Search results for: Bi-directional associative memory (BAM) neural networks

Authors: Rehab I. Abdul Rahman, Mazhar B. Tayel

Abstract:

This paper presents a method of hardening the 8051 micro-controller, able to assure reliable operation in the presence of bit flips caused by radiation. Aiming at avoiding such faults in the 8051 micro-controller, Hamming code protection was used in its SRAM memory and registers. A VHDL code has been used for this hamming code protection.

Keywords: Radiation, hardening, bitflip, hamming code.

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Authors: Brennan Lodge

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Amidst a complex regulatory landscape, Retrieval Augmented Generation (RAG) emerges as a transformative tool for Governance Risk and Compliance (GRC) officers. This paper details the application of RAG in synthesizing Large Language Models (LLMs) with external knowledge bases, offering GRC professionals an advanced means to adapt to rapid changes in compliance requirements. While the development for standalone LLMs is exciting, such models do have their downsides. LLMs cannot easily expand or revise their memory, and they cannot straightforwardly provide insight into their predictions, and may produce “hallucinations.” Leveraging a pre-trained seq2seq transformer and a dense vector index of domain-specific data, this approach integrates real-time data retrieval into the generative process, enabling gap analysis and the dynamic generation of compliance and risk management content. We delve into the mechanics of RAG, focusing on its dual structure that pairs parametric knowledge contained within the transformer model with non-parametric data extracted from an updatable corpus. This hybrid model enhances decision-making through context-rich insights, drawing from the most current and relevant information, thereby enabling GRC officers to maintain a proactive compliance stance. Our methodology aligns with the latest advances in neural network fine-tuning, providing a granular, token-level application of retrieved information to inform and generate compliance narratives. By employing RAG, we exhibit a scalable solution that can adapt to novel regulatory challenges and cybersecurity threats, offering GRC officers a robust, predictive tool that augments their expertise. The granular application of RAG’s dual structure not only improves compliance and risk management protocols but also informs the development of compliance narratives with pinpoint accuracy. It underscores AI’s emerging role in strategic risk mitigation and proactive policy formation, positioning GRC officers to anticipate and navigate the complexities of regulatory evolution confidently.

Keywords: Retrieval Augmented Generation, Governance Risk and Compliance, Cybersecurity, AI-driven Compliance, Risk Management, Generative AI.

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Authors: Abdel Ilah Alshabtat, Liang Dong

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In this paper, we proposed a new routing protocol for Unmanned Aerial Vehicles (UAVs) that equipped with directional antenna. We named this protocol Directional Optimized Link State Routing Protocol (DOLSR). This protocol is based on the well known protocol that is called Optimized Link State Routing Protocol (OLSR). We focused in our protocol on the multipoint relay (MPR) concept which is the most important feature of this protocol. We developed a heuristic that allows DOLSR protocol to minimize the number of the multipoint relays. With this new protocol the number of overhead packets will be reduced and the End-to-End delay of the network will also be minimized. We showed through simulation that our protocol outperformed Optimized Link State Routing Protocol, Dynamic Source Routing (DSR) protocol and Ad- Hoc On demand Distance Vector (AODV) routing protocol in reducing the End-to-End delay and enhancing the overall throughput. Our evaluation of the previous protocols was based on the OPNET network simulation tool.

Keywords: Mobile Ad-Hoc Networks, Ad-Hoc RoutingProtocols, Optimized link State Routing Protocol, Unmanned AerialVehicles, Directional Antenna.

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Authors: Chia-Feng Lu, Yuh-Jen Wang, Yu-Te Wu, Sui-Hing Yan

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This study aimed at investigating whether the functional brain networks constructed using the initial EEG (obtained when patients first visited hospital) can be correlated with the progression of cognitive decline calculated as the changes of mini-mental state examination (MMSE) scores between the latest and initial examinations. We integrated the time–frequency cross mutual information (TFCMI) method to estimate the EEG functional connectivity between cortical regions, and the network analysis based on graph theory to investigate the organization of functional networks in aMCI. Our finding suggested that higher integrated functional network with sufficient connection strengths, dense connection between local regions, and high network efficiency in processing information at the initial stage may result in a better prognosis of the subsequent cognitive functions for aMCI. In conclusion, the functional connectivity can be a useful biomarker to assist in prediction of cognitive declines in aMCI.

Keywords: Cognitive decline, functional connectivity, MCI, MMSE.

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Authors: Mahmood Reza Shakarami, Reza Sedaghati

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Recently, distributed generation technologies have received much attention for the potential energy savings and reliability assurances that might be achieved as a result of their widespread adoption. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering wind turbines. The main objective of the DFR is to minimize the deviation of the bus voltage. Since the DFR is a nonlinear optimization problem, we apply the Adaptive Modified Firefly Optimization (AMFO) approach to solve it. As a result of the conflicting behavior of the single- objective function, a fuzzy based clustering technique is employed to reach the set of optimal solutions called Pareto solutions. The approach is tested on the IEEE 32-bus standard test system.

Keywords: Adaptive Modified Firefly Optimization (AMFO), Pareto solutions, feeder reconfiguration, wind turbines, bus voltage.

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Authors: R. Khadim, M. Erritali, A. Maaden

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Localization of nodes is one of the key issues of Wireless Sensor Network (WSN) that gained a wide attention in recent years. The existing localization techniques can be generally categorized into two types: range-based and range-free. Compared with rang-based schemes, the range-free schemes are more costeffective, because no additional ranging devices are needed. As a result, we focus our research on the range-free schemes. In this paper we study three types of range-free location algorithms to compare the localization error and energy consumption of each one. Centroid algorithm requires a normal node has at least three neighbor anchors, while DV-hop algorithm doesn’t have this requirement. The third studied algorithm is the amorphous algorithm similar to DV-Hop algorithm, and the idea is to calculate the hop distance between two nodes instead of the linear distance between them. The simulation results show that the localization accuracy of the amorphous algorithm is higher than that of other algorithms and the energy consumption does not increase too much.

Keywords: Wireless Sensor Networks, Node Localization, Centroid Algorithm, DV–Hop Algorithm, Amorphous Algorithm.

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Authors: Mohamed Adnan Landolsi, Ali F. Almutairi

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The paper addresses the problem of line-of-sight (LOS) vs. non-line-of-sight (NLOS) propagation link identification in ultra-wideband (UWB) wireless networks, which is necessary for improving the accuracy of radiolocation and positioning applications. A LOS/NLOS likelihood hypothesis testing approach is applied based on exploiting distinctive statistical features of the channel impulse response (CIR) using parameters related to the “skewness” of the CIR and its root mean square (RMS) delay spread. A log-normal fit is presented for the probability densities of the CIR parameters. Simulation results show that different environments (residential, office, outdoor, etc.) have measurable differences in their CIR parameters’ statistics, which is then exploited in determining the nature of the propagation channels. Correct LOS/NLOS channel identification rates exceeding 90% are shown to be achievable for most types of environments. Additional improvement is also obtained by combining both CIR skewness and RMS delay statistics.

Keywords: Ultra-wideband, propagation, line-of-sight, non-line-of-sight, identification.

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Authors: K. Singh, T. P. Sharma

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In wireless sensor network (WSN) the use of mobile sink has been attracting more attention in recent times. Mobile sinks are more effective means of balancing load, reducing hotspot problem and elongating network lifetime. The sensor nodes in WSN have limited power supply, computational capability and storage and therefore for continuous data delivery reliability becomes high priority in these networks. In this paper, we propose a Reliable Energy-efficient Data Dissemination (REDD) scheme for WSNs with multiple mobile sinks. In this strategy, sink first determines the location of source and then directly communicates with the source using geographical forwarding. Every forwarding node (FN) creates a local zone comprising some sensor nodes that can act as representative of FN when it fails. Analytical and simulation study reveals significant improvement in energy conservation and reliable data delivery in comparison to existing schemes.

Keywords: Energy Efficient, REED, Sink Mobility, WSN.

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Authors: Nasim Fallahi, Erasmo Carrera, Alfonso Pagani

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Variable angle tow describes the fibres which are curvilinearly steered in a composite lamina. Significantly, stiffness tailoring freedom of VAT composite laminate can be enlarged and enabled. Composite structures with curvilinear fibres have been shown to improve the buckling load carrying capability in contrast with the straight laminate composites. However, the optimal design and analysis of VAT are faced with high computational efforts due to the increasing number of variables. In this article, an efficient optimum solution has been used in combination with 1D Carrera’s Unified Formulation (CUF) to investigate the optimum fibre orientation angles for buckling analysis. The particular emphasis is on the LE-based CUF models, which provide a Lagrange Expansions to address a layerwise description of the problem unknowns. The first critical buckling load has been considered under simply supported boundary conditions. Special attention is lead to the sensitivity of buckling load corresponding to the fibre orientation angle in comparison with the results which obtain through the Genetic Algorithm (GA) optimization frame and then Artificial Neural Network (ANN) is applied to investigate the accuracy of the optimized model. As a result, numerical CUF approach with an optimal solution demonstrates the robustness and computational efficiency of proposed optimum methodology.

Keywords: Beam structures, layerwise, optimization, variable angle tow, neural network

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Authors: Giulia I. Pintea

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The Roma people are a nomadic ethnic group native to India, and they are one of the most prevalent minorities in Europe. In the past, Roma were enslaved and they were imprisoned in concentration camps during the Holocaust; today, Roma are subject to hate crimes and are denied access to healthcare, education, and proper housing. The aim of this project is to analyze how the public perception of the Roma people may be influenced by antiziganist and pro-Roma institutions in Europe. In order to carry out this project, we used social network analysis to build two large social networks: The antiziganist network, which is composed of institutions that oppress and racialize Roma, and the pro-Roma network, which is composed of institutions that advocate for and protect Roma rights. Measures of centrality, density, and modularity were obtained to determine which of the two social networks is exerting the greatest influence on the public’s perception of Roma in European societies. Furthermore, data on hate crimes on Roma were gathered from the Organization for Security and Cooperation in Europe (OSCE). We analyzed the trends in hate crimes on Roma for several European countries for 2009-2015 in order to see whether or not there have been changes in the public’s perception of Roma, thus helping us evaluate which of the two social networks has been more influential. Overall, the results suggest that there is a greater and faster exchange of information in the pro-Roma network. However, when taking the hate crimes into account, the impact of the pro-Roma institutions is ambiguous, due to differing patterns among European countries, suggesting that the impact of the pro-Roma network is inconsistent. Despite antiziganist institutions having a slower flow of information, the hate crime patterns also suggest that the antiziganist network has a higher impact on certain countries, which may be due to institutions outside the political sphere boosting the spread of antiziganist ideas and information to the European public.

Keywords: Applied mathematics, oppression, Roma people, social network analysis.

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Authors: A. Pedram, M. R. Jamali, T. Pedram, S. M. Fakhraie, C. Lucas

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Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.

Keywords: LOLIMOT, hardware, neurofuzzy systems, reconfigurable, parallel.

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Authors: Debajyoti Mishra, Urmila Bhanja

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In this paper, a novel fuzzy approach is developed while solving the Dynamic Routing and Wavelength Assignment (DRWA) problem in optical networks with Wavelength Division Multiplexing (WDM). In this work, the effect of nonlinear and linear impairments such as Four Wave Mixing (FWM) and amplifier spontaneous emission (ASE) noise are incorporated respectively. The novel algorithm incorporates fuzzy logic controller (FLC) to reduce the effect of FWM noise and ASE noise on a requested lightpath referred in this work as FWM aware fuzzy dynamic routing and wavelength assignment algorithm. The FWM crosstalk products and the static FWM noise power per link are pre computed in order to reduce the set up time of a requested lightpath, and stored in an offline database. These are retrieved during the setting up of a lightpath and evaluated online taking the dynamic parameters like cost of the links into consideration.

Keywords: Amplifier spontaneous emission (ASE), Dynamic routing and wavelength assignment, Four wave mixing (FWM), Fuzzy rule based system (FRBS).

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Authors: Walenty Oniszczuk

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The use of buffer thresholds, blocking and adequate service strategies are well-known techniques for computer networks traffic congestion control. This motivates the study of series queues with blocking, feedback (service under Head of Line (HoL) priority discipline) and finite capacity buffers with thresholds. In this paper, the external traffic is modelled using the Poisson process and the service times have been modelled using the exponential distribution. We consider a three-station network with two finite buffers, for which a set of thresholds (tm1 and tm2) is defined. This computer network behaves as follows. A task, which finishes its service at station B, gets sent back to station A for re-processing with probability o. When the number of tasks in the second buffer exceeds a threshold tm2 and the number of task in the first buffer is less than tm1, the fed back task is served under HoL priority discipline. In opposite case, for fed backed tasks, “no two priority services in succession" procedure (preventing a possible overflow in the first buffer) is applied. Using an open Markovian queuing schema with blocking, priority feedback service and thresholds, a closed form cost-effective analytical solution is obtained. The model of servers linked in series is very accurate. It is derived directly from a twodimensional state graph and a set of steady-state equations, followed by calculations of main measures of effectiveness. Consequently, efficient expressions of the low computational cost are determined. Based on numerical experiments and collected results we conclude that the proposed model with blocking, feedback and thresholds can provide accurate performance estimates of linked in series networks.

Keywords: Blocking, Congestion control, Feedback, Markov chains, Performance evaluation, Threshold-base networks.

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Authors: Mohsen Soori, Fooad Karimi Ghaleh Jough

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The integration of Artificial Intelligence (AI) techniques in the optimization of steel moment frame structures represents a transformative approach to enhance the design, analysis, and performance of these critical engineering systems. The review encompasses a wide spectrum of AI methods, including machine learning algorithms, evolutionary algorithms, neural networks, and optimization techniques, applied to address various challenges in the field. The synthesis of research findings highlights the interdisciplinary nature of AI applications in structural engineering, emphasizing the synergy between domain expertise and advanced computational methodologies. This synthesis aims to serve as a valuable resource for researchers, practitioners, and policymakers seeking a comprehensive understanding of the state-of-the-art in AI-driven optimization for steel moment frame structures. The paper commences with an overview of the fundamental principles governing steel moment frame structures and identifies the key optimization objectives, such as efficiency of structures. Subsequently, it delves into the application of AI in the conceptual design phase, where algorithms aid in generating innovative structural configurations and optimizing material utilization. The review also explores the use of AI for real-time structural health monitoring and predictive maintenance, contributing to the long-term sustainability and reliability of steel moment frame structures. Furthermore, the paper investigates how AI-driven algorithms facilitate the calibration of structural models, enabling accurate prediction of dynamic responses and seismic performance. Thus, by reviewing and analyzing the recent achievements in applications artificial intelligent in optimization of steel moment frame structures, the process of designing, analysis, and performance of the structures can be analyzed and modified.

Keywords: Artificial Intelligent, optimization process, steel moment frame, structural engineering.

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Authors: M. R. Homaeinezhad, E. Tavakkoli, M. Habibi, S. A. Atyabi, A. Ghaffari

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This paper describes a new supervised fusion (hybrid) electrocardiogram (ECG) classification solution consisting of a new QRS complex geometrical feature extraction as well as a new version of the learning vector quantization (LVQ) classification algorithm aimed for overcoming the stability-plasticity dilemma. Toward this objective, after detection and delineation of the major events of ECG signal via an appropriate algorithm, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Then, the curve length of each excerpted segment is calculated and is used as the element of the feature space. To increase the robustness of the proposed classification algorithm versus noise, artifacts and arrhythmic outliers, a fusion structure consisting of five different classifiers namely as Support Vector Machine (SVM), Modified Learning Vector Quantization (MLVQ) and three Multi Layer Perceptron-Back Propagation (MLP–BP) neural networks with different topologies were designed and implemented. The new proposed algorithm was applied to all 48 MIT–BIH Arrhythmia Database records (within–record analysis) and the discrimination power of the classifier in isolation of different beat types of each record was assessed and as the result, the average accuracy value Acc=98.51% was obtained. Also, the proposed method was applied to 6 number of arrhythmias (Normal, LBBB, RBBB, PVC, APB, PB) belonging to 20 different records of the aforementioned database (between– record analysis) and the average value of Acc=95.6% was achieved. To evaluate performance quality of the new proposed hybrid learning machine, the obtained results were compared with similar peer– reviewed studies in this area.

Keywords: Feature Extraction, Curve Length Method, SupportVector Machine, Learning Vector Quantization, Multi Layer Perceptron, Fusion (Hybrid) Classification, Arrhythmia Classification, Supervised Learning Machine.

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Authors: S. Bag, S. Ramaiah, P. Anitha, K. M. Kumar, P. Lavanya, V. Sivasakhthi, A. Anbarasu

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Gene networks present a graphical view at the level of gene activities and genetic functions and help us to understand complex interactions in a meaningful manner. In the present study, we have analyzed the gene interaction of PPAR-γ (peroxisome proliferator-activated receptor gamma) by search tool for retrieval of interacting genes. We find PPAR-γ is highly networked by genetic interactions with 10 genes: RXRA (retinoid X receptor, alpha), PPARGC1A (peroxisome proliferator-activated receptor gamma, coactivator 1 alpha), NCOA1 (nuclear receptor coactivator 1), NR0B2 (nuclear receptor subfamily 0, group B, member 2), HDAC3 (histone deacetylase 3), MED1 (mediator complex subunit 1), INS (insulin), NCOR2 (nuclear receptor co-repressor 2), PAX8 (paired box 8), ADIPOQ (adiponectin) and it augurs well for the fact that obesity and several other metabolic disorders are inter related.

Keywords: Gene networks, NCOA1, PPARγ, PPARGC1A, RXRA.

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Authors: Myung-Won Lee, Keun-Chang Kwak

Abstract:

This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homogeneity between the clustered patterns from the IT2 contexts produced in the output space. Furthermore, we can obtain the automatic knowledge representation in the design of Radial Basis Function Networks (RBFN), Linguistic Model (LM), and Adaptive Neuro-Fuzzy Networks (ANFN) from the numerical input-output data pairs. We shall focus on a design of ANFN in this paper. The experimental results on an estimation problem of energy performance reveal that the proposed method showed a good knowledge representation and performance in comparison with the previous works.

Keywords: IT2-FCM, IT2-CFCM, context-based fuzzy clustering, adaptive neuro-fuzzy network, knowledge representation.

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Authors: Izabela Pietras

Abstract:

The level of visual abilities, language, memory processes and intellectual functioning development affects the quality of a written text. The following analysis will present the results of diagnostic tests indicating the most common criterion for a group and stating whether a person has been diagnosed with having cognitive developmental level below the group-s average or not.The study-s aim is to determine whether there are specific patterns of cognitive deficits, which can be distinguished among the group of young people with spelling disorders.

Keywords: cognitive deficits, cognitive functions, spellingdisorders

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Authors: Yu-Chen Kuo, Shih-Chieh Lin

Abstract:

Sensor relocation is to repair coverage holes caused by node failures. One way to repair coverage holes is to find redundant nodes to replace faulty nodes. Most researches took a long time to find redundant nodes since they randomly scattered redundant nodes around the sensing field. To record the precise position of sensor nodes, most researches assumed that GPS was installed in sensor nodes. However, high costs and power-consumptions of GPS are heavy burdens for sensor nodes. Thus, we propose a fast sensor relocation algorithm to arrange redundant nodes to form redundant walls without GPS. Redundant walls are constructed in the position where the average distance to each sensor node is the shortest. Redundant walls can guide sensor nodes to find redundant nodes in the minimum time. Simulation results show that our algorithm can find the proper redundant node in the minimum time and reduce the relocation time with low message complexity.

Keywords: Coverage, distributed algorithm, sensor relocation, wireless sensor networks.

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Authors: Bin Mu, Site Li, Shijin Yuan

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Under the circumstance of environment deterioration, people are increasingly concerned about the quality of the environment, especially air quality. As a result, it is of great value to give accurate and timely forecast of AQI (air quality index). In order to simplify influencing factors of air quality in a city, and forecast the city’s AQI tomorrow, this study used MATLAB software and adopted the method of constructing a mathematic model of PCA-GABP to provide a solution. To be specific, this study firstly made principal component analysis (PCA) of influencing factors of AQI tomorrow including aspects of weather, industry waste gas and IAQI data today. Then, we used the back propagation neural network model (BP), which is optimized by genetic algorithm (GA), to give forecast of AQI tomorrow. In order to verify validity and accuracy of PCA-GABP model’s forecast capability. The study uses two statistical indices to evaluate AQI forecast results (normalized mean square error and fractional bias). Eventually, this study reduces mean square error by optimizing individual gene structure in genetic algorithm and adjusting the parameters of back propagation model. To conclude, the performance of the model to forecast AQI is comparatively convincing and the model is expected to take positive effect in AQI forecast in the future.

Keywords: AQI forecast, principal component analysis, genetic algorithm, back propagation neural network model.

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Authors: Haidar Safa, Hassan Artail, Ahmad Mehio, Hicham Zahr, Ziad Matragi

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The Mobile IP Standard has been developed to support mobility over the Internet. This standard contains several drawbacks as in the cases where packets are routed via sub-optimal paths and significant amount of signaling messages is generated due to the home registration procedure which keeps the network aware of the current location of the mobile nodes. Recently, a dynamic hierarchical mobility management strategy for mobile IP networks (DHMIP) has been proposed to reduce home registrations costs. However, this strategy induces a packet delivery delay and increases the risk of packet loss. In this paper, we propose an enhanced version of the dynamic hierarchical strategy that reduces the packet delivery delay and minimizes the risk of packet loss. Preliminary results obtained from simulations are promising. They show that the enhanced version outperforms the original dynamic hierarchical mobility management strategy version.

Keywords: Location management, Mobile IP (MIP), Home Agent, Foreign Agent.

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Authors: Mohd. Israil, M. Salim Beg

Abstract:

In order to accommodate various multimedia services, next generation wireless networks are characterized by very high transmission bit rates. Thus, in such systems and networks, the received signal is not only limited by noise but - especially with increasing symbols rate often more significantly by the intersymbol interference (ISI) caused by the time dispersive radio channels such as those are used in this work. This paper deals with the study of the performance of detector for high bit rate transmission on some worst case models of frequency selective fading channels for outdoor mobile radio environments. This paper deals with a number of different wireless channels with different power profiles and different number of resolvable paths. All the radio channels generated in this paper are for outdoor vehicular environments with Doppler spread of 100 Hz. A carrier frequency of 1800 MHz is used and all the channels used in this work are such that they are useful for next generation wireless systems. Schemes for mitigation of ISI with adaptive equalizers of different types have been investigated and their performances have been investigated in terms of BER measured as a function of SNR.

Keywords: Mobile channels, Rayleigh Fading, Equalization, NMLD.

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Authors: Kazuo Mori, Takeo Saga, Katsuhiro Naito, Hideo Kobayashi

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The asymmetric trafc between uplink and downlink over recent mobile communication systems has been conspicuous because of providing new communication services. This paper proposes an asymmetric trafc accommodation scheme adopting a multihop cooperative transmission technique for CDMA/FDD cellular networks. The proposed scheme employs the cooperative transmission technique in the already proposed downlink multihop transmissions for the accommodation of the asymmetric trafc, which utilizes the vacant uplink band for the downlink relay transmissions. The proposed scheme reduces the transmission power at the downlink relay transmissions and then suppresses the interference to the uplink communications, and thus, improves the uplink performance. The proposed scheme is evaluated by computer simulation and the results show that it can achieve better throughput performance.

Keywords: asymmetric traffic, cooperative transmissions, multihop transmissions, CDMA, FDD, cellular systems

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Authors: S. Ait Ali Yahia

Abstract:

In Algeria, the most impressive and most known prehistoric art is the painted or engraved rock art which is present with abundance in several regions. The existence of rock art in Great Kabylia region has been known for over sixty years. The main purpose of this research is to show the dangers facing these rock paintings and engravings and what are the arrangements for their protection and recovery. As every vestige destroyed is a part of the world's memory which disappears, some steps have to be taken in order to protect these historical and archaeological heritages.

Keywords: Kabylia, preservation, rock paintings and engravings, valorization.

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Authors: M. G. Tadros, S. M. Ezzat, M. M. Salama, M. A. Farag

Abstract:

In this study, the in vitro anticholinesterase activity of
the volatile oils of both O. basilicum and O. africanum was
investigated and both samples showed significant activity. The major
constituents of the two oils were isolated using several column
chromatographies. Linalool, 1,8-cineol and eugenol were isolated
from the volatile oil of O. basilicum and camphor was isolated from
the volatile oil of O. africanum. The anticholinesterase activities of
the isolated compounds were also evaluated where 1,8-cineol showed
the highest inhibitory activity followed by camphor. To confirm these
activities, learning and memory enhancing effects were tested in
mice. Memory impairment was induced by scopolamine, a
cholinergic muscarinic receptor antagonist. Anti-amnesic effects of
both volatile oils and their terpenoids were investigated by the
passive avoidance task in mice. We also examined their effects on
brain acetylcholinesterase activity. Results showed that scopolamineinduced
cognitive dysfunction was significantly attenuated by
administration of the volatile oils and their terpenoids, eugenol and
camphor, in the passive avoidance task and inhibited brain
acetylcholinesterase activity. These results suggest that O. basilicum
and O. africanum volatile oils can be good candidates for further
studies on Alzheimer’s disease via their acetylcholinesterase
inhibitory actions.

Keywords: Acetylcholinesterase, Ocimum africanum, Ocimum basilicum, passive avoidance.

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Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

Abstract:

Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: Deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator.

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

Abstract:

Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: Breast cancer, health diagnosis, Machine Learning, biomarker classification, Neural Network.

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Authors: Boregowda S.B., Hemanth Kumar A.R. Babu N.V, Puttamadappa C., And H.S Mruthyunjaya

Abstract:

In the past few years, the use of wireless sensor networks (WSNs) potentially increased in applications such as intrusion detection, forest fire detection, disaster management and battle field. Sensor nodes are generally battery operated low cost devices. The key challenge in the design and operation of WSNs is to prolong the network life time by reducing the energy consumption among sensor nodes. Node clustering is one of the most promising techniques for energy conservation. This paper presents a novel clustering algorithm which maximizes the network lifetime by reducing the number of communication among sensor nodes. This approach also includes new distributed cluster formation technique that enables self-organization of large number of nodes, algorithm for maintaining constant number of clusters by prior selection of cluster head and rotating the role of cluster head to evenly distribute the energy load among all sensor nodes.

Keywords: Clustering algorithms, Cluster head, Energy consumption, Sensor nodes, and Wireless sensor networks.

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Authors: Weifeng Pan

Abstract:

The stability of a software system is one of the most important quality attributes affecting the maintenance effort. Many techniques have been proposed to support the analysis of software stability at the architecture, file, and class level of software systems, but little effort has been made for that at the feature (i.e., method and attribute) level. And the assumptions the existing techniques based on always do not meet the practice to a certain degree. Considering that, in this paper, we present a novel metric, Stability of Software (SoS), to measure the stability of object-oriented software systems by software change propagation analysis using a simulation way in software dependency networks at feature level. The approach is evaluated by case studies on eight open source Java programs using different software structures (one employs design patterns versus one does not) for the same object-oriented program. The results of the case studies validate the effectiveness of the proposed metric. The approach has been fully automated by a tool written in Java.

Keywords: Software stability, change propagation, design pattern, software maintenance, object-oriented (OO) software.

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Authors: Hsu-Yung Cheng, Chih-Chang Yu

Abstract:

In this work, we present an automatic vehicle detection system for airborne videos using combined features. We propose a pixel-wise classification method for vehicle detection using Dynamic Bayesian Networks. In spite of performing pixel-wise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. The main novelty of the detection scheme is that the extracted combined features comprise not only pixel-level information but also region-level information. Afterwards, tracking is performed on the detected vehicles. Tracking is performed using efficient Kalman filter with dynamic particle sampling. Experiments were conducted on a wide variety of airborne videos. We do not assume prior information of camera heights, orientation, and target object sizes in the proposed framework. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging dataset.

Keywords: Vehicle Detection, Airborne Video, Tracking, Dynamic Bayesian Networks

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