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Authors: Benazeer Md. Shahzada, Verelst Jan, Van Grembergen Wim, Mannaert Herwig

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This article is an extension and a practical application approach of Wheeler-s NEBIC theory (Net Enabled Business Innovation Cycle). NEBIC theory is a new approach in IS research and can be used for dynamic environment related to new technology. Firms can follow the market changes rapidly with support of the IT resources. Flexible firms adapt their market strategies, and respond more quickly to customers changing behaviors. When every leading firm in an industry has access to the same IT resources, the way that these IT resources are managed will determine the competitive advantages or disadvantages of firm. From Dynamic Capabilities Perspective and from newly introduced NEBIC theory by Wheeler, we know that only IT resources cannot deliver customer value but good configuration of those resources can guarantee customer value by choosing the right emerging technology, grasping the economic opportunities through business innovation and growth. We found evidences in literature that SOA (Service Oriented Architecture) is a promising emerging technology which can deliver the desired economic opportunity through modularity, flexibility and loosecoupling. SOA can also help firms to connect in network which can open a new window of opportunity to collaborate in innovation and right kind of outsourcing

Keywords: Absorptive capacity, Dynamic Capability, Netenabled business innovation cycle, Service oriented architecture.

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Authors: Neeraj Nehra, R. B. Patel, V. K. Bhat

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Use of the Internet and the World-Wide-Web (WWW) has become widespread in recent years and mobile agent technology has proliferated at an equally rapid rate. In this scenario load balancing becomes important for P2P systems. Beside P2P systems can be highly heterogeneous, i.e., they may consists of peers that range from old desktops to powerful servers connected to internet through high-bandwidth lines. There are various loads balancing policies came into picture. Primitive one is Message Passing Interface (MPI). Its wide availability and portability make it an attractive choice; however the communication requirements are sometimes inefficient when implementing the primitives provided by MPI. In this scenario we use the concept of mobile agent because Mobile agent (MA) based approach have the merits of high flexibility, efficiency, low network traffic, less communication latency as well as highly asynchronous. In this study we present decentralized load balancing scheme using mobile agent technology in which when a node is overloaded, task migrates to less utilized nodes so as to share the workload. However, the decision of which nodes receive migrating task is made in real-time by defining certain load balancing policies. These policies are executed on PMADE (A Platform for Mobile Agent Distribution and Execution) in decentralized manner using JuxtaNet and various load balancing metrics are discussed.

Keywords: Mobile Agents, Agent host, Agent Submitter, PMADE.

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Authors: Jefferson Camacho Mejía, Jenny Paola Forero Pachón, Luis Carlos Gómez Flórez

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Action research is a qualitative research methodology, which leads the researcher to delve into the problems of a community in order to understand its needs in depth and finally, to propose actions that lead to a change of social paradigm. Although this methodology had its beginnings in the human sciences, it has attracted increasing interest and acceptance in the field of information systems research since the 1990s. The countless possibilities offered nowadays by the use of Information Technologies (IT) in the development of different socio-economic activities have meant a change of social paradigm and the emergence of the so-called information and knowledge society. According to this, governments, large corporations, small entrepreneurs and in general, organizations of all kinds are using IT to virtualize their processes, taking them from the physical environment to the digital environment. However, there is a potential risk for organizations related with exposing valuable information without an appropriate framework for protecting it. This paper shows progress in the development of a methodological design to manage the information security risks associated with the IT-based processes virtualization, by applying the principles of the action research methodology and it is the result of a systematic review of the scientific literature. This design consists of seven fundamental stages. These are distributed in the three stages described in the action research methodology: 1) Observe, 2) Analyze and 3) Take actions. Finally, this paper aims to offer an alternative tool to traditional information security management methodologies with a view to being applied specifically in the planning stage of IT-based process virtualization in order to foresee risks and to establish security controls before formulating IT solutions in any type of organization.

Keywords: Action research, information security, information technology, methodological design, process virtualization, risk management.

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Authors: Shrikrishan Yadav, Akhilesh Saini, Krishna Chandra Roy

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The electromagnetic spectrum is a natural resource and hence well-organized usage of the limited natural resources is the necessities for better communication. The present static frequency allocation schemes cannot accommodate demands of the rapidly increasing number of higher data rate services. Therefore, dynamic usage of the spectrum must be distinguished from the static usage to increase the availability of frequency spectrum. Cognitive radio is not a single piece of apparatus but it is a technology that can incorporate components spread across a network. It offers great promise for improving system efficiency, spectrum utilization, more effective applications, reduction in interference and reduced complexity of usage for users. Cognitive radio is aware of its environmental, internal state, and location, and autonomously adjusts its operations to achieve designed objectives. It first senses its spectral environment over a wide frequency band, and then adapts the parameters to maximize spectrum efficiency with high performance. This paper only focuses on the analysis of Bit-Error-Rate in cognitive radio by using Particle Swarm Optimization Algorithm. It is theoretically as well as practically analyzed and interpreted in the sense of advantages and drawbacks and how BER affects the efficiency and performance of the communication system.

Keywords: BER, Cognitive Radio, Environmental Parameters, PSO, Radio spectrum, Transmission Parameters

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Authors: Soofiyan Atar, Adil Shaikh, Sahil Rajpurkar, Pragnesh Bhalala, Aniket Desai, Irfan Siddavatam

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Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics.

Keywords: model-based reinforcement learning, gait stability, supervised learning, pneumatic quadruped

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Authors: Oluseye A. Adeleke, Mohd. F. M. Salleh

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The performance of any cooperative communication system depends largely on the selection of a proper partner. Another important factor to consider is an efficient allocation of resource like power by the source node to help it in forwarding information to the destination. In this paper, we look at the concepts of partner selection and resource (power) allocation for a distributed communication network. A type of non-cooperative game referred to as Trade-Off game is employed so as to jointly consider the utilities of the source and relay nodes, where in this case, the source is the node that requires help with forwarding of its information while the partner is the node that is willing to help in forwarding the source node’s information, but at a price. The approach enables the source node to maximize its utility by selecting a partner node based on (i) the proximity of the partner node to the source and destination nodes, and (ii) the price the partner node will charge for the help being rendered. Our proposed scheme helps the source locate and select the relay nodes at ‘better’ locations and purchase power optimally from them. It also aids the contending relay nodes maximize their own utilities as well by asking proper prices. Our game scheme is seen to converge to unique equilibrium.

Keywords: Cooperative communication, game theory, node, power allocation, trade-off, utility.

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Authors: Joseph S. Ojo, Vincent A. Akpan, Oladayo G. Ajileye, Olalekan L, Ojo

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In this paper, the intelligent algorithm (IA) that is capable of adapting to dynamical tropical weather conditions is proposed based on fuzzy logic techniques. The IA effectively interacts with the quality of service (QoS) criteria irrespective of the dynamic tropical weather to achieve improvement in the satellite links. To achieve this, an adaptive network-based fuzzy inference system (ANFIS) has been adopted. The algorithm is capable of interacting with the weather fluctuation to generate appropriate improvement to the satellite QoS for efficient services to the customers. 5-year (2012-2016) rainfall rate of one-minute integration time series data has been used to derive fading based on ITU-R P. 618-12 propagation models. The data are obtained from the measurement undertaken by the Communication Research Group (CRG), Physics Department, Federal University of Technology, Akure, Nigeria. The rain attenuation and signal-to-noise ratio (SNR) were derived for frequency between Ku and V-band and propagation angle with respect to different transmitting power. The simulated results show a substantial reduction in SNR especially for application in the area of digital video broadcast-second generation coding modulation satellite networks.

Keywords: Fuzzy logic, intelligent algorithm, Nigeria, QoS, satellite applications, tropical weather.

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Authors: Yiorgos Hadjichristou

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The paper will focus on the hybrid living typologies which are brought about due to the Global Crisis. Mixing of the generations and the groups of people, mingling the functions of living with working and socializing, merging the act of living in synergy with the urban realm and its constituent elements will be the springboard of proposing an essential sustainable housing approach and the respective urban development. The thematic will be based on methodologies developed both on the academic, educational environment including participation of students’ research and on the practical aspect of architecture including case studies executed by the author in the island of Cyprus. Both paths of the research will deal with the explorative understanding of the hybrid ways of living, testing the limits of its autonomy. The evolution of the living typologies into substantial hybrid entities, will deal with the understanding of new ways of living which include among others: re-introduction of natural phenomena, accommodation of the activity of work and services in the living realm, interchange of public and private, injections of communal events into the individual living territories. The issues and the binary questions raised by what is natural and artificial, what is private and what public, what is ephemeral and what permanent and all the in-between conditions are eloquently traced in the everyday life in the island. Additionally, given the situation of Cyprus with the eminent scar of the dividing ‘Green line’ and the waiting of the ‘ghost city’ of Famagusta to be resurrected, the conventional way of understanding the limits and the definitions of the properties is irreversibly shaken. The situation is further aggravated by the unprecedented phenomenon of the crisis on the island. All these observations set the premises of reexamining the urban development and the respective sustainable housing in a synergy where their characteristics start exchanging positions, merge into each other, contemporarily emerge and vanish, changing from permanent to ephemeral. This fluidity of conditions will attempt to render a future of the built- and unbuilt realm where the main focusing point will be redirected to the human and the social. Weather and social ritual scenographies together with ‘spontaneous urban landscapes’ of ‘momentary relationships’ will suggest a recipe for emerging urban environments and sustainable living. Thus, the paper will aim at opening a discourse on the future of the sustainable living merged in a sustainable urban development in relation to the imminent solution of the division of island, where the issue of property became the main obstacle to be overcome. At the same time, it will attempt to link this approach to the global need for a sustainable evolution of the urban and living realms.

Keywords: Social ritual scenographies, spontaneous urban landscapes, substantial hybrid entities, re-introduction of natural phenomena.

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Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

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The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an Artificial Neural Network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study include granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R2), Root Mean Square Error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: National development, granite, profitability assessment, ANN models.

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Authors: C. Sathiyakumar, K. Duraiswamy

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In MANET, mobile nodes communicate with each other using the wireless channel where transmission takes place with significant interference. The wireless medium used in MANET is a shared resource used by all the nodes available in MANET. Packet reserving is one important resource management scheme which controls the allocation of bandwidth among multiple flows through node cooperation in MANET. This paper proposes packet reserving and clogging control via Routing Aware Packet Reserving (RAPR) framework in MANET. It mainly focuses the end-to-end routing condition with maximal throughput. RAPR is complimentary system where the packet reserving utilizes local routing information available in each node. Path setup in RAPR estimates the security level of the system, and symbolizes the end-to-end routing by controlling the clogging. RAPR reaches the packet to the destination with high probability ratio and minimal delay count. The standard performance measures such as network security level, communication overhead, end-to-end throughput, resource utilization efficiency and delay measure are considered in this work. The results reveals that the proposed packet reservation and clogging control via Routing Aware Packet Reserving (RAPR) framework performs well for the above said performance measures compare to the existing methods.

Keywords: Packet reserving, Clogging control, Packet reservation in MANET, RAPR.

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Authors: Layla Mujahed

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Amman, the capital of Jordan, is the main political, economic, social and cultural center of Jordan and beyond. The city faces multitude demographic challenges related to the unstable political situation in the surrounded countries. It has regional and local migrants who left their homes to find better life in the capital. This resulted with random and unequaled population distribution. Some districts have high population and pressure on the infrastructure and services more than other districts.Government works to resolve this challenge in compliance with 100 Cities Resilience Framework (CRF). Amman participated in this framework as a member in December 2014 to work in achieving the four goals: health and welfare, infrastructure and utilities, economy and education as well as administration and government.  Previous research studies lack in studying Amman resilient work in neighborhood scale and the population growth as resilient challenge. For that, this study focuses on Al-Thahabiah neighborhood in Shafa Badran district in Amman. This paper studies the reasons and drivers behind this population growth during the selected period in this area then provide strategies to improve the resilient work in neighborhood scale. The methodology comprises of primary and secondary data. The primary data consist of interviews with chief officer in the executive part in Great Amman Municipality and resilient officer. The secondary data consist of papers, journals, newspaper, articles and book’s reading. The other part of data consists of maps and statistical data which describe the infrastructural and social situation in the neighborhood and district level during the studying period. Based upon those data, more detailed information will be found, e.g., the centralizing position of population and the provided infrastructure for them. This will help to provide these services and infrastructure to other neighborhoods and enhance population distribution. This study develops an analytical framework to assess urban demographical time series in accordance with the criteria of CRF to make accurate detailed projections on the requirements for the future development in the neighborhood scale and organize the human requirements for affordable quality housing, employment, transportation, health and education in this neighborhood to improve the social relations between its inhabitants and the community. This study highlights on the localization of resilient work in neighborhood scale and spread the resilient knowledge related to the shortage of its research in Jordan. Studying the resilient work from population growth challenge perspective helps improve the facilities provide to the inhabitants and improve their quality of life.

Keywords: City resilience framework, CRF, demography, population growth, stakeholders, urban resilience.

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Authors: Susmita Das, Kala Praveen Bagadi

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SDMA (Space-Division Multiple Access) is a MIMO (Multiple-Input and Multiple-Output) based wireless communication network architecture which has the potential to significantly increase the spectral efficiency and the system performance. The maximum likelihood (ML) detection provides the optimal performance, but its complexity increases exponentially with the constellation size of modulation and number of users. The QR decomposition (QRD) MUD can be a substitute to ML detection due its low complexity and near optimal performance. The minimum mean-squared-error (MMSE) multiuser detection (MUD) minimises the mean square error (MSE), which may not give guarantee that the BER of the system is also minimum. But the minimum bit error rate (MBER) MUD performs better than the classic MMSE MUD in term of minimum probability of error by directly minimising the BER cost function. Also the MBER MUD is able to support more users than the number of receiving antennas, whereas the rest of MUDs fail in this scenario. In this paper the performance of various MUD techniques is verified for the correlated MIMO channel models based on IEEE 802.16n standard.

Keywords: Multiple input multiple output, multiuser detection, orthogonal frequency division multiplexing, space division multiple access, Bit error rate

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Authors: Aboamama Atahar Ahmed, Muhammad Shafie Abd Latiff, Kamalrulnizam Abu Bakar, Zainul AhmadRajion

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Distance visualization of large datasets often takes the direction of remote viewing and zooming techniques of stored static images. However, the continuous increase in the size of datasets and visualization operation causes insufficient performance with traditional desktop computers. Additionally, the visualization techniques such as Isosurface depend on the available resources of the running machine and the size of datasets. Moreover, the continuous demand for powerful computing powers and continuous increase in the size of datasets results an urgent need for a grid computing infrastructure. However, some issues arise in current grid such as resources availability at the client machines which are not sufficient enough to process large datasets. On top of that, different output devices and different network bandwidth between the visualization pipeline components often result output suitable for one machine and not suitable for another. In this paper we investigate how the grid services could be used to support remote visualization of large datasets and to break the constraint of physical co-location of the resources by applying the grid computing technologies. We show our grid enabled architecture to visualize large medical datasets (circa 5 million polygons) for remote interactive visualization on modest resources clients.

Keywords: Visualization, Grid computing, Medical datasets, visualization techniques, thin clients, Globus toolkit, VTK.

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: Building energy prediction, data mining, demand response, electricity market.

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Authors: Ahmed Ebian, Mona Shokair, Kamal Awadalla

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Mobile WiMAX is a broadband wireless solution that enables convergence of mobile and fixed broadband networks through a common wide area broadband radio access technology and flexible network architecture. It adopts Orthogonal Frequency Division Multiple Access (OFDMA) for improved multi-path performance in Non-Line-Of-Sight (NLOS) environments. Scalable OFDMA (SOFDMA) is introduced in the IEEE 802e[1]. WIMAX system uses one of different types of channel coding but The mandatory channel coding scheme is based on binary nonrecursive Convolutional Coding (CC). There are other several optional channel coding schemes such as block turbo codes, convolutional turbo codes, and low density parity check (LDPC). In this paper a comparison between the performance of WIMAX using turbo code and using convolutional product code (CPC) [2] is made. Also a combination between them had been done. The CPC gives good results at different SNR values compared to both the turbo system, and the combination between them. For example, at BER equal to 10-2 for 128 subcarriers, the amount of improvement in SNR equals approximately 3 dB higher than turbo code and equals approximately 2dB higher than the combination respectively. Several results are obtained at different modulating schemes (16QAM and 64QAM) and different numbers of sub-carriers (128 and 512).

Keywords: Turbo Code, Convolutional Product Code (CPC), Convolutional Product Code (CPC).

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Authors: Andreas Theissler, Ian Dear

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In the automotive industry test drives are being conducted during the development of new vehicle models or as a part of quality assurance of series-production vehicles. The communication on the in-vehicle network, data from external sensors, or internal data from the electronic control units is recorded by automotive data loggers during the test drives. The recordings are used for fault analysis. Since the resulting data volume is tremendous, manually analysing each recording in great detail is not feasible. This paper proposes to use machine learning to support domainexperts by preventing them from contemplating irrelevant data and rather pointing them to the relevant parts in the recordings. The underlying idea is to learn the normal behaviour from available recordings, i.e. a training set, and then to autonomously detect unexpected deviations and report them as anomalies. The one-class support vector machine “support vector data description” is utilised to calculate distances of feature vectors. SVDDSUBSEQ is proposed as a novel approach, allowing to classify subsequences in multivariate time series data. The approach allows to detect unexpected faults without modelling effort as is shown with experimental results on recordings from test drives.

Keywords: Anomaly detection, fault detection, test drive analysis, machine learning.

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Authors: Bassma Aldahlan, Zongming Fei

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Recently, there is a great interest in smart parking application. Theses applications are enhanced by a vehicular ad-hoc network, which helps drivers find and reserve satiable packing spaces for a period of time ahead of time. Named Data Networking (NDN) is a future Internet architecture that benefits vehicular ad-hoc networks because of its clean-slate design and pure communication model. In this paper, we proposed an NDN-based frame-work for smart parking that involved a fog computing architecture. The proposed application had two main directions: First, we allowed drivers to query the number of parking spaces in a particular parking lot. Second, we introduced a technique that enabled drivers to make intelligent reservations before their arrival time. We also introduced a “push-based” model supporting the NDN-based framework for smart parking applications. To evaluate the proposed solution’s performance, we analyzed the function for finding parking lots with available parking spaces and the function for reserving a parking space. Our system showed high performance results in terms of response time and push overhead. The proposed reservation application performed better than the baseline approach.

Keywords: Cloud Computing, Vehicular Named Data Networking, Smart Parking Applications, Fog Computing.

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Authors: Zhiwen Pan, Jesus Pacheco, Salim Hariri, Yiqiang Chen, Bozhi Liu

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The Internet of Things (IoT) will lead to the development of advanced Smart Home services that are pervasive, cost-effective, and can be accessed by home occupants from anywhere and at any time. However, advanced smart home applications will introduce grand security challenges due to the increase in the attack surface. Current approaches do not handle cybersecurity from a holistic point of view; hence, a systematic cybersecurity mechanism needs to be adopted when designing smart home applications. In this paper, we present a generic intrusion detection methodology to detect and mitigate the anomaly behaviors happened in Smart Home Systems (SHS). By utilizing our Smart Home Context Data Structure, the heterogeneous information and services acquired from SHS are mapped in context attributes which can describe the context of smart home operation precisely and accurately. Runtime models for describing usage patterns of home assets are developed based on characterization functions. A threat-aware action management methodology, used to efficiently mitigate anomaly behaviors, is proposed at the end. Our preliminary experimental results show that our methodology can be used to detect and mitigate known and unknown threats, as well as to protect SHS premises and services.

Keywords: Internet of Things, network security, context awareness, intrusion detection

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Authors: Sheena Christabel Pravin, M. Palanivelan

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Speech disfluencies are common in spontaneous speech. The primary purpose of this study was to distinguish linguistic disfluencies from stuttering disfluencies in bilingual Tamil–English (TE) speaking children. The secondary purpose was to determine whether their disfluencies are mediated by native language dominance and/or on an early onset of developmental stuttering at childhood. A detailed study was carried out to identify the prosodic and acoustic features that uniquely represent the disfluent regions of speech. This paper focuses on statistical modeling of repetitions, prolongations, pauses and interjections in the speech corpus encompassing bilingual spontaneous utterances from school going children – English and Tamil. Two classifiers including Hidden Markov Models (HMM) and the Multilayer Perceptron (MLP), which is a class of feed-forward artificial neural network, were compared in the classification of disfluencies. The results of the classifiers document the patterns of disfluency in spontaneous speech samples of school-aged children to distinguish between Children Who Stutter (CWS) and Children with Language Impairment CLI). The ability of the models in classifying the disfluencies was measured in terms of F-measure, Recall, and Precision.

Keywords: Bilingual, children who stutter, children with language impairment, Hidden Markov Models, multi-layer perceptron, linguistic disfluencies, stuttering disfluencies.

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Authors: Meng-Lung Lin, Chien-Min Chu, Chung-Hung Tsai, Chih-Cheng Chen, Chen-Yuan Chen

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The study of tourist activities and the mapping of their routes in space and time has become an important issue in tourism management. Here we represent space-time paths for the tourism industry by visualizing individual tourist activities and the paths followed using a 3D Geographic Information System (GIS). Considerable attention has been devoted to the measurement of accessibility to shopping, eating, walking and other services at the tourist destination. I turns out that GIS is a useful tool for studying the spatial behaviors of tourists in the area. The value of GIS is especially advantageous for space-time potential path area measures, especially for the accurate visualization of possible paths through existing city road networks. This study seeks to apply space-time concepts with a detailed street network map obtained from Google Maps to measure tourist paths both spatially and temporally. These paths are further determined based on data obtained from map questionnaires regarding the trip activities of 40 individuals. The analysis of the data makes it possible to determining the locations of the more popular paths. The results can be visualized using 3D GIS to show the areas and potential activity opportunities accessible to tourists during their travel time.

Keywords: Tourist activity analysis, space-time path, GIS, geovisualization, activity-travel pattern.

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Authors: Om Prakash Verma, Rajesh Singla, Rajesh Kumar

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Conventional controller’s usually required a prior knowledge of mathematical modelling of the process. The inaccuracy of mathematical modelling degrades the performance of the process, especially for non-linear and complex control problem. The process used is Water-Bath system, which is most widely used and nonlinear to some extent. For Water-Bath system, it is necessary to attain desired temperature within a specified period of time to avoid the overshoot and absolute error, with better temperature tracking capability, else the process is disturbed.

To overcome above difficulties intelligent controllers, Fuzzy Logic (FL) and Adaptive Neuro-Fuzzy Inference System (ANFIS), are proposed in this paper. The Fuzzy controller is designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. To design ANFIS, Fuzzy-Inference-System is combined with learning capability of Neural-Network.

It is analyzed that ANFIS is best suitable for adaptive temperature control of above system. As compared to PID and FLC, ANFIS produces a stable control signal. It has much better temperature tracking capability with almost zero overshoot and minimum absolute error.

Keywords: PID Controller, FLC, ANFIS, Non-Linear Control System, Water-Bath System, MATLAB-7.

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Authors: H. Iranmanesh, M. R. Skandari, M. Allahverdiloo

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Project managers are the ultimate responsible for the overall characteristics of a project, i.e. they should deliver the project on time with minimum cost and with maximum quality. It is vital for any manager to decide a trade-off between these conflicting objectives and they will be benefited of any scientific decision support tool. Our work will try to determine optimal solutions (rather than a single optimal solution) from which the project manager will select his desirable choice to run the project. In this paper, the problem in project scheduling notated as (1,T|cpm,disc,mu|curve:quality,time,cost) will be studied. The problem is multi-objective and the purpose is finding the Pareto optimal front of time, cost and quality of a project (curve:quality,time,cost), whose activities belong to a start to finish activity relationship network (cpm) and they can be done in different possible modes (mu) which are non-continuous or discrete (disc), and each mode has a different cost, time and quality . The project is constrained to a non-renewable resource i.e. money (1,T). Because the problem is NP-Hard, to solve the problem, a meta-heuristic is developed based on a version of genetic algorithm specially adapted to solve multi-objective problems namely FastPGA. A sample project with 30 activities is generated and then solved by the proposed method.

Keywords: FastPGA, Multi-Execution Activity Mode, ParetoOptimality, Project Scheduling, Time-Cost-Quality Trade-Off.

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Authors: Priyanka Paliwal, N.P. Patidar

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Distributed Power generation has gained a lot of attention in recent times due to constraints associated with conventional power generation and new advancements in DG technologies .The need to operate the power system economically and with optimum levels of reliability has further led to an increase in interest in Distributed Generation. However it is important to place Distributed Generator on an optimum location so that the purpose of loss minimization and voltage regulation is dully served on the feeder. This paper investigates the impact of DG units installation on electric losses, reliability and voltage profile of distribution networks. In this paper, our aim would be to find optimal distributed generation allocation for loss reduction subjected to constraint of voltage regulation in distribution network. The system is further analyzed for increased levels of Reliability. Distributed Generator offers the additional advantage of increase in reliability levels as suggested by the improvements in various reliability indices such as SAIDI, CAIDI and AENS. Comparative studies are performed and related results are addressed. An analytical technique is used in order to find the optimal location of Distributed Generator. The suggested technique is programmed under MATLAB software. The results clearly indicate that DG can reduce the electrical line loss while simultaneously improving the reliability of the system.

Keywords: AENS, CAIDI, Distributed Generation, lossreduction, Reliability, SAIDI

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Authors: G. T. Balakayeva, K. K. Nurlybayeva, M. B. Zhanuzakov

Abstract:

In the proposed work, we have developed software and designed a software architecture for the implementation of enterprise business processes. The proposed software has a multi-level architecture using a domain-specific tool. The developed architecture is a guarantor of the availability, reliability and security of the system and the implementation of business processes, which are the basis for effective enterprise management. Automating business processes, automating the algorithmic stages of an enterprise, developing optimal algorithms for managing activities, controlling and monitoring, reducing risks and improving results help organizations achieve strategic goals quickly and efficiently. The software described in this article can connect to the corporate information system via two methods: a desktop client and a web client. With an appeal to the application server, the desktop client program connects to the information system on the company's work PCs over a local network. Outside the organization, the user can interact with the information system via a web browser, which acts as a web client and connects to a web server. The developed software consists of several integrated modules that share resources and interact with each other through an API. The following technology stack was used during development: Node js, React js, MongoDB, Ngnix, Cloud Technologies, Python.

Keywords: Algorithms, document processing, automation, integrated modules, software architecture, software design, information system.

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Authors: S. Mohideen Badhusha, K. Duraiswamy

Abstract:

Most of the existing video streaming protocols provide video services without considering security aspects in decentralized mobile ad-hoc networks. The security policies adapted to the currently existing non-streaming protocols, do not comply with the live video streaming protocols resulting in considerable vulnerability, high bandwidth consumption and unreliability which cause severe security threats, low bandwidth and error prone transmission respectively in video streaming applications. Therefore a synergized methodology is required to reduce vulnerability and bandwidth consumption, and enhance reliability in the video streaming applications in MANET. To ensure the security measures with reduced bandwidth consumption and improve reliability of the video streaming applications, a Secure Low-bandwidth Video Streaming through Reliable Multipath Propagation (SLVRMP) protocol architecture has been proposed by incorporating the two algorithms namely Secure Low-bandwidth Video Streaming Algorithm and Reliable Secure Multipath Propagation Algorithm using Layered Video Coding in non-overlapping zone routing network topology. The performances of the proposed system are compared to those of the other existing secure multipath protocols Sec-MR, SPREAD using NS 2.34 and the simulation results show that the performances of the proposed system get considerably improved.

Keywords: Bandwidth consumption, layered video coding, multipath propagation, reliability, security threats, video streaming applications, vulnerability.

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Authors: Yong W. Foo, Lai M. Tang

Abstract:

Artificial Intelligence (AI), particularly the usage of deep neural networks for hierarchical representations from data, has found numerous complex applications across various domains, including computer vision, robotics, autonomous vehicles, and other scientific fields. However, their inherent “black box” nature can sometimes make it challenging for early researchers or school students of various levels to comprehend and trust the results they produce. Consequently, there has been a growing demand for reliable visualization tools in engineering and science education to help learners understand, trust, and explain a deep learning network. This has led to a notable emphasis on the visualization of AI in the research community in recent years. AI visualization tools are increasingly being adopted to significantly improve the comprehension of complex topics in deep learning. This paper presents an approach to empower students to actively explore the inner workings of deep neural networks by integrating the student-centered learning approach of flipped classroom models with the investigative capabilities of AI visualization tools, namely, the TensorFlow Playground, the Local Interpretable Model-agnostic Explanations (LIME), and the SHapley Additive exPlanations (SHAP), for delivering an AI education curriculum. Integrating these two factors is crucial for fostering ownership, responsibility, and critical thinking skills in the age of AI.

Keywords: Deep Learning, Explainable AI, AI Visualization, Representation Learning.

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Authors: Jessica Lòpez Espejel, Mahaman Sanoussi Yahaya Alassan, Walid Dahhane, El Hassane Ettifouri

Abstract:

Pretrained transformer-based models have shown high performance in natural language generation task. However, a new wave of interest has surged: automatic programming language generation. This task consists of translating natural language instructions to a programming code. Despite the fact that well-known pretrained models on language generation have achieved good performance in learning programming languages, effort is still needed in automatic code generation. In this paper, we introduce JaCoText, a model based on Transformers neural network. It aims to generate java source code from natural language text. JaCoText leverages advantages of both natural language and code generation models. More specifically, we study some findings from the state of the art and use them to (1) initialize our model from powerful pretrained models, (2) explore additional pretraining on our java dataset, (3) carry out experiments combining the unimodal and bimodal data in the training, and (4) scale the input and output length during the fine-tuning of the model. Conducted experiments on CONCODE dataset show that JaCoText achieves new state-of-the-art results.

Keywords: Java code generation, Natural Language Processing, Sequence-to-sequence Models, Transformers Neural Networks.

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Authors: L. A. Muhammad, M. S. Argungu

Abstract:

Educational data mining (EDM) has recorded substantial considerations. Techniques of data mining in one way or the other have been proposed to dig out out-of-sight knowledge in educational data. The result of the study got assists academic institutions in further enhancing their process of learning and methods of passing knowledge to students. Consequently, the performance of students boasts and the educational products are by no doubt enhanced. This study adopted a student performance prediction model premised on techniques of data mining with Students' Essential Features (SEF). SEF are linked to the learner's interactivity with the e-learning management system. The performance of the student's predictive model is assessed by a set of classifiers, viz. Bayes Network, Logistic Regression, and Reduce Error Pruning Tree (REP). Consequently, ensemble methods of Bagging, Boosting, and Random Forest (RF) are applied to improve the performance of these single classifiers. The study reveals that the result shows a robust affinity between learners' behaviors and their academic attainment. Result from the study shows that the REP Tree and its ensemble record the highest accuracy of 83.33% using SEF. Hence, in terms of the Receiver Operating Curve (ROC), boosting method of REP Tree records 0.903, which is the best. This result further demonstrates the dependability of the proposed model.

Keywords: Ensemble, bagging, Random Forest, boosting, data mining, classifiers, machine learning.

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Authors: Ayman El Ansary, Mohamed Shalaby

Abstract:

The current study describes a multi-objective optimization technique for positioning of houses in a residential neighborhood. The main task is the placement of residential houses in a favorable configuration satisfying a number of objectives. Solving the house layout problem is a challenging task. It requires an iterative approach to satisfy design requirements (e.g. energy efficiency, skyview, daylight, roads network, visual privacy, and clear access to favorite views). These design requirements vary from one project to another based on location and client preferences. In the Gulf region, the most important socio-cultural factor is the visual privacy in indoor space. Hence, most of the residential houses in this region are surrounded by high fences to provide privacy, which has a direct impact on other requirements (e.g. daylight and direction to favorite views). This investigation introduces a novel technique to optimally locate and orient residential buildings to satisfy a set of design requirements. The developed technique explores the search space for possible solutions. This study considers two dimensional house planning problems. However, it can be extended to solve three dimensional cases.

Keywords: Evolutionary optimization, Houses planning, Urban modeling, Daylight, Visual Privacy, Residential compounds.

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Authors: Jamileh Vasheghani Farahani

Abstract:

The main purpose of this research is to show the current active faults and active tectonic of the area by three seismic networks in Tehran region: 1-Tehran Disaster Mitigation and Management Organization (TDMMO), 2-Broadband Iranian National Seismic Network Center (BIN), 3-Iranian Seismological Center (IRSC). In this study, we analyzed microearthquakes happened in Tehran city and its surroundings using the Tehran networks from 1996 to 2015. We found some active faults and trends in the region. There is a 200-year history of historical earthquakes in Tehran. Historical and instrumental seismicity show that the east of Tehran is more active than the west. The Mosha fault in the North of Tehran is one of the active faults of the central Alborz. Moreover, other major faults in the region are Kahrizak, Eyvanakey, Parchin and North Tehran faults. An important seismicity region is an intersection of the Mosha and North Tehran fault systems (Kalan village in Lavasan). This region shows a cluster of microearthquakes. According to the historical and microseismic events analyzed in this research, there is a seismic gap in SE of Tehran. The empirical relationship is used to assess the Mmax based on the rupture length. There is a probability of occurrence of a strong motion of 7.0 to 7.5 magnitudes in the region (based on the assessed capability of the major faults such as Parchin and Eyvanekey faults and historical earthquakes).

Keywords: Iran, major faults, microseismicity, Tehran.

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