International Journal of Intelligent Engineering and Systems

11

Authors: Tossapol Kiatcharoenpol, Thanakarn Vichiraprasert

Design of Experiment (DOE) has prevailingly used in various industries from its effectiveness of a statistical technique for managing and improving processes. In this work, the three DOE approaches, Classical DOE or factorial design, Taguchi method and Shainin DOE are applied in a case of plastic injection molding process. A comparative analysis is carried out to express the way of implementation and the merit of each approach based on the accuracy of information and the ease of use. The results show that significant factors obtained from the three DOEs are aligned together. For all approaches, the most three factors influencing to the response, %volume shrinkage, are exactly the same including the optimal process parameters chosen. This is one of an evidence of effectiveness for both Taguchi method and Shainin DOE in practice. The adoption of DOE approaches outlined in this paper and their application to an industrial process will be valuable to practitioners to gain understanding of DOE techniques and to use them fit to their quality issue.

31

Authors: Lukman Hakim, Riyanarto Sarno, Kelly R. Sungkono

Process discovery is a concern in analyzing business processes. Process discovery methods handle many aspects, e.g. a hybrid of non-free choice and invisible task. A hybrid of non-free choice and an invisible task is a condition that needs additions of invisible tasks in non-free choice relationships. Process discovery methods, e.g. Alpha++, Alpha#, Graphical method, Time-based Alpha Miner, cannot discover the hybrid of non-free choice and invisible task, especially invisible tasks in the overlapping pattern. To modeling a hybrid of non-free choice and invisible tasks, this research proposes an algorithm of adding invisible tasks, including invisible tasks of overlapping pattern, automatically in the event log and processing the event log with the rules of Alpha++ algorithm. To test the proposed algorithm, namely Invisible Task Log with Alpha++, the experiment compares it with Alpha++, Alpha#, Graphical Method, Time-based Alpha Miner, Hidden Markov Model-Parallel, and Coupled Hidden Markov Model-Invisible Task. The experiment results proved that Invisible Task Log with Alpha++ algorithm obtains higher precision than the obtained precision by comparison methods.

50

Authors: Rokesh Kumar Yarava, Rajendra Prasad Singh

Cloud storage auditing technique helps to verify the data integrity in the cloud on behalf of the user. Earlier researcher proposed many techniques on the cloud auditing, but it still lags in computational overhead for the resource constraint users. In this research, the Diffie-Hellman method is used to exchange the key in the Third Party Auditor (TPA) to improve the performance of the auditing method. The key is generated in the third party auditor and this is shared with the user as an encrypted key. The Diffie-Hellman creates the shared encryption key between the two parties and can be communicate in the insecure channel. The Diffie-Hellman generates ephemeral keys and this is extremely fast in generating new key pairs. This method is suitable for the big server and have more efficiency over the state-of-art method, which doesn’t involve in ephemeral key generation. The proposed method is evaluated in the simulated environment and compared with the other state-of the art methods. The verification cost of the proposed auditing method has achieved 1.1 s, while the existing method has 1.19s.

68

Authors: Archana Kalidindi, Prasanthi Yavanamandha, Anusha Nagina Kunuku

The recommendation system plays a major role in e-commerce services and online applications such as social-networking, product recommendation, etc. The recommendation system uses the most popular techniques namely Collaborative Filtering (CF) and Deep Learning Neural Network (DLNN) approach. The Cold-Start (CS) problem and the recommendation efficiency are considered as crucial challenges and affected the efficiency of the most popular techniques. In this paper, a Discrete Deep Learning (DDL) based on hashing framework is implemented to achieve Hamming space for mapping Users and Items (UIs). Hamming distance used here to calculate the user’s preference for an item, and the efficiency of online recommendation significantly improved by this computation technique. The performance of DDL framework evaluated by conducting various experiments on the large Netflix rating dataset. The experimental outcomes showed that online recommendation efficiency and CS recommendation accuracy of DDL outperformed the existing methods.

88

Authors: Agus Urip Ari Wibowo, Selo Sulistyo, I Wayan Mustika

Probabilistic Routing Protocols using History of Encounters and Transitivity (PRoPHET) assume that nodes do not move randomly but encounter destinations that have been encountered before. In some original PRoPHET and original PRoPHETv2 testing, they are superior to other routing protocols. Testing in environments that use trains as relay nodes, original PRoPHET performance is worse than the epidemic. This happens because messages delivery to other relay nodes are assumed to encounter the destination (having a larger probability of encountering destination) while the other relay nodes are moving away. The original PRoPHET and the original PRoPHETv2 performance can be improved by delivering a message to relay nodes that will encounter the destination. This can be done by delivering a message to relay nodes with lower probability values encountering the destination. The result obtained is a modified PRoPHET and a modified PRoPHETv2 performance improvement compared to the original PRoPHET and the original PRoPHETv2.

108

Authors: Arna Fariza, Agus Zainal Arifin, Eha Renwi Astuti, Takio Kurita

Tooth component segmentation is a crucial task in computer-aided design for forensic odontology, especially to estimate chronological age. Tooth segmentation on radiographic data is a very challenging task due to noise, low contrast, and uneven illumination. The Fuzzy C-Means clustering is generally used for image segmentation that allow pixels to be classified into one or more clusters according to their membership value. However, this clustering method still has problems associated with the shifting of cluster centers and sensitivity to the overlapping intensity distributions between classes. This paper proposes a modified strategy of the conditional spatial Fuzzy C-Means (csFCM) that incorporates global and spatial information into a weighted membership function by replacing the Euclidean distance with the Gaussian kernel distance to increase insensitivity to noise and outliers. The aim of this paper is to divide the tooth into 3 components, i.e. enamel, dentine, and pulp. Therefore, this modified algorithm is preceded by dental X-ray image pre-processing and continued by combining each dental component clusters into one composite image. The tooth image is pre-processed using Contrast Limited Adequate Histogram Equalization (CLAHE) and gamma adjustment to enhance the dental X-ray images quality from the non-uniform lighting. The Gausian kernel-based conditional spatial Fuzzy C-Means (GK-csFCM) segments the dental image into four class clusters, namely enamel, dentine, pulp, and background. Through iterations, the resulting cluster centers are more convergent with real cluster centers, thus ensuring the proposed method improves the drawback of inherent FCM-based methods and further promoting image segmentation performance. The experimental results on the real dental X-ray images showed that GK-csFCM has better performance than the typical FCM and csFCM clustering algorithms in terms of both qualitative and quantitative metrics, i.e. accuracy, specificity, sensitivity, and precision.

129

Authors: Ahmad Saikhu, Agus Zainal Arifin, Chastine Fatichah

Data relating to everyday phenomena can be recorded in the form of time series. Recently, modeling of time-series has become a topic of interest in data mining. Conducting an analysis of multivariate time series effectively is an essential task for decision-making activities in fields such as meteorology, medicine, and finance. Features selection is a key problem in the analysis of multivariate time series. Rainfall prediction, biomedical classification, pattern recognition, sensor network analysis and so on all have different input features. The problem is that these features have interdependencies and time-delay relationships. Currently, research on the selection of input features of these data still depends on whether they are linear or non-linear. In this paper, we propose a new integration strategy between Pearson Correlation and Symmetrical Uncertainty for relevant feature selection based on linear and non-linear relationships for multivariate time-series classification. We evaluated the goodness of fit of feature subsets using merit value. The meteorological data set was used to test the proposed method. The result showed that the method was able to reduce the number of features by 77.9% features and increase their merit value 2.25 times compared to no input features selection.

148

Authors: Gundugonti Kishore Kumar, Narayanam Balaji, Kotha Srinivasa Reddy, Vemu Thanuja

This paper presents a modified 2D Discrete Wavelet Transform (DWT) architecture with a proposed 16-bit Radix8 Booth multiplier. Existing architecture makes use of Canonic Sign Digit (CSD) representation and when replaced the CSD multiplier with the proposed 16-bit Radix8 Booth multiplier it achieves better performance with small area and low power. In proposed Radix-8 Booth multiplier, the necessary product terms are generated and the remaining terms are truncated. In this method, the n order bit required by the specific coefficient is obtained and the remaining n bits are truncated so that 2n bit output truncated to n bit. The modified 2D DWT architecture is proposed to enhance that it occupies less number of clock cycles, so that it improves in the speed of operation By comparing synthesis results for existing CSD multiplier and the proposed Radix-8 Booth multiplier achieves an improvement of nearly 29.02% Area Delay Product (ADP) and 26.13% Power Delay Product (PDP).

166

Authors: Majeti Venkata Sireesha, Panchala Venkata Naganjaneyulu, Kaparapu Babulu

To depict the hyperspectral data, here a sturdy mixing model is implemented by employing various perfect spectral signatures mixture, which enhances the generally utilized linear mixture model (LMM) by inserting an extra term that describes the potential nonlinear effects (NEs), which are addressed as additive nonlinearities (NLs) those are disseminated without dense. Accompanying the traditional nonnegativity and sum-to-one restraints underlying to the spectral mixing, this proposed model heads to a novel pattern of sturdy nonnegative matrix factorization (S-NMF) with a term named group sparse outlier. The factorization is presented as an issue of optimization which is later dealt by an iterative block-coordinated descent algorithm (IB-CDA) regarding the updates with maximation-minimisation. Moreover, distinctive hyperspectral mixture models also presented by adopting the considerations like NEs, mismodelling effects (MEs) and endmember variability (EV). The extensive simulation analysis by the implementation of proposed models with their estimation approaches tested on both the synthetic and real-time images. Further, it is also shown that the comparative analysis with the conventional approaches.

192

Authors: Sanjive Tyagi, Rakesh Kumar Dwivedi, Ashendra K. Saxena

In this paper, a novel approach of PDF-based text steganography is presented. A covert communication is achieved by embedding secret information at between-characters positions of words within cover PDF text. A stego-encoding technique is being designed to improve the embedding capacity along with sufficiently reduced overheads of stego-cover PDF file. The proposed method constructs multilevel embedding capacities which can be used according to requirements from higher embedding capacity to typical embedding capacity. Encoding and decoding of secret text is being obtained by implementing quadratic probing of hashing technique that builds up a direct encoding table which provides better performance in term of time complexity. Exploratory outcome exhibits that the proposed technique gives productive algorithms in term upgraded security and high payload of concealed data. Experiential verification and examination of the proposed technique with certain prominent text steganographic techniques demonstrate that proposed technique performs better than current methodologies and free of requirements like shading scheme, languages, wordlist, compression and so forth.

214

Authors: Mouna Berquedich, Oualid Kamach, Malek Masmoudi

The management of emergency services within hospitals typically requires an effective manipulation and capitalization of data sources comprising worth information useful for hospitals’ security, safety and efficiency. To meet these requirements, we dispose an agile approach of decision-making that addresses massive crowds occurring at the emergency departments. This approach considers the major constraints that might face these departments, as such lacks of human resources, high costs, and confusedness of patient’s prioritization cases, non-sufficient capacity and logistics. We, basically, inspired from the biological immune defence processes to illustrate a piloting emergency mean known as the artificial immune system (AIS). This system intelligently assists hospital’s decision-makers to enhance their supplying strategies, provides relevant traces from tracking previous information that assist hospital’s staffs that face the massive patient flow, and fosters the execution of efficient solutions. This paper investigates AIS data and relevant traces of overcrowding dilemmas, in which these data could be exploited to increase the reception planning, decrease tension-related anomalies within the emergency department, and supplying hospital chiefs working under stress. The main objective is to present the basic concepts of a new approach that aims to provide hospital decision-makers with relevant traces to assist them in their decision-making in the difficult situations. The expected benefits (main findings) for using the considered approach are not only to provide crisis managers with a relevant computerized decision support system, but also to minimize financial costs, reduce the response time and positively impact the crisis management.

244

Authors: Ahmed Abdelaziz, Nagy Ramadan Darwish, Hesham Ahmed Hefny

The management of agile software projects plays an important role in the success or failure of projects. There are many factors of success or failure that clearly affect agile software projects. There is a big problem in not having a formal study to identify critical failure factors that assess the success or failure of projects accurately. This paper focuses on failure factors of agile software projects. This paper seeks to configure a formal study by using multiple regression analysis for identifying critical failure factors to avoid projects failure, thus saving time and money for the stakeholders. The proposed model is implemented by using R programming. The proposed model also is implemented by two models. The first model based on critical failure factors (CFF) of agile software projects. The second model based the failure factors of the proposed in the initial list (FFIL). The model of CFF outperforms on the model of FFIL in order to accuracy by 10.7% and random error ratio by 4.8%. The results revealed that only 10 out of 19 hypotheses were supported, identifying seven critical failure factors for Agile software development projects: (a) Organizational culture too traditional, (b) Lack of agile logistical arrangements,(c) Lack of project management competence, (d) Lack of team work, (e) Bad customer relationship, (f) defined project scope, and (g) defined project planning.

268

Authors: Dipti Yashodhan Sakhare

Nowadays, Motion Estimation (ME) plays a vital role in traffic signals, movie making, and security purpose to identify the motion of an object. In this paper, Smart and Low power (SLP) based ME is introduced with Level Convertor and Carry SeLect Adder (LC-CSLA-ME). Normal SLP architecture requires more power for performing ME, but in the LC-CSLA-ME architecture, 0.9v supply voltage is used to carry-out the ME. The LC is used to supply the required voltage to the respective sub blocks, which helps to reduce the power consumption of the overall architecture. Instead of using normal adder, CSLA adder is used in prediction and classification unit for improving the hardware utilization. The performance of Application Specified Integrated Chips (ASIC) and Field Programmable Gate Array (FPGA) analysed for both existing and proposed methods. In 180nm technology, LC-CSLA-ME architecture occupied 0.065mm2 area, 2.10mW power, and 104ms delay. The different dataset such as Johnny, Vidyo 1, and Vidyo 4 is analysed for ME. The Coding Time Saving (CTS) evaluated for both existing and LC-CSLA-ME architectures. In the LC-CSLA-ME, average CTS of Operating Power Points (OPP1, OPP2, OPP3, OPP4, and OPP5) are 34.021, 54.316, 67.021, 75.027, and 80.071 that is high compared to the existing methods.

292

Authors: Kavikondala Praveen Kumar Rao, Tamilarasan Senthil Murugan

Mobile Ad-hoc Network (MANET) dynamically designed by the self-organization of the mobile nodes connected through wireless links without using any centralized administration. Energy consumption is the most significant issue in the MANETs, because most of the mobile host’s functions have inadequate battery resources. Reduction in the energy consumption increases the lifetime and throughput of the network. The performance of existing techniques is less in terms of energy conservation issues. To overcome this limits, this paper suggested an energy conservation mechanism incorporated with a pro-active MANET routing scheme. The routing schema is employed in the energy level and the movement of the nodes. Calculation of energy cost is accomplished based on the assessment of the energy consumption level of the node, using K-Means clustering along with AODV (Ad-hoc On Demand Distance Vector). The data packets are secured using (AES), while sending data from source to destination. The proposed methodology “K-Means-AODV-ACO-AES” model is compared with the existing Optimal Key Management for Secure Data Transmission (OKMSDT) and Secured AODV (Sec-AODV). From the comparison result, it is observed that the K-Means-AODV-ACO-AES” increases in Packet Delivery Ratio (5%), along with the decrease in end-to-end-delay (6%), and energy consumption (7%) and drop (8%) in secured environment.

319

Authors: G. R. Sakthidharan, P. Chandra Sekhar Reddy

There has been a tremendous growth in research concern in Wireless Sensor Networks (WSNs), due to the broad range of applications in distinctive areas such as environmental observation, security domains, home automation, etc. Despite advantages, the prevalent adoption of WSN, specifically in remote and isolated regions, has become the major challenge, because of this battery constrained network with limited energy source. As advancement, the nodes are framed under Wireless Rechargeable Sensor Network (WRSN) to enhance the lifetime of sensor nodes with the establishment of charging mechanisms. With those considerations, this paper presents a Schedule-based Optimized Node Recharging Model (SONRM), in accordance to the condition of limited capability of mobile charging. In the process of the construction of charging circuit, the SONRM effectively considers the dynamic changes of energy utilization among nodes at different factors and develops a Recharge Scheduling Algorithm (RSA) that helps in finding the next Energy Starving Sensor Node (ESSN) to be recharged. Simultaneously, a model for the establishment of feasible circuit for recharging has also been done for ensuring the effectiveness of charging. During the operation, the node mortality can be reduced by the proposed model with the incorporation of dynamic charging threshold. When compared with the existing mechanisms, the SONRM attains better results on analysing decisive factors such as network longevity, service time, efficient charging, throughput and recharging time.