA is a service that can be used to determine the coastline, coastal inundation areas, and the limits of inland water bodies using remote sensing (satellite and Unmanned Aerial Vehicles - UAVs) and in situ data (from field surveys). It applies to various purposes, from determining flooded areas (from rainfall, storms, hurricanes, or tsunamis) to detecting large water leaks in major water distribution networks. This service was built on components developed in national and European projects, integrated to provide a one-stop-shop service for remote sensing information, integrating data from the Copernicus satellite and drone/unmanned aerial vehicles, validated by existing online in-situ data. Since WORSICA is operational using the European Open Science Cloud (EOSC) computational infrastructures, the service can be accessed via a web browser and is freely available to all European public research groups without additional costs. In addition, the private sector will be able to use the service, but some usage costs may be applied, depending on the type of computational resources needed by each application/user. Although the service has three main sub-services i) coastline detection; ii) inland water detection; iii) water leak detection in irrigation networks, in the present study, an application of the service to Óbidos lagoon in Portugal is shown, where the user can monitor the evolution of the lagoon inlet and estimate the topography of the intertidal areas without any additional costs. The service has several distinct methodologies implemented based on the computations of the water indexes (e.g., NDWI, MNDWI, AWEI, and AWEIsh) retrieved from the satellite image processing. In conjunction with the tidal data obtained from the FES model, the system can estimate a coastline with the corresponding level or even topography of the inter-tidal areas based on the Flood2Topo methodology. The outcomes of the WORSICA service can be helpful for several intervention areas such as i) emergency by providing fast access to inundated areas to support emergency rescue operations; ii) support of management decisions on hydraulic infrastructures operation to minimize damage downstream; iii) climate change mitigation by minimizing water losses and reduce water mains operation costs; iv) early detection of water leakages in difficult-to-access water irrigation networks, promoting their fast repair.

Keywords: remote sensing, coastline detection, water detection, satellite data, sentinel, Copernicus, EOSC

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Authors: Ananya Gangavarapu

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Convolutional Neural Networks (CNN) based models are providing diagnostic capabilities on par with the medical specialists in many specialty areas. However, collecting the medical data for training purposes is very challenging because of the increased regulations around data collections and privacy concerns around personal health data. The gathering of the data becomes even more difficult if the capture devices are edge-based mobile devices (like smartphones) with feeble wireless connectivity in rural/remote areas. In this paper, I would like to highlight Federated Learning approach to mitigate data privacy and security issues.

Keywords: deep learning in healthcare, data privacy, federated learning, training in distributed environment

Procedia PDF Downloads 141

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The increasing use of deep learning applications in production is becoming a competitive advantage. Predictive quality enables the assurance of product quality by using data-driven forecasts via machine learning models as a basis for decisions on test results. The use of real Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the leakage of directional valves.

Keywords: artificial neural networks, classification, hydraulics, predictive quality, deep learning

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Authors: Ren-Jieh Kuo, Chun-Shou Huang

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The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance.

Keywords: flavor forecasting, artificial neural networks, Support Vector Regression, China

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Authors: Qiankun Zhong

Abstract:

In a global cultural context, film festival awards become authorities to define the aesthetic value of films. To study which genres and producing countries are valued by different film festivals and how those evaluations interact with each other, this research explored the interactions between the film festivals through their selection of movies and the factors that lead to the tendency of film festivals to nominate the same movies. To do this, the author employed a two-mode network on the movies that won the highest awards at five international film festivals with the highest attendance in the past ten years (the Venice Film Festival, the Cannes Film Festival, the Toronto International Film Festival, Sundance Film Festival, and the Berlin International Film Festival) and the film festivals that nominated those movies. The title, genre, producing country and language of 50 movies, and the range (regional, national or international) and organizing country or area of 129 film festivals were collected. These created networks connected by nominating the same films and awarding the same movies. The author then assessed the density and centrality of these networks to answer the question: What are the film festivals that tend to have more shared values with other festivals? Based on the Eigenvector centrality of the two-mode network, Palm Springs, Robert Festival, Toronto, Chicago, and San Sebastian are the festivals that tend to nominate commonly appreciated movies. In contrast, Black Movie Film Festival has the unique value of generally not sharing nominations with other film festivals. A homophily test was applied to access the clustering effects of film and film festivals. The result showed that movie genres (E-I index=0.55) and geographic location (E-I index=0.35) are possible indicators of film festival clustering. A blockmodel was also created to examine the structural roles of the film festivals and their meaning in real-world context. By analyzing the same blocks with film festival attributes, it was identified that film festivals either organized in the same area, with the same history, or with the same attitude on independent films would occupy the same structural roles in the network. Through the interpretation of the blocks, language was identified as an indicator that contributes to the role position of a film festival. Comparing the result of blockmodeling in the different periods, it is seen that international film festivals contrast with the Hollywood industry’s dominant value. The structural role dynamics provide evidence for a multi-value film festival network.

Keywords: film festivals, film studies, media industry studies, network analysis

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Authors: John Ayoade

Abstract:

Cloud computing is a model that enables the delivery of on-demand computing resources such as networks, servers, storage, applications and services over the internet. Cloud Computing is a relatively growing concept that presents a good number of benefits for its users; however, it also raises some security challenges which may slow down its use. In this paper, we identify some of those security issues that can serve as barriers to realizing the full benefits that cloud computing can bring. One of the key security problems is security trust. A security trust model is proposed that can enhance the confidence that users need to fully trust the use of public and mobile cloud computing and maximize the potential benefits that they offer.

Keywords: cloud computing, trust, security, certificate authority, PKI

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Authors: Danuvasin Charoen

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Online romance scams are conducted by criminals using social networks and dating sites. These criminals use love to deceive the victims to send them money. The victims not only lose money to the criminals, but they are also heartbroken. This study investigates how online romance scams work and why people become victims to them. The researcher also identifies the characteristics of the perpetrators and victims. The data were collected from in-depth interviews with former victims and police officers responsible for the cases. By studying the methods and characteristics of the online romance scam, we can develop effective methods and policies to reduce the rates of such crimes.

Keywords: romance scam, online scam, phishing, cybercrime

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Authors: Amin Hamrahi, Niloofar Moghaddam

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Denial of Service is for denial-of-service attack, a type of attack on a network that is designed to bring the network to its knees by flooding it with useless traffic. Denial of Service (DoS) attacks have become a major threat to current computer networks. Many recent DoS attacks were launched via a large number of distributed attacking hosts in the Internet. These attacks are called distributed denial of service (DDoS) attacks. To have a better understanding on DoS attacks, this article provides an overview on existing DoS and DDoS attacks and major defense technologies in the Internet.

Keywords: denial of service, distributed denial of service, traffic, flooding

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Authors: C. P. Autry, A.W. Roscoe

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Authentication protocols based on public key infrastructure (PKI) and trusted third party (TTP) are no longer adequate for industrial scale IoT networks thanks to issues such as low compute and power availability, the use of widely distributed and commercial off-the-shelf (COTS) systems, and the increasingly sophisticated attackers and attacks we now have to counter. For example, there is increasing concern about nation-state-based interference and future quantum computing capability. We have examined this space from first principles and have developed several approaches to group and point-to-point authentication for IoT that do not depend on the use of a centralised client-server model. We emphasise the use of quantum resistant primitives such as strong cryptographic hashing and the use multi-factor authentication.

Keywords: authentication, enterprise IoT cybersecurity, PKI/TTP, IoT space

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Authors: Ayanda Magida, Brian Armstrong

Abstract:

The study aimed to assess the third level of the digital divide among the millennials and post-millennials in South Africa. The millennials are people born from 1981-to 1996, that is, people between the ages of 25-40 years old and post-millennials are people born from 1997 to date. For the study, only post-millennials born between 1997-2003 were included as they were old enough to consent to participation in the study. Data was collected as part of the Ph.D. project that focuses on the relationship between income inequality, the digital divide, and social cohesion in South Africa. The digital divide has three main levels, namely the first, second and third. The first and second focus on access and usage, respectively. The third-level digital divide can be defined as the differences in the benefits associated with being online. The current paper focuses on the third level: the benefits derived by being online using four domains: economic, educational, social, and personal benefits. The economic benefits include income, employment and finance-related activities; the social benefits include socializing belonging, identity, and informal networks. The personal benefits include personal wellbeing and self-actualization. A total of 763 participants completed the survey, and 61.3% were post-millennials between the ages of 18-24 and s 38.6 % were millennials between 25 and 40. The majority of the respondents were female (62%), male (34%) and nonbinary (1%), respectively. Most of the respondents were black, followed by whites, Indians and colored, respectively. Thus, they represented the status of the demographics of the country. Most of the respondents had access to the internet and smartphone. Most expressed that they use laptops (68%) or mobile (71%) to access the internet and 54 % access the internet using wireless/Wi-Fi. There were no differences between the millennial and post-millennial economic and educational benefits of being online. However, the post-millennials were more inclined to use the internet for social and personal benefits than the millennials. This could be attributed to many factors, such as age. The post-millennials are still discovering themselves and therefore would derive social and personal benefits associated with being online. The findings confirm studies that argue that younger generations derive more benefits from being online than the older generation. Based on the findings, it is evident that the post-millennials are not using the internet or online activities for social networks and socializing but can derive economic benefits such as job looking and education benefits from being online. It can be inferred that there are no significant differences between the two groups, and it seems like the third-level digital divide is not evident among the two groups as they both have been able to derive meaningful benefits from being online. Further studies should focus on the third-level divide between the baby boomers and Generation X.

Keywords: third-level digital divide, millennials, post-millennials, online activities

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Authors: A. Bhandari, S. Mukherjee

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Two frames in a Hilbert space are called woven or weaving if all possible merge combinations between them generate frames of the Hilbert space with uniform frame bounds. Weaving frames are powerful tools in wireless sensor networks which require distributed data processing. Considering the practical applications, this article deals with finite woven frames. We provide methods of constructing finite woven frames, in particular, bounded linear operators are used to construct woven frames from a given frame. Several examples are discussed. We also introduce the notion of woven frame sequences and characterize them through the concepts of gaps and angles between spaces.

Keywords: frames, woven frames, gap, angle

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Authors: Beatriz Martinez-Pastor, Maria Nogal, Alan O'Connor

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During the last years, the number of extreme weather events has increased. A variety of extreme weather events, including river floods, rain-induced landslides, droughts, winter storms, wildfire, and hurricanes, have threatened and damaged many different regions worldwide. These events have a devastating impact on critical infrastructure systems resulting in high social, economical and environmental costs. These events have a huge impact in transport systems. Since, transport networks are completely exposed to every kind of climatological perturbations, and its performance is closely related with these events. When a traffic network is affected by a climatological hazard, the quality of its service is threatened, and the level of the traffic conditions usually decreases. With the aim of understanding this process, the concept of resilience has become most popular in the area of transport. Transport resilience analyses the behavior of a traffic network when a perturbation takes place. This holistic concept studies the complete process, from the beginning of the perturbation until the total recovery of the system, when the perturbation has finished. Many concepts are included in the definition of resilience, such as vulnerability, redundancy, adaptability, and safety. Once the resilience of a transport network can be evaluated, in this case, the methodology used is a dynamic equilibrium-restricted assignment model that allows the quantification of the concept, the next step is its improvement. Through the improvement of this concept, it will be possible to create transport networks that are able to withstand and have a better performance under the presence of climatological hazards. Analyzing the impact of a perturbation in a traffic network, it is observed that the response of the different links, which are part of the network, can be completely different from one to another. Consequently and due to this effect, many questions arise, as what makes a link more critical before an extreme weather event? or how is it possible to identify these critical links? With this aim, and knowing that most of the times the owners or managers of the transport systems have limited resources, the identification of the critical links of a transport network before extreme weather events, becomes a crucial objective. For that reason, using the available resources in the areas that will generate a higher improvement of the resilience, will contribute to the global development of the network. Therefore, this paper wants to analyze what kind of characteristic makes a link a critical one when an extreme weather event damages a transport network and finally identify them.

Keywords: critical links, extreme weather events, hazard, resilience, transport network

Procedia PDF Downloads 286

Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

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Authors: Nesrine Toubaline, Djamel Bennouar, Ali Mahdoum

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3D technology can lead to a significant reduction in power and average hop-count in Networks on Chip (NoCs). It offers short and fast vertical links which copes with the long wire problem in 2D NoCs. This work proposes heuristic-based method to optimize number and placement of vertical links to achieve specified performance goals. Experiments show that significant improvement can be achieved by using a specific number of vertical interconnect.

Keywords: interconnect optimization, monolithic inter-tier vias, network on chip, system on chip, through silicon vias, three dimensional integration circuits

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Authors: Razwan Ahmed Tanvir, Greg Speegle

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Collaborative blockchain ecosystems allow diverse groups to cooperate on tasks while providing properties such as decentralization and transaction security. We provide a model that uses a repository blockchain to manage hard forks within a collaborative system such that a single process (assuming that it has knowledge of the requirements of each fork) can access all of the blocks within the system. The repository blockchain replaces the need for Inter Blockchain Communication (IBC) within the ecosystem by navigating the networks. The resulting construction resembles a tree instead of a chain. A proof-of-concept implementation performs a depth-first search on the new structure.

Keywords: hard fork, shared governance, inter blockchain communication, blockchain ecosystem, regular research paper

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Authors: Oktay Nusratov, Ramin Rzaev, Aydin Goyushov

Abstract:

Fuzzy inference method based approach to the forming of modular intellectual system of assessment the quality of communication services is proposed. Developed under this approach the basic fuzzy estimation model takes into account the recommendations of the International Telecommunication Union in respect of the operation of packet switching networks based on IP-protocol. To implement the main features and functions of the fuzzy control system of quality telecommunication services it is used multilayer feedforward neural network.

Keywords: quality of communication, IP-telephony, fuzzy set, fuzzy implication, neural network

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Authors: H. S. Kang

Abstract:

Coupled electrical and optical model for conversion of electrical energy into coherent optical energy for transmitter-receiver link by solid state device is presented. Probability distribution for travelling laser beam switching time intervals and the number of switchings in the time interval is obtained. Selector function mapping is employed to regulate optical data transmission speed. It is established that regulated laser transmission from PhotoActive Laser transmitter follows principal of invariance. This considerably simplifies design of PhotoActive Laser Transmission networks.

Keywords: computational mathematics, finite difference Markov chain methods, sequence spaces, singularly perturbed differential equations

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Authors: Cristina Galván-Fernández

Abstract:

Students differentiate the different contexts of their lives as well as employment, hobbies or studies. In higher education is needed to transfer the experiential knowledge to theory and viceversa. However, is difficult to achieve than students use their personal experiences and social readings for get the learning evidences. In an experience with 178 education students from Chile and Spain we have used an e-portfolio system and a methodology for 4 years with the aims of help them to: 1) self-regulate their learning process and 2) use social networks and professional experiences for make the learning evidences. These two objectives have been controlled by interviews to the same students in different moments and two questionnaires. The results of this study show that students recognize the ownership of their learning and progress in planning and reflection of their own learning.

Keywords: competences, e-portfolio, higher education, self-regulation

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Authors: Manoj Kumar

Abstract:

Routing protocols have a central role in any mobile ad hoc network (MANET). There are many routing protocols that exhibit different performance levels in different scenarios. In this paper, we compare AODV, DSDV, DSR, and ZRP routing protocol in mobile ad hoc networks to determine the best operational conditions for each protocol. We analyze these routing protocols by extensive simulations in OPNET simulator and show how to pause time and the number of nodes affect their performance. In this study, performance is measured in terms of control traffic received, control traffic sent, data traffic received, sent data traffic, throughput, retransmission attempts.

Keywords: AODV, DSDV, DSR, ZRP

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Authors: Nelvin Joseph, K. Krishna Milan Rao, Praveen Dwarakanath

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The explosive growth of Smart phones around the world has led to the shift of the primary engagement tool for entertainment from traditional consoles and music players to an all integrated device. Augmented Reality is the next big shift in bringing in a new dimension to the play. The paper explores the construct and working of the community engine in Delta T – an Augmented Reality game that allows users to evolve rules in the game basis collective bargaining mirroring democracy even in a gaming world.

Keywords: augmented reality, artificial neural networks, mobile application, human computer interaction, community engine

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Authors: Ali Kazemi

Abstract:

Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Seema Sant

Abstract:

In the 21st century were four generation workforces are working, it’s crucial for organizations to build talent management strategy, as tech-savvy Gen Y has entered the work force. They are more connected to each other than ever – through the internet enabled Social media networks Social media has become important in today’s world. The users of such Social media sites have increased in multiple. From sharing their opinion for a brand/product to researching a company before going for an interview, making a conception about a company’s culture or following a Company’s updates due to sheer interest or for job vacancy, Work force today is constantly in touch with social networks. Thus corporate world has rightly realized its potential uses for business purpose. Companies now use social media for marketing, advertising, consumer survey, etc. For HR professionals, it is used for networking and connecting to the Talent pool- through Talent Community. Social recruiting is the process of sourcing or hiring candidates through the use of social sites such as LinkedIn, Facebook Twitter which provide them with an array of information about potential employee; this study represents an exploratory investigation on the role of social networking sites in recruitment. The primarily aim is to analyze the factors that can enhance the channel of recruitment used by of the recruiter with specific reference to the IT organizations in Mumbai, India. Particularly, the aim is to identify how and why companies use social media to attract and screen applicants during their recruitment processes. It also examines the advantages and limitations of recruitment through social media for employers. This is done by literature review. Further, the papers examine the recruiter impact and understand the various opportunities which have created due to technology, thus, to analyze and examine these factors, both primary, as well as secondary data, are collected for the study. The primary data are gathered from five HR manager working in five top IT organizations in Mumbai and 100 HR consultants’ i.e., recruiter. The data was collected by conducting a survey and supplying a closed-ended questionnaire. A comprehension analysis of the study is depicted through graphs and figures. From the analysis, it was observed that there exists a positive relationship between the level of employee recruited through social media and their organizational commitment. Finally the findings show that company’s i.e. recruiters are currently using social media in recruitment, but perhaps not as effective as they could be. The paper gives recommendations and conditions for success that can help employers to make the most out of social media in recruitment.

Keywords: recruitment, social media, social sites, workforce

Procedia PDF Downloads 179

Authors: Mitat Uysal, Aynur Uysal

Abstract:

Artificial Intelligence (AI) is fundamentally driven by mathematics, with many of its core algorithms rooted in mathematical principles such as linear algebra, probability theory, calculus, and optimization techniques. This paper explores the deep connection between AI and mathematics, highlighting the role of mathematical concepts in key AI techniques like machine learning, neural networks, and optimization. To demonstrate this connection, a case study involving the implementation of a neural network using Python is presented. This practical example illustrates the essential role that mathematics plays in training a model and solving real-world problems.

Keywords: AI, mathematics, machine learning, optimization techniques, image processing

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Authors: Fariborz Ahmadi, Hamid Salehi, Khosrow Karimi

Abstract:

A sensor network is set of sensor nodes that cooperate together to perform a predefined tasks. The important problem in this network is power consumption. So, in this paper one algorithm based on the eurygaster life is introduced to minimize power consumption by the nodes of these networks. In this method the search space of problem is divided into several partitions and each partition is investigated separately. The evaluation results show that our approach is more efficient in comparison to other evolutionary algorithm like genetic algorithm.

Keywords: evolutionary computation, genetic algorithm, particle swarm optimization, sensor network optimization

Procedia PDF Downloads 428