Search results for: multi-layer perception neural networks

Authors: Greeshma Sharma, Sushil Chandra, Vijander Singh, Alok Prakash Mittal

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Navigational ability requires spatial representation, planning, and memory. It covers three interdependent domains, i.e. cognitive and perceptual factors, neural information processing, and variability in brain microstructure. Many attempts have been made to see the role of spatial representation in the navigational ability, and the individual differences have been identified in the neural substrate. But, there is also a need to address the influence of planning, memory on navigational ability. The present study aims to evaluate relations of aforementioned factors in the navigational ability. Total 30 participants volunteered in the study of a virtual shopping complex and subsequently were classified into good and bad navigators based on their performances. The result showed that planning ability was the most correlated factor for the navigational ability and also the discriminating factor between the good and bad navigators. There was also found the correlations between spatial memory recall and navigational ability. However, non-verbal episodic memory and spatial memory recall were also found to be correlated with the learning variable. This study attempts to identify differences between people with more and less navigational ability on the basis of planning and memory.

Keywords: memory, planning navigational ability, virtual reality

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Authors: M.Rüştü Karaman, İsmail İşeri, Kadir Saltalı, A.Reşit Brohi, Ayhan Horuz, Mümin Dizman

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Considerable relations has recently been given to the environmental hazardous caused by agricultural chemicals such as excess fertilizers. In this study, a neural network approach was investigated in the prediction of potential nitrate pollution across different land-use patterns by using a feedforward multilayered computer model of artificial neural network (ANN) with proper training. Periodical concentrations of some anions, especially nitrate (NO3-), and cations were also detected in drainage waters collected from the drain pipes placed in irrigated tomato field, unirrigated wheat field, fallow and pasture lands. The soil samples were collected from the irrigated tomato field and unirrigated wheat field on a grid system with 20 m x 20 m intervals. Site specific nitrate concentrations in the soil samples were measured for ANN based simulation of nitrate leaching potential from the land profiles. In the application of ANN model, a multi layered feedforward was evaluated, and data sets regarding with training, validation and testing containing the measured soil nitrate values were estimated based on spatial variability. As a result of the testing values, while the optimal structures of 2-15-1 was obtained (R2= 0.96, P < 0.01) for unirrigated field, the optimal structures of 2-10-1 was obtained (R2= 0.96, P < 0.01) for irrigated field. The results showed that the ANN model could be successfully used in prediction of the potential leaching levels of nitrate, based on different land use patterns. However, for the most suitable results, the model should be calibrated by training according to different NN structures depending on site specific soil parameters and varied agricultural managements.

Keywords: artificial intelligence, ANN, drainage water, nitrate pollution

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Authors: Sina Saadati

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Multiprocessors Systems-On-Chips (MPSOCs) are used widely on modern computers to execute sophisticated software and applications. These systems include different processors for distinct aims. Most of the proposed task schedulers attempt to improve energy consumption. In some schedulers, the processor's temperature is considered to increase the system's reliability and performance. In this research, we have proposed a new method for thermal-aware task scheduling which is based on an artificial neural network (ANN). This method enables us to consider a variety of factors in the scheduling process. Some factors like ambient temperature, season (which is important for some embedded systems), speed of the processor, computing type of tasks and have a complex relationship with the final temperature of the system. This Issue can be solved using a machine learning algorithm. Another point is that our solution makes the system intelligent So that It can be adaptive. We have also shown that the computational complexity of the proposed method is cheap. As a consequence, It is also suitable for battery-powered systems.

Keywords: task scheduling, MOSOC, artificial neural network, machine learning, architecture of computers, artificial intelligence

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Authors: Salah Kayed Kayed, Jessica Hong Yang

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This study investigates, whether earnings management in the audited financial statements is associated with tone management in the narrative sections of the annual report in the UK. Earnings management and narrative disclosure are communication strategies used from managers to communicate with investors or other users. Because earnings management and narrative disclosure stem from managers, they can exploit this by doing manipulation in their earnings, and simultaneously disclosing qualitative text (narrative information) in their reports as a tone of words, which will affect users’ perception, and hence users will be misinformed. The association between earnings and tone management can be explained by the self-serving, through cognitive reference points, theory. The sample period lasts from 2010 to 2015, and the sample comprises all non-financial firms that consider under FTSE 350 in any year during the sample period. A list of words from previous research is used to measure the tone in the narrative sections of the annual report. Because this study focuses on the managerial strategic choice and the subjective issues that come from management, it uses the abnormal tone to capture the managerial discretion on tone, and a number of different discretionary accruals proxies to measure earnings management, where accruals management is considered as a manipulation tool from managers to change the users' perception. This research is motivated to fulfil the literature gap by examining the association between earnings and tone management. Moreover, if firms that apply earnings management use tone management to mislead investors, it is beneficial for investors, policy makers, standard setters, or other users to know whether there is an association between earnings management and tone management. Clearly, we believe that this study is fundamental in the accounting context, where it evaluates the communication strategies that are used in firms' financial reports. Consistent with prior research, it is expected that tone management is positively associated with earnings management. This means that firms that use earnings management have incentives to manipulate in their narrative disclosure through tone of words, to reflect a good perception for users, which will conceal the earnings management techniques used in their reporting.

Keywords: earnings management, FTSE 350, narrative disclosure, tone management

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Authors: Dimitar Karastoyanov, Todor Penchev

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The paper presents the research of a kind of autonomous mobile robots, intended for work and adaptive perception in unknown and unstructured environment. The objective are robots, dedicated for multi-sensory environment perception and exploration, like measurements and samples taking, discovering and putting a mark on the objects as well as environment interactions–transportation, carrying in and out of equipment and objects. At that ground classification of the different types mobile robots in accordance with the way of locomotion (wheel- or chain-driven, walking, etc.), used drive mechanisms, kind of sensors, end effectors, area of application, etc. is made. Modular system for the mechanical construction of the mobile robots is proposed. Special PLC on the base of AtMega128 processor for robot control is developed. Electronic modules for the wireless communication on the base of Jennic processor as well as the specific software are developed. The methods, means and algorithms for adaptive environment behaviour and tasks realization are examined. The methods of group control of mobile robots and for suspicious objects detecting and handling are discussed too.

Keywords: mobile robots, wireless communications, environment investigations, group control, suspicious objects

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Authors: Anna Mucha, Ewa Wojtyna, Anita Pollak

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The environment in which an individual resides and observes may play a meaningful role in well-being and related constructs. Contact with nature may have a positive influence of natural environments on individuals, impacting mood and psychophysical sensations, such as pain relief. Conversely, urban settings, dominated by concrete elements, might lead to mood decline and heightened stress levels. Similarly, the situation may appear in the case of the perception of virtual environments. However, this is a topic that requires further exploration, especially in the context of relationships with pain. The aforementioned matters served as the basis for formulating and executing the outlined experimental research within the realm of environmental psychology, leveraging new technologies, notably virtual reality (VR), which is progressively gaining prominence in the domain of mental health. The primary objective was to investigate the impact of a simulated virtual environment, mirroring a natural setting abundant in greenery, on the perception of acute pain induced by thermal stimuli (high temperature) – encompassing intensity, unpleasantness, and pain tolerance. Comparative analyses were conducted between the virtual natural environment (intentionally constructed in the likeness of a therapeutic garden), virtual urban environment, and a control group devoid of virtual projections. Secondary objectives aimed to determine the mutual relationships among variables such as positive and negative emotions, preferences regarding virtual environments, sense of presence, and restorative experience in the context of the perception of presented virtual environments and induced thermal pain. The study encompassed 126 physically healthy Polish adults, distributing 42 individuals across each of the three comparative groups. Oculus Rift VR technology and the TSA-II neurosensory analyzer facilitated the experiment. Alongside demographic data, participants' subjective feelings concerning virtual reality and pain were evaluated using the Visual Analogue Scale (VAS), the original Restorative Experience in the Virtual World questionnaire (Doświadczenie Regeneracji w Wirtualnym Świecie), and an adapted Slater-Usoh-Steed (SUS) questionnaire. Results of statistical and psychometric analyses, such as Kruskal-Wallis tests, Wilcoxon tests, and contrast analyses, underscored the positive impact of the virtual natural environment on individual pain perception and mood. The virtual natural environment outperformed the virtual urban environment and the control group without virtual projection, particularly in subjective pain components like intensity and unpleasantness. Variables such as restorative experience, sense of presence and virtual environment preference also proved pivotal in pain perception and pain tolerance threshold alterations, contingent on specific conditions. This implies considerable application potential for virtual natural environments across diverse realms of psychology and related fields, among others as a supportive analgesic approach and a form of relaxation following psychotherapeutic sessions.

Keywords: environmental psychology, nature, acute pain, emotions, vitrual reality, virtual environments

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Authors: Babak Forouraghi

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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

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Authors: Bhavini Pandya

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This paper considers Hη: T2 → T2 the Perturbed Cerbelli-Giona map. That is a family of 2-dimensional nonlinear area-preserving transformations on the torus T2=[0,1]×[0,1]= ℝ2/ ℤ2. A single parameter η varies between 0 and 1, taking the transformation from a hyperbolic toral automorphism to the “Cerbelli-Giona” map, a system known to exhibit multifractal properties. Here we study the multifractal properties of the family of maps. We apply a box-counting method by defining a grid of boxes Bi(δ), where i is the index and δ is the size of the boxes, to quantify the distribution of stable and unstable manifolds of the map. When the parameter is in the range 0.51< η <0.58 and 0.68< η <1 the map is ergodic; i.e., the unstable and stable manifolds eventually cover the whole torus, although not in a uniform distribution. For accurate numerical results we require correspondingly accurate construction of the stable and unstable manifolds. Here we use the piecewise linearity of the map to achieve this, by computing the endpoints of line segments which define the global stable and unstable manifolds. This allows the generalized fractal dimension Dq, and spectrum of dimensions f(α), to be computed with accuracy. Finally, the intersection of the unstable and stable manifold of the map will be investigated, and compared with the distribution of periodic points of the system.

Keywords: feed forward, gradient descent, neural network, integral equation

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Authors: Silpita Paul, Susanta Sadhukhan, Biswanath Maity, Madhusudan Das

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Neural tube defects (NTDs) are one of the most common forms of birth defect and affect ~300,000 new born worldwide each year. The prevalence is higher in Northern India (11 per 1000 birth) compare to southern India (5 per 1000 birth). NTDs are one of the common birth defects related with low blood folate and Hcy concentration. Though the mechanism is still unknown, but it is now established that, NTDs in human are polygenic in nature and follow the heterogeneous trait. In spite of its heterogeneity, polymorphism in few genes affects significantly the trait of NTDs. Polymorphisms in the genes FOLH1 and MTHFR plays important role in NTDs. In this study, the polymorphisms of these genes were screened by bi-directional sequencing from 30 mothers with NTD babies as case. The result revealed that 26.67% patients had bi-allelic FOLH1 polymorphism. The polymorphism has been identified as p.Y60H and frequent to cause NTDs. The study of MTHFR gene showed 2 different SNPs rs1801131 (at exon 4) and rs1801131 (at exon 7). The study showed 6.67% patients of both mono- and bi-allelic MTHFR-rs1801131 polymorphism and 6.67% patients of bi-allelic MTHFR-rs1801131 polymorphism. These polymorphisms has been responsible for p.A222V and p.E429A change respectively and frequently involved in NTD formation. Those polymorphisms affect mainly the absorption of dietary folate from intestine and the formation of 5-methylenetetrahydrofolate (5 MTHF) from 5,10-methylenetetrahydrofolate (5,10- MTHF), which is the functional folate form in our system. Though the study is not complete yet, but these polymorphisms play crucial roles in the formation of NTDs in other world population. Based on the result till date, it can be concluded that they also play significant role in our population too as in control samples we have not found any changes.

Keywords: neural tube defects, polymorphism, FOLH1, MTHFR

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Authors: Madhushi Bandara, Dharshana Kasthurirathna, Danaja Maldeniya, Mahendra Piraveenan

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Community detection is an extremely useful technique in understanding the structure and function of a social network. Louvain algorithm, which is based on Newman-Girman modularity optimization technique, is extensively used as a computationally efficient method extract the communities in social networks. It has been suggested that the nodes that are in close geographical proximity have a higher tendency of forming communities. Variants of the Newman-Girman modularity measure such as dist-modularity try to normalize the effect of geographical proximity to extract geographically dispersed communities, at the expense of losing the information about the geographically proximate communities. In this work, we propose a method to extract geographically dispersed communities while preserving the information about the geographically proximate communities, by analyzing the ‘community network’, where the centroids of communities would be considered as network nodes. We suggest that the inter-community link strengths, which are normalized over the community sizes, may be used to identify and extract the ‘overlay communities’. The overlay communities would have relatively higher link strengths, despite being relatively apart in their spatial distribution. We apply this method to the Gowalla online social network, which contains the geographical signatures of its users, and identify the overlay communities within it.

Keywords: social networks, community detection, modularity optimization, geographically dispersed communities

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Authors: Pejman Daryabor, Kamal Mohammadi

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In the present work, the study of vibration characteristics of a functionally graded thick hollow cylinder is investigated. The cylinder natural frequencies are obtained using Galerkin finite element method. The functionally graded cylinder is assumed to be made from many subcylinders. Each subcylinder is considered as an isotropic layer. Material’s properties in each layer are constant and functionally graded properties result by exponential function of layer radius in multilayer cylinder. To validate the FE results code, plane strain model of functionally graded cylinder are also modeled in ABAQUS. Analytical results are validated for both models. Also, a good agreement is found between the present results and those reported in the literature.

Keywords: natural frequency, functionally graded material, finite element method, thick cylinder

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Authors: Vahid Asadzadeh, Amin Ataee

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Since the advent of social media, there has been a wave of optimism among researchers and civic activists about the influence of virtual networks on the democratization process, which has gradually waned. One of the lesser-known concerns is how to increase the possibility of hearing the voices of different minorities. According to the theory of media logic, the media, using their technological capabilities, act as a structure through which events and ideas are interpreted. Social media, through the use of the learning machine and the use of algorithms, has formed a kind of structure in which the voices of minorities and less popular topics are lost among the commotion of the trends. In fact, the recommended systems and algorithms used in social media are designed to help promote trends and make popular content more popular, and content that belongs to minorities is constantly marginalized. As social networks gradually play a more active role in politics, the possibility of freely participating in the reproduction and reinterpretation of structures in general and political structures in particular (as Laclau‎ and Mouffe had in mind‎) can be considered as criteria to democracy in action. The point is that the media logic of virtual networks is shaped by the rule and even the tyranny of the majority, and this logic does not make it possible to design a self-foundation and self-revolutionary model of democracy. In other words, today's social networks, though seemingly full of variety But they are governed by the logic of homogeneity, and they do not have the possibility of multiplicity as is the case in immanent radical democracies (influenced by Gilles Deleuze). However, with the emergence and increasing popularity of Clubhouse as a new social media, there seems to be a shift in the social media space, and that is the diminishing role of algorithms and systems reconditioners as content delivery interfaces. This has led to the fact that in the Clubhouse, the voices of minorities are better heard, and the diversity of political tendencies manifests itself better. The purpose of this article is to show, first, how social networks serve the elimination of minorities in general, and second, to argue that the media logic of social networks must adapt to new interpretations of democracy that give more space to minorities and human rights. Finally, this article will show how the Clubhouse serves the new interpretations of democracy at least in a minimal way. To achieve the mentioned goals, in this article by a descriptive-analytical method, first, the relation between media logic and postmodern democracy will be inquired. The political economy popularity in social media and its conflict with democracy will be discussed. Finally, it will be explored how the Clubhouse provides a new horizon for the concepts embodied in radical democracy, a horizon that more effectively serves the rights of minorities and human rights in general.

Keywords: algorithmic tyranny, Clubhouse, minority rights, radical democracy, social media

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Authors: Zdravko Eškinja, Lovre Miljanić, Ognjen Kuljača

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This paper is an overview of simulation tools used to model specific thermal dynamics that occurs while controlling double skin façade. Research has been conducted on simplified construction with single zone where one side is glazed. Heat flow and temperature responses are simulated in three different simulation tools: IDA-ICE, EnergyPlus and HAMBASE. The excitation of observed system, used in all simulations, was a temperature step of exterior environment. Air infiltration, insulation and other disturbances are excluded from this research. Although such isolated behaviour is not possible in reality, experiments are carried out to gain novel information about heat flow transients which are not observable under regular conditions. Results revealed new possibilities for adapting the parameters of the neural network regulator. Along numerical simulations, the same set-up has been also tested in a real-time experiment with a 1:18 scaled model and thermal chamber. The comparison analysis brings out interesting conclusion about simulation accuracy in this particular case.

Keywords: double skin façade, experimental tests, heat control, heat flow, simulated tests, simulation tools

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Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

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Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

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Authors: Mohsen Dourandish

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Iran has a huge pipeline network in every state of country which is the longest and vastest pipeline network after Russia and USA (360,000 Km high pressure pipelines and 250,000 Km distribution networks). Furthermore in recent years National Iranian Gas Company is planning to develop natural gas network to cover all cities and villages above 20 families, in a way that 97 percent of Iran population will be gas consumer by 2020. In this condition, network resiliency will be the first priority of NIGC and due to that several planning for increasing resiliency of gas network is under construction. The most important strategy of NIGC is converting tree form pattern network to loop gas networks and developing underground gas storage near main gas consuming centers. In this regard NIGC is planning for construction of over 3500 km high-pressure pipeline and also 10 TCM gas storage capacities in UGSs.

Keywords: Iranian gas network, peak shaving, resiliency, underground gas storage

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Authors: Mohanad Alhabo, Li Zhang, Naveed Nawaz

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Small cell deployment in 5G networks is a promising technology to enhance capacity and coverage. However, unplanned deployment may cause high interference levels and high number of unnecessary handovers, which in turn will result in an increase in the signalling overhead. To guarantee service continuity, minimize unnecessary handovers, and reduce signalling overhead in heterogeneous networks, it is essential to properly model the handover decision problem. In this paper, we model the handover decision according to Multiple Attribute Decision Making (MADM) method, specifically Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). In this paper, we propose a hybrid TOPSIS method to control the handover in heterogeneous network. The proposed method adopts a hybrid weighting, which is a combination of entropy and standard deviation. A hybrid weighting control parameter is introduced to balance the impact of the standard deviation and entropy weighting on the network selection process and the overall performance. Our proposed method shows better performance, in terms of the number of frequent handovers and the mean user throughput, compared to the existing methods.

Keywords: handover, HetNets, interference, MADM, small cells, TOPSIS, weight

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Authors: Diego Caballero Vélez

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In 1997 the Schengen Convention was incorporated in the mainstream of EU law by the Amsterdam Treaty. It came into effect in 1999 with the abolition of internal border controls in the EU, a milestone in the European integration project. In the meantime, due to the Yugoslav wars, nearly 700,000 asylum applications were filed in the European countries provoking a major refugee crisis. During this period, the opening of Eastern Europe fostered more cooperation and policy-making at the EU level in migration issues. Currently, a similar migratory crisis is taking place in Europe. The Syrian war has caused the most massive influx of immigrants in Europe since World War II. Nevertheless, the EU is adopting different migration policies from those implemented during the Yugoslav migration crisis. The current crisis has not led to a common European position but national responses have been offered on migration policies and responsibility for border security and asylum-seekers. A lot of factors can explain this change from a cooperation scenario to a no cooperation one, such as the economic crisis, but this research is focused on the premise that 'threat perception' lies at the core of some states grand strategies towards migration and it also influences in multilateral or unilateral responses. Migration rests at the nexus of three dimensions of security, including geopolitical interests, material production, and internal security. According to some scholars, migration policy is an 'integral instrument' of state grand strategy in that context. Political integration at the EU might be altered with the emergence of existential threats. In other words, some areas of the European cooperation can be transformed when a 'critical juncture' occurs, for instance a migration crisis. In that instance, Member states could see migration as a matter of threat that modifies their national interests and willingness to embrace international cooperation. This research will focus on EU Member states´ perceptions of the 90´s migration crisis and the current one. The goal is to evaluate to what extent the perceptions of threat are one of the main factors for explaining the transition from a cooperation scenario to a no-cooperation one in European asylum and security policies. To analyze threat perception in both migration crisis, some relevant Member states are treated as cases of study and a comparative analysis is carried out based on public opinion polls, public and policy discourse in migration, voting practices and deconstruction of the migration policies themselves both at EU level and a national one.

Keywords: cooperation, migration crisis, national responses, threat perception

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Authors: Saima Yaqoob

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Due to advancement in technologies and cutting cost competition, especially in manufacturing business, organizations are now becoming more focused toward achieving exceptional quality standards with low manufacturing cost. In this concern, many process improvement strategies are becoming popular in the way of increasing productivity and output. Lean manufacturing principles are among one of them, increasingly used for improving productivity by reducing wastages. Many success factors are involved in lean implementation. But, the role of senior management is most important in developing the lean culture in an organization. Purpose of this study is to investigate the perception of executive level management related to the successful implementation of lean practices and its comparison with the existing practices in the organization. In order to collect data, survey questionnaire comprised of eight statements rendering the critical success factors were sent to the top management of fifty (50) automotive manufacturing companies of Karachi. After analyzing their feedbacks, the trend of lean manufacturing principles and the commitment of senior management toward its implementation was identified in the manufacturing industries of Karachi, Pakistan.

Keywords: lean manufacturing, process improvement, senior management, perception, involvement, waste reduction

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Shivam Kushwaha, Veena Bansal

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Timeshare refers to real estate that is owned simultaneously by many, for a specified time in a year, for a specified numbers of years and is maintained and managed by an agency. Timeshare falls under the umbrella of tourism and is often used for vacation. Timeshare industry has attracted significantly less number of customers in India as compared to the US and Europe. In more than 40 years of existence of timeshare industry, it has not been able to grow its roots among Indian customers. The purpose of the study: To explore perception of Indian customers towards the adoption of timeshare segment of the hospitality industry and identify the factors. Source of data: Survey has been done on existing owners of holidays memberships, resorts or those who at least tourism experience in their past purchases. Methodology: Logistic Regression is used to predict binary responses of the customers based on identified critical factors which might influence timeshare purchases. Result: The study identified four factors: discretionary income, exchange options, ownership pride, risk, and measured their influence on intention to purchases in India. It is recognized that is all four variables are statistically significant while explaining in purchase intentions of customers in India.

Keywords: timeshare, holiday, tourism, customer perception, intent to use, Indian tourism

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Authors: B. Lokuketagoda, M. Weerakoon, U. Madushan, A. N. Senaratne, K. Y. Abeywardena

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Ransomware has become a common threat in past few years and the recent threat reports show an increase of growth in Ransomware infections. Researchers have identified different variants of Ransomware families since 2015. Lack of knowledge of the user about the threat is a major concern. Ransomware detection methodologies are still growing through the industry. Email is the easiest method to send Ransomware to its victims. Uninformed users tend to click on links and attachments without much consideration assuming the emails are genuine. As a solution to this in this paper R-Killer Ransomware detection tool is introduced. Tool can be integrated with existing email services. The core detection Engine (CDE) discussed in the paper focuses on separating suspicious samples from emails and handling them until a decision is made regarding the suspicious mail. It has the capability of preventing execution of identified ransomware processes. On the other hand, Sandboxing and URL analyzing system has the capability of communication with public threat intelligence services to gather known threat intelligence. The R-Killer has its own mechanism developed in its Proactive Monitoring System (PMS) which can monitor the processes created by downloaded email attachments and identify potential Ransomware activities. R-killer is capable of gathering threat intelligence without exposing the user’s data to public threat intelligence services, hence protecting the confidentiality of user data.

Keywords: ransomware, deep learning, recurrent neural networks, email, core detection engine

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Authors: Valerii Dudin

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With the continuous increase in quantity and importance of the information surrounding our daily lives, it has become crucial to understand what makes information stand out and affect our point of view, regardless of the accuracy of the facts involved. Alaa Al-Aswany’s numerous works have been an inspiration for millions of his readers in Egypt and all across the Arab World. While highly factual, the author’s political essays are both lexically and stylistically rich; they also implement descriptive allusions and proverbs to support the presented opinions. We have undertaken an effort to explore the impact on the individual perception through these political works of the author. In this study, we have overviewed previously made research on similar subjects and through contextual, intertextual, linguistic and corpus analyses we have come to realize the presence of suggestive themes in these works, capable of shaping the reader’s perception regarding a certain topic, specifically targeting the reader’s emotional bias. The findings presented in the study will reveal an overview of such examples of suggestive elements used in the author’s works, as well as various new insights on what can be considered suggestive in the context of modern Arabic printed press.

Keywords: Alaa al-Aswany, cognitive linguistics, political essays, suggestion

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Authors: Cherouk Amr Yassin

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This study aims to provide insight into consumer decision-making, which has become very complicated to understand and predict in the existing world of sustainable development. The deficiency of a good understanding of the tourist's perception and attitude toward sustainable development in the tourism industry may impede the ability of organizations to build a sustainable marketing orientation and may negatively influence predicted consumer response. Therefore, this research paper adds further insights into the attitude toward recommended eco-friendly hotel advertisements and their effect on the purchase intention of eco-friendly services. Structural equational modeling was completed to realize the effects of the variables under investigation. The findings revealed that consumer decision-making in choosing eco-friendly hotels is affected by the positive attitude toward sustainable development ads, influenced by informativeness and credibility as values perceived by eco-friendly hotels. This study provides practical implications for tourism, marketers, hotel managers, promoters, and consumers.

Keywords: attitude, consumer behavior, consumer decision making, eco-friendly hotels, perception, the tourism industry

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Authors: Mohd Salmi Md Noorani, Ghada Al-Mahbashi, Sakhinah Abu Bakar

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This paper investigates projective lag synchronization (PLS) behavior in drive response dynamical networks (DRDNs) model with identical nodes. A hybrid feedback control method is designed to achieve the PLS with mismatch and without mismatch terms. The stability of the error dynamics is proven theoretically using the Lyapunov stability theory. Finally, analytical results show that the states of the dynamical network with non-delayed coupling can be asymptotically synchronized onto a desired scaling factor under the designed controller. Moreover, the numerical simulations results demonstrate the validity of the proposed method.

Keywords: drive-response dynamical network, projective lag synchronization, hybrid feedback control, stability theory

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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: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

Abstract:

In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

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Authors: T. Sattarpour, D. Nazarpour

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This paper investigates the effect of simultaneous placement of DGs and smart meters (SMs), on voltage profile improvement in active distribution networks (ADNs). A substantial center of attention has recently been on responsive loads initiated in power system problem studies such as distributed generations (DGs). Existence of responsive loads in active distribution networks (ADNs) would have undeniable effect on sizing and siting of DGs. For this reason, an optimal framework is proposed for sizing and siting of DGs and SMs in ADNs. SMs are taken into consideration for the sake of successful implementing of demand response programs (DRPs) such as direct load control (DLC) with end-side consumers. Looking for voltage profile improvement, the optimization procedure is solved by genetic algorithm (GA) and tested on IEEE 33-bus distribution test system. Different scenarios with variations in the number of DG units, individual or simultaneous placing of DGs and SMs, and adaptive power factor (APF) mode for DGs to support reactive power have been established. The obtained results confirm the significant effect of DRPs and APF mode in determining the optimal size and site of DGs to be connected in ADN resulting to the improvement of voltage profile as well.

Keywords: active distribution network (ADN), distributed generations (DGs), smart meters (SMs), demand response programs (DRPs), adaptive power factor (APF)

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Authors: Clarisse Lawson-Guidigbe, Nicolas Louveton, Kahina Amokrane-Ferka, Jean-Marc Andre

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Trust in automated driving systems is receiving growing attention in the research community. Anthropomorphism has been identified by past research as a trust-building factor. In this paper, we consider three anthropomorphic interfaces integrating three versions of a virtual assistant. We attempt to measure the impact of each of these interfaces on trust in the automated driving system. An experiment following a between-subject design was conducted in a driving simulator (N = 36) to evaluate participants’ performance and experience in two handover situations (a simple one and a critical one). Perception of anthropomorphism and trust was measured using scales, while participants’ experience was measured during elicitation interviews. We found no significant difference between the three interfaces regarding the perception of anthropomorphism, trust levels, or experience. However, regarding participants’ performance, we found a significant difference between the three interfaces in the simple handover situations but not the critical one. Learnings from anthropomorphism and trust measurement scales are discussed and suggestions for further research are proposed.

Keywords: highly automated driving, trust, anthropomorphic design, mindful anthropomorphism, mindless anthropomorphism

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Authors: Shokoufeh Monjezi Kouchak

Abstract:

Cognitive radio network (CRN) is future network .Without this network wireless devices can’t work appropriately in the next decades. Today, wireless devices use static spectrum access methods and these methods don’t use spectrums optimum so we need use dynamic spectrum access methods to solve shortage spectrum challenge and CR is a great device for DSA but first of all its challenges should be solved .security is one of these challenges .In this paper we provided a survey about CR security. You can see this survey in tables 1 to 7 .After that we proposed a multi criteria authentication method in CRN. Our criteria in this method are: sensing results, following sending data rules, position of secondary users and no talk zone. Finally we compared our method with other authentication methods.

Keywords: authentication, cognitive radio, security, radio networks

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Authors: Funmilayo Juliana Afolabi, Joke Haafkens, Paul De Beer

Abstract:

Globally, there is a growing concern on reducing workplace accidents in the informal sector. However, there is a dearth of study on the perception of the informal workers on occupational risks they are exposed to. The way a worker perceives the workplace risk will influence his/her risk tolerance and risk behavior. The aim of this paper, therefore, is to have an in-depth understanding of the way the artisans perceive the risks at their workplace and how it influences their risk tolerance and risk behavior. This will help in designing meaningful intervention for the artisans and it will assist the policy makers in formulating a policy that will help them. Methods: Forty-three artisans were purposely selected for the study; data were generated through observation of the workplace and work practices of the artisans and in-depth interview from automobile artisans (Panel beater, Mechanic, Vulcanizer, and Painters) in Osun State, Nigeria. The transcriptions were coded and analyzed using MAXQDA software. Results: The perceived occupational risks among the study groups are a danger of being run over by oncoming vehicles while working by the roadside, a risk of vehicle falling on workers while working under the vehicle, cuts, and burns, fire explosion, falls from height and injuries from bursting of tires. The identified risk factors are carelessness of the workers, pressure from customers, inadequate tools, preternatural forces, God’s will and lack of apprentices that will assist them in the workplace. Furthermore, the study revealed that artisans engage in risky behavior like siphoning fuel with mouth because of perception that fuel is good for expelling worms and will make them free from any stomach upset. Conclusions: The study concluded that risky behaviors are influenced by culture, beliefs, and perception of the artisans. The study, therefore, suggested proper health and safety education for the artisans.

Keywords: automobile artisans, informal, occupational risks, Nigeria, sociological enquiry

Procedia PDF Downloads 173