International Journal of Intelligent Engineering and Systems

1

Authors: Prashant Digambar Hakim, Vinod Moreshwar Vaze

Electronic Health Record (EHR) is widely adopted by various providers for health care systems such as hospitals, medical practitioners, doctors, etc. and the medical technology involves collecting health information related to diseases, medication, medical images, etc. Personal information such as age, name, gender, billing, and weight details of an individual is highly sensitive data that need to be protected against unauthorized access, hence, the protection of medical data is a big challenge for healthcare systems. The importance of this research work to analyze the sharing of medical data securely without any leakage of patient’s data when the unauthorized user accesses the data. In this current research, Chest X-ray MedPix images from the EHR are used to share medical data through encryption and uploading the data into the cloud. The proposed framework uses SHA256 cryptographic hashing technique as it can enhance the security of a patient’s data. Meanwhile, the attacker is not able to reverse back the key by the process of hashing. The SHA256 has a verifiable key ability for matching and verifying the new passwords against the authentication to improve the security for sensitive data. The proposed SHA256 Verifiable Key (SHA256-VK) executed at a faster rate, as it consumed a lesser ‘average block time’ of 5.48612s when compared to Searchable Encryption-48.125s, Smart-Contract Ethereum Distributed Ledger-14s, Decentralized Security Architecture-Software Defined Networking (SDN)-10s and Elliptic Curve Cryptosystems (ECC) -5.88s.

20

Authors: Monika Rajput, Sanjay Kumar Sharma, Pallavi Khatri

The energy of a wireless sensor network is crucial. Energy optimization is one of the key areas where research is being conducted. The upgraded technology in wireless sensor network (WSN) protocols has been seen tending towards clustering of nodes for energy optimization. Sensor nodes are battery- operated, so the size of the WSN matters mainly in terms of energy and the lifespan of the network. This work proposes a new approach of clustering process for energy efficiency in WSN thereby increasing the network lifetime. The initial clustering uses K-medoid, and setup phase using timer-based execution of adaptive neuro-fuzzy inference system (ANFIS). The ANFIS neuron structure is optimized using the firefly algorithm. The timer-based call of ANFIS for re clustering process is the novel approach and shows optimum efficiency in energy consumption. The optimum choice of cluster head (CH) selection leads to uniform energy consumption amongst all the sensor nodes and avoids energy run out problem when CH charge is given to particular node the performance evaluation shows improvement in energy efficiency and lifetime of the network. This work shows that the lifetime of the network is enhanced by 4% as compared to existing methods.

40

Authors: Madhu Mallikarjuna Nayak, Sumithra Devi Kengeri Anjanappa

Magnetic Resonance Image (MRI) uses an automated system to detect the presence of tumors in brain tumor images. Many existing methods performed the classification based on machine learning approaches that include supervised and unsupervised machine learning approaches. There are various researches undergone with more efforts and many types of research related to brain tumor were examined using the small dataset. The existing models performed segmentation techniques that extracted irrelevant features showed over-fitting problems. Similarly, the segmentation process uses clustering approaches that identify the brain tumor at an early stage, which was a challenging task. Therefore, a Convolution Neural Network with Global Average Pooling (CNN-GAP) model is designed to automatically segment the tumor regions and from the segmented regions, the relevant features are extracted to overcome the problem of overfitting. The proposed CNN-GAP classifies the MRI data into Benign I and II grade or Malignant III or IV grade. The proposed CNN-GAP method showed a 2 % of improvement in accuracy when compared to the existing CNN technique.

66

Authors: Muhammad Ainul Yaqin, Riyanarto Sarno, Siti Rochimah

Business process models can represent course schedules. Every business process model has complexity. The greater the number of courses and the number of study groups, the more complex the business process model. The rate of increase in the complexity of the business process model is referred to as growth. The growth in the business process model complexity helps estimate business processes resource requirements at a certain complexity level. Therefore the equation for the growth of the complexity of the business process model needs to be formulated. The equation for the growth in the business process model complexity is obtained through several stages, namely developing a course schedule using the aSc TimeTables software. The order of the scheduling results is formatted as an event log. The event log is used as material in the mining process using the inductive mining algorithm to get a business process model represented using the Business Process Modeling Notation (BPMN). The complexity of the business process model is calculated using the Yaqin complexity formula. The equation for the growth function for the complexity of the business process model as a function of the number of study groups is obtained through logistic regression. Logistic regression is based on a logistic equation with 3 parameters, 4 parameters, and 5 parameters. Based on the three types of logistic equations regression results, we find that the logistic equation with 5 parameters has the highest average value of R2, namely 0.9698. Meanwhile, the equations for the growth of the business process model complexity are a function of the number of courses through power regression. The equations obtained through power regression have an average value of R2 = 0.9549. In this study, regression is carried out separately between the growth function as the number of study groups with the growth function as a function of the number of courses. This research novelty lies in using the number of study groups and the number of courses to measure the growth in the business process model complexity.

95

Authors: Wayan Firdaus Mahmudy, Adyan Nur Alfiyatin, Candra Fajri Ananda, Agus Wahyu Widodo

Inflation is an important tool to assess the current condition of a nation economy. Uncontrolled inflation rate may have a lot of negative impacts on economic development. Forecasting can be used to control the inflation by making appropriate economic policies. However, uncertainty pattern of the inflation rate may make it hard to forecast. This study proposes an extreme learning machine (ELM) for the inflation rate forecasting. As part of the machine learning algorithm, the ELM has an ability to address uncertainty in data input pattern. However, ELM had weakness in determining initial weights that may produce inaccurate results. So, we propose particle swarm optimization (PSO) to determine good initial weights for the ELM. PSO is a metaheuristic method that gives good results in local searches but requires longer computation time to locate its particles on the global optimum point in the vast search space area. To overcome this problem, auto-speed acceleration algorithm is employed to drive particles of the PSO in searching of the global optimum with lower computation time. The performance of the proposed approach is evaluated using root mean square error (RMSE). A series of computational experiments prove that the proposed approach achieves better results with the average RMSE of 0.01926. This result is better than RMSE of 0.02020 achieved by the original version of ELM.

115

Authors: Mallikarjuna Anantapur, Venkanagouda Chanabasavanagouda Patil

Mobile Ad hoc Network (MANET) is an infrastructure-less wireless network that is characterized by multi-hop communication, dynamic network topology, open medium, and so on. MANET is generally vulnerable to security threats because of its decentralized control architecture. Therefore, the malicious attack is required to be avoided in the routing path for improving the network efficiency. In this paper, the Ant Colony Optimization (ACO) based Modified Ad-hoc On-Demand Distance Vector (MAODV) is proposed to obtain secure data transmission over the MANET. The ACO considers the four fitness functions such as trust, residual energy, distance, and node degree to detect an optimal path under the constraints of blackhole attacks. The main objective of the MAODV-ACO method is to improve the Packet Delivery Ratio (PDR) while minimizing the delay. The performance of the MAODV-ACO method is analyzed in terms of PDR, throughput, average End to End Delay (EED) and overhead. This proposed MAODV-ACO method is compared with Context-Aware Routing Protocol (CARP) and, Ad-hoc On-Demand Distance Vector (AODV) using Bayesian Approach and Dempster Shafer Theory (BA-DST) to evaluate the network performances. The PDR of the MAODV-ACO method is 99.66 % for 100 nodes, which is high when compared to the CARP and AODV-BA-DST.

137

Authors: Divya Jain, Sanjaeev Narayan Sharma, Alok Jain

Digital differentiation is an important phenomenon in digital signal processing to finding out sudden changes in signal. In this paper design of digital differentiator based on Fourier series expansion with quadratic fall in response has been presented. Error minimization criterion is applied to obtain minimum error in band of interest and to optimize the designed digital differentiator. Differentiator produces noise after differentiation and differentiated signal become noisy therefore guided filtering has been detailed to further smoothen the differentiated signal. Criterion for sampling frequency has been evaluated for particular cut-off frequency so that efficient selection of sampling frequency for Electrocardiogram (ECG) signal may possible. The results are demonstrated by applying designed differentiator on ECG signal and it has been found that the response of smoothened digital differentiator is equivalent to ideal differentiator. In this work sensitivity and error rate are 99.97 percent, and 0.069 percent, respectively.

159

Authors: Madhavi Devi Lanka, Subbarao Chalasani

A low profile and highly efficient M-Shaped monopole antenna has been designed and its analysis is presented in this work. The designed model can work in the sub 6 GHz band with coverage in the communication applications like Bluetooth(2.4 GHz), Long Term Evolution LTE (2.45 GH), Industrial Scientific and Medical Band ISM(2.5 GHz), Wireless Fidelity Wi-Fi (3.1 GHz) and Wireless Local Area Network WLAN (5.5 GHz) respectively. It has been built on commercially available FR4 material with dimensions of 37X26X1.6 mm. Electromagnetic simulation-based modelling with optimization has been done and presented in this work. An average gain of 3.8 dB, average directivity of 3.2 dB and attained average efficiency reading of 82% from the current design. The results obtained in real time measurement with respect to reflection coefficient, gain, radiation characteristics and Voltage Standing Wave Ratio VSWR are matching with the results obtained in the simulation.

177

Authors: Hana H. kareem, Rana T. Saihood

Enhancing aerial images plays an important role in many remote sensing applications; aerial images are often affected by dust, smoke, and fog. In this study, a new method was proposed to enhance aerial images. The proposed method is enhanced the light value (Y) in the YCbCr color transform by using the Sigmoid function mapping and Adaptive Histogram Equalization techniques, While the chromatic compounds (CbCr) are enhanced by Dark Channel Prior (DCP). To know the efficiency of enhancement, the no-reference quality measures as Entropy and Average Gradient (AG) have been calculated, which are used to see the quality of the proposed method in comparison with Image Entropy and Information Fidelity (IEIF), Multiscale Retinex with color restoration (MSRCR), Color Enhancement Scene Estimation (CESE), traditional (DCP), and Histogram Equalization (HE). From the results, it is found that the proposed method has succeeded in enhancing the aerial images better than other methods. Where it had high values for the rate for Entropy (7.73) and AG (68.75).

198

Authors: Dimas Aryo Anggoro, Salsa Sasmita Mukti

The kidneys have an essential role in the body; if it does not work properly, it will cause disease, one of which is chronic kidney failure (CRF). Therefore, a machine learning algorithm is needed to help predict CRF, one of which is the Extreme Gradient Boosting (XGBoost) algorithm. However, XGBoost has one thing that must be considered, namely the presence of hyperparameters that need to be tuned. In this research, tuning was done using grid search and random search methods, which would be compared to get a good performance and help predict GGK. In the dataset used, several features were incomplete, so that data cleaning was carried out. Based on the unbalanced amount of data, oversampling with SMOTE and random oversampling methods were needed to balance the data. Next, the data were normalized using two: min-max and z-score normalization. After the data had been normalized, a splitting process was performed with a division of 70% for training data and 30% for testing data. Furthermore, the hyperparameter was tuned to obtain the optimal parameter value, which was then performed data processing and evaluated to obtain the accuracy and f-measure values of the tuned parameter values. In the grid search method with data performed with min-max and z-score normalization combined with oversampling random oversampling, the best results were obtained with an accuracy value of 99.28% and an f-measure value of 0.9942. Comparison with other methods was carried out and the proposed method obtained an accuracy of 99.33% and f-measure 1.0.

220

Authors: Seyed Alireza Bashiri Mosavi, Mohsen Javaherian, Mohammad Sadeghi, Halime Miraghaei

The increases in releasing high-dimensional radio galaxy images necessitate addressing automatic methods for compacting the curse of dimensionality. Hence, in this paper, the modularized filter-wrapper feature selection scheme (MFWFS) is exploited to select the most discriminative features (MDFs) of the galaxy images. First, we employed the Fanaroff–Riley (FR) radio galaxy images in the data-gathering phase. Next, we applied the MFWFS scheme to 528 moments of radio images for selecting MDFs in dual-phases. The preliminary optimal features (POFs) are selected in the filter phase concerning the triple information theory criteria. In the wrapper phase, the obtained POFs are fed to the twin support vector machine (TWSVM) classifier to extract MDFs. Finally, the quad-final MDFs are introduced to the experimental comparison strategy through the cross-validation technique. Our results show that the best-laid MDFs in a subset of 96 features have accuracy close to 80% with a dimension reduction rate of 5.5.

249

Authors: Pooja Manghnani, Asmita Moghe

Eye is a precious part of human body. But in the world, glaucoma has affected the human eye as second main cause of blindness. This disease grows very slowly in the eye and without noticing it destroyed the optic verves within the eye. Traditional glaucoma detection methods are costly and time consuming. Therefore, better methods are required to detect glaucoma more accurately. Hence, a new adaptive approach for glaucoma detection using bi-dimensional empirical mode decomposition (BDEMD) from retinal images is proposed. Adaptive bi-dimensional intrinsic mode functions (BDIMFs) are obtained using very simple steps, from pre-processed and coloured decomposed images, is the main idea of the proposed method. Moment and texture features contain more information hence these are extracted from decomposed BDIMFs. These features are then normalized and classified by support vector machine (SVM) with its different kernel functions. The achieved results like, accuracy, sensitivity, and specificity are 97.02 %, 98.23 %, 95.46 %, respectively for 10-fold cross validation. Experimental analysis shows that our method outperformed over the traditional methods for glaucoma detection.

274

Authors: Achraf Boumhidi, El Habib Nfaoui

Advanced reputation generation systems analyze user-generated content such as opinions and reviews expressed in natural language in order to produce a reliable and trusted reputation value. According to recent and relevant literature, while many important attributes are exploited such as semantic and sentiment orientation of the reviews, time, and opinions’ relevancy, other relevant attributes such as users’ credibility, as well as the sentiment intensity of the reviews, are not considered. In this paper, we propose a system that computes a single numerical reputation value between 0 and 10 from Twitter microblogging platform by incorporating the sentiment orientation of the tweets, the sentiment intensity of the positive tweets, as well as the users' and tweets' credibility score. To assess the effectiveness of our system we have compared its computed reputation value with the ground truth one ranging from 0 to 10. This later is the weighted average votes of thousands of users taken from IMDb, Amazon, TripAdvisor, and Yelp concerning respectively four products and services (movie: 7.9, phone product: 7.3, hotel: 9, restaurant: 7.1). The experimental results conducted on four real-world Twitter datasets related to the aforementioned products show that our system provides a reputation value (movie: 7.66, phone product: 7.18, hotel: 8.71, restaurant: 7.08) that is near to the ground truth one. Consequently, it can be applied in practice and used by consumers and businesses to generate a reliable and trusted reputation value from tweets in order to support them during their decision-making process in E-commerce platforms (buying, renting, booking, etc.).

301

Authors: Asha Kathigi, Krishnappa Honnamachanahalli Kariputtaiah

In recent times, identifying Kannada, Arabic and English handwritten characters is a challenging task in pattern recognition application. The low resolution, complex backgrounds, text orientation, text size, and the variations in the writing styles makes character recognition as a challenging task. To address the above stated issues, a new automated character recognition model is introduced in this paper. Firstly, skewed line segmentation technique is applied to the handwritten character for dissecting the document images into line elements and then split into phrases and single characters. Next, AlexNet model is used for extracting the deep features from the individual characters, where the extracted feature vectors are multi-dimensional in nature that increases the system complexity. So, an unsupervised feature selection algorithm is proposed to select the active feature vectors that are fed to multi support vector machine classifier for individual character classification such as 64 classes in English language, 10 classes in Arabic language, and 657 classes in Kannada language. Experimental analysis showed that the proposed model obtained 85.80%, and 95.55% of accuracy on the chars74K dataset for Kannada, and English characters, respectively. In addition, the proposed model obtained 71.79%, and 99.97% of recognition accuracy in Kannada and Arabic handwritten character recognition on a real time dataset and MADbase digits dataset. The obtained results are better compared to the existing deep learning models; DIGI-Net, feed forward neural network, and context aware model.

321

Authors: Shahbaa I. Khaleel

Since digital images have become an essential part in information systems and many other scientific applications, and due to the importance of some of these images in terms of the importance of the information they contain, there have been some problems in terms of the inability to determine the object in the image accurately or directly from the raw image. Because of the occurrence of errors affecting the process of transferring or taking these pictures, or there is a percentage of lack of clarity that leads to the loss of a part of the information, and this has led to the development and use of algorithms and methods that facilitate the process of detecting the edges of objects in the image and working to determine the spatial characteristics that are difficult to distinguish in sometimes. In this research, edge detection techniques and methods that depend on computational intelligence, and which are based in their work on biological content inspired by nature are presented. Two swarm intelligence methods represented by the beetle optimization method BSO and the butterfly optimization method BFM for conducting the edge detection process were presented in the images. In addition to developing a powerful edge detector based on the beetle and butterfly algorithms by improving the performance of the butterfly algorithm by choosing optimal values for its parameters that play an important role in finding the optimal solution. This was done by hybridizing it with the beetle algorithm that provides the butterfly algorithm with the best values for its parameters. The hybridization process was done in the first stage of the butterfly method, which is the initialization stage, this method called Beetle Butterfly Method BSO-BFM. All edge images that obtained using different swarm intelligence techniques of proposed system were compared by the reference edge image (ground truth) to ensure the performance, and accuracy of all methods used in the research, using mean square error MSE and root mean square error RMSE measurements as well as peak signal to noise ratio PSNR. From the results it turns out that the proposed hybrid method BSO-BFM is the best of all, this was evident through the values of the measures used here, as this method had the best values for the MSE measure, where its values ranged between 0.023375 - 0.466766, and the RMSE measure, where the best values were obtained in this method, which is between 0.152889 - 0.216048, and also for the PSNR measure, which ranges between 61.4398 - 64.443273.

344

Authors: Zahraa A. Ghazi, Kasim K. Abdalla

Multiple-input multiple-output (MIMO) systems are used in most modern wireless communication systems, including WiMAX, DVB-NGH, WiFi, HSPA+, LTE, and 4G. MIMO systems with space time coding are a potential technique for increasing data rates and improving wireless communications reliability.This work proposed a new technology for decoding Multiple-Input Multiple-Output Space–Time Block Code (MIMO-STBC) by using blind source separation (BSS), to enhance the BER performance for MIMO STBC channel estimator by using semi-blind independent component analysis (ICA) method, The kurtosis-based source extraction technique is achieved by using real imaginary decomposition (R-Im) of maximum ratio combiner (MRC), Finally, by applying the water cycle method in optimization, we were able to increase the work speed of channel estimation using the ICA technique. The MATLAB software was used to model the system's performance. The simulation results demonstrate that the proposed channel estimation technique is feasible and effective, with only a small difference in performance between the estimated and known channel parameters, such that the proposed algorithm using WCA-kurtosis is better than the performance of the LS algorithm for transmit and receive antennas 2×1, 2×2, 4×2, and 4×4 by 1.6, 1.5, 1.2, and 2.2 dB, respectively, at the frame length of the 1024.

369

Authors: Sakon Klongboonjit, Tossapol Kiatcharoenpol

Wind energy potential is the main data to making decision for green energy projects in resident areas. The decision makers generally prioritize wind energy of alternative areas with data of wind power that are hard collected. In this study, the AHP method is introduced to prioritize wind energy of alternative areas with data of average wind velocity generally receiving from the secondary data sources. With the example of 13 alternative areas in the south of Thailand, the data of average wind velocity can be used to construct the AHP pairwise comparison matrix. Finally, the priority values of these 13 alternative areas are calculated with C.R. = 0.1157 and the wind energy priority of alternative areas is prioritized. The priority results with the data of average wind velocity are really similar to the priority results with the data of wind power.

391

Authors: Ujjwala S. Rawandale, Mahesh T Kolte

The hearing aid system is used for hearing impair patient communication, but the hearing aid's main issues are improper communication, power dissipation, delay, matching error, and low complexity. Many existing filter bank techniques are developed to overcome these issues but still have the problem of delay, unwanted noise, etc. This paper has designed a Tuned Feed forward Variable Bandwidth Filter (TFVBF) to enhance the Hearing Aid System (HAS) by reducing unwanted noise, matching error, and complexity. Moreover, the developed technique is implemented in MATLAB and it achieve low rate in power consumption and delay. Thus, the gained performance metrics has enhanced the HAS's performance by reducing, unwanted noise, matching error, and complexity. Finally, the attained performance metrics of the developed technique using 50 bands are compared with existing replicas in terms of matching error, delay, power consumption, and complexity for checking the effectiveness of developed replica.

417

Authors: Pharida Jeebklum, Phumin Kirawanich, Chaiyut Sumpavakup

The dynamic wireless power transfer system consists of multiple power transmission sectors in parallel with a single inverter circuit for light duty electric vehicles. The use of multiple power transmission sectors reduces power loss. Usually, each power transmission sector needs to use an inverter for every sector but this system uses one inverter to save cost. This article proposed a study of the dynamic wireless power transfer efficiency between the power transmission sector and the power receiver sector. The performance of the proposed dynamic wireless power transfer system was experimented and compared by varying the gap between the power transmission sector and the power receiver sector, and the speed of the electric vehicle at a resonance frequency. The results show that the dynamic wireless power transfer system had a maximum power transfer efficiency of 82.01% at a distance between the power transmission sector and the power receiver sector of 0.10 m, with a speed of 0.28 m/s.

442

Authors: Yasameen Sajid Razooqi, Muntasir Al-Asfoor

Routing is one of the most critical issues in Wireless Sensor Networks (WSN) as it differs from routing in classical networks. The dynamic nature of WSN environment and the constrained limited resources availability have made routing process a real challenge to industry and academia. One of the best solutions in this routing case is a heuristic search to find the best and optimal path. Thus, many heuristic optimization algorithms are used to enhance routing performance in WSNs. Ant Colony Optimization (ACO) is a well-known heuristic algorithm which used in WSNs, as it showed an admirable performance improvement routing context. In this paper, two routing protocols are proposed: modified Ant Colony Optimization for flat routing protocol (MACOF), and the second is Naïve Baye’s with ranking for flat routing protocol (NBRF). We have used inverse of the distance between the node and it’s neighbours in the probability equations of ACO and Naïve Baye’s formulas, along with pheromone and residual energy. Those formulas have been used in choosing the candidate between neighbors for the next hop that will lead to consume less average energy of all nodes in each round. In addition, in the first protocol we have adjusted ACO’s parameters values after several experiments, and in the second protocol we have proposed ranking for network training before start working. The experimental analysis has been done using varied scenarios of networks topologies. Furthermore, the experimental results have been compared and contrasted against the well-known ACO approaches and IOP routing protocols. The performance of the proposed methods of MACOF and NBRF have been shown in terms of throughput, energy consumption, network lifetime, the size of data transferred over the network, and the length of paths travelled by the packets.

464

Authors: Purba Daru Kusuma, Abduh Sayid Albana

Intermediate storage is one important aspect of parallel permutation flow-shop scheduling (PPFSP). Unfortunately, research about optimizing intermediate storage is rare. Most existing studies were conducted to minimize or optimize the maximum completion time under various circumstances and constraints. Thus, this work aims to minimize intermediate storage with a tolerable maximum completion time level. In this work, the proposed model was developed by using an evolutionary algorithm (EA). This model consists of two steps. In the first step, the EA is used to determine the tolerable maximum completion time. In the second step, the EA is used to make a solution with minimum intermediate storage and a tolerable maximum completion time. This proposed model is then compared with the existing non-dominated sorting genetic algorithm (NSGA II), improved efficient genetic algorithm (IEGA), cloud-theory based simulated annealing (CTSA), and artificial bee colony algorithm (ABC). Based on the simulation results, the proposed model performs better than the IEGA, CTSA, and ABC models. Meanwhile, the proposed model is competitive enough compared with the NSGA II. And, the two-step evolutionary algorithm (TSEA) produces 26 percent higher maximum intermediate storage, and the improved two-step evolutionary algorithm (ITSEA) produces 7 percent higher maximum intermediate storage than the NSGA II. The ITSEA produces 16 lower maximum intermediate storage than the TSEA. Both TSEA and ITSEA produce a 21 percent higher maximum completion time than the NSGA II.

488

Authors: Nandeeswar Sampigehalli Basavaraju, Shanmugarathinam Ganesarathinam

In recent decades, the automatic recognition of diabetic retinopathy is an emerging research topic, where diabetic retinopathy is the major cause of vision loss in human beings. The early recognition of diabetic retinopathy lesions like the optic disc, cotton wools, hemorrhages, exudates, and Microaneurysms reduces the growth rate of diabetic retinopathy disease and assists clinicians in early treatment. However, the monitoring, and recognition of diabetic retinopathy lesions are tiring, cyclic and error-prone processes. To overcome these problems, a new automated diabetic retinopathy detection model is proposed in this paper. Firstly, the retinal fundus images are acquired from Kaggle retinal dataset and DiaRetDB1 dataset, and the acquired raw fundus image quality is improved using histogram equalization technique, Haar wavelet transform, discrete wavelet transform and Gaussian matched kernel filtering method. Further, the k-means clustering algorithm and Gaussian blur technique are applied for fovea and blood vessel removal, and to highlight the optic disc regions. Next, color histogram features are extracted from the segmented regions, and the obtained features are fed to an ensemble classifier for classifying the healthy, and DR subjects. The simulation results show that the proposed k-means based ensemble model achieved 93.29% and 99.37% of accuracy on the Kaggle retinal dataset and DiaRetDB1 dataset, which are better compared to the existing state of the art models.

507

Authors: Srikanth Parikibandla, Sreenivas Alluri

Nowadays, the Lightweight cryptography algorithm is mostly used for security level purposes, because it’s designed with the help of basic arithmetic logic blocks. The basic blocks are perfectly placed in respective modules to process the encryption with effective manner. The fundamental shifting based PRESENT algorithms are frequently used to help the hackers to retrieve the data. To overcome this issue, Separation With NOT (SWN) with Srikanth Parikibandla box (SP-box) architecture is used in this research work. Due to the usage of modified Register Transfer Level (RTL) structure, the process and the confidentiality of the PRESENT encryption architecture is improved in a significant manner. The Modified Single Coupled Linear Congruential Generator (MS-CLCG) architecture is used to generate the random number, which act as a key for encryption as well as decryption. For every clock cycle, MS-CLCG architecture generates the random value to perform the encryption. The SWN-SP architecture occupied 56 counts of LUTs, 33 count of Flip-flops and 30 counts of slices, which are less compared to the existing SRS-PRESENT architecture.

528

Authors: Thanh Long Duong, Ngoc Anh Nguyen, Thuan Thanh Nguyen

Optimal power flow (OPF) is an important problem in the power system operation. The purpose of the OPF problem is to optimize a defined objective function by modifying variables of control such as real power at generator buses except slack bus generator, voltages at all generator buses, reactive power of compensators and tap ratio of transformers while all constraints are satisfied. This paper is proposed an approach based on the artificial ecosystem optimization (AEO) to solve problem of optimal power flow. The suggested algorithm is tested on the IEEE-30 bus systems with five target functions consisting of fuel cost, emission, power loss, voltage deviations and L_index. The results obtained of the suggested AEO approach compared with equilibrium optimizer (EO), particle swarm optimization (PSO), sunflower optimization (SFO), genetic algorithm (GA) and other exiting methods. The results simulation shows that, standard deviation obtained value after 50 independent runs by the proposed AEO algorithm is better compared with EO, PSO, SFO and GA method. The fuel cost, emissions, active power loss and voltage deviations levels are reduced by 11.21%, 44.06%, 46.44%, and 92.13% respectively, compared to the initial case. Furthermore, for other exiting methods the improvement level percentage (IL) of the proposed AEO algorithm can be up to 0.2285 % for fuel cost objective, 0.137% for emission objective, 7.618% for total power loss objective, 89.85% for voltage deviation objective and 0.652% for L_index objective. Thus, the proposed AEO method is also one of effective and reliable algorithms for handling OPF problem.

554

Authors: Robert Marco, Sakinah Sharifah Syed Ahmad, Sabrina Ahmad

In the subject of software effort estimation, missing data is a significant issue that leads to information loss and bias in data analysis. The majority of data preprocessing procedures are simple reuse approaches built for numerical data, which presents a problem when missing data and irrelevant features are associated with categorical variables. The purpose of this paper is to evaluate and compare the performance of the proposed technique with the k-nearest neighbor imputation (kNNI) technique, random forest imputation, and multiple imputation by chained equations in terms of error and accuracy in the ISBSG dataset when missing data is present. This study relied on five machine learning approaches as its foundation with hyperparameter tuning using grid search. The results show that the three imputation methods have almost the same performance. However, the combination of kNNI, Genetic feature selection, and classification and regression tree (CART) yielded better results than other combinations of methods with MAE (0.015), RMSE (0.037) and R-squared (0.804) values.

579

Authors: Dwiki Kurnia, Istikmal, Ida Wahidah

Delay in the use of the 10 Gigabit Passive Optical Network (XG-PON) on the Centralized RAN (C-RAN) is known to be not very good since a small maximum capacity of Remote Radio Heads (RRH) used, only 8 units. Dynamic Bandwidth Allocation (DBA) is required on upstream PON to dynamically allocate bandwidth. Conventional DBA on XG-PON produces an upstream delay which is 0.6 ms at least. In fact, C-RAN has strict delay requirements, namely 0.3 ms, which is why a DBA with low delay is needed as a solution. In this study, a modified Round Robin (RR) DBA algorithm using NG-PON2 is proposed with consideration of delay performance in upstream by utilizing excess bandwidth and using forecasting methods. Modified RR DBA has the best upstream delay among other DBAs that can be applied to C-RAN using a maximum of 16 Optical Network Unit (ONU) with the result of 0.261 ms.

596

Authors: Sowmyashree Malligehalli Shivakumaraswamy, Chikkanayakanahalli sadashivaiah Mala

In the previous few years, innovations in the Wireless Sensor Network (WSNs) domain have acquired momentum because of WSN’s various applications in farming, mechanical assembling, military observation, ecological checking, clinical and medical care, debacle recuperation tasks and so forth. In WSN, the wireless sensor nodes are organized randomly to communicate with other nodes inside the network. Since the nodes are battery-powered, energy is one of the major constraints in WSN. Moreover, the nodes are affected by malicious attacks (ex. black hole attack) which leads to security issues in the network. In this paper, the Cost Centric Cuckoo Search Algorithm (CCCSA) is proposed to overcome the energy and security issues in WSN. Here, the clustering operation is performed using the K-means clustering algorithm followed by CCCSA is used to select the adaptive Cluster Head (CH) between the normal nodes in the cluster. Further, the CCCSA is integrated with Ad hoc On-Demand Distance Vector (AODV) routing protocol to generate the adaptive routing path. In CCCSA, the trust value is considered as primary cost value for accomplishing a secure communication in WSN. The Packet delivery rate of the CCCSA method is 95.91% and it is higher when compared to the existing Media access control Centralized Routing Protocol (MCRP), Secure Routing Protocol based on Multi-objective Ant-colony-optimization (SRPMA) method and Realisable Secure Aware Routing (RSAR) protocol.

616

Authors: Yulianto Triwahyuadi Polly, Sri Hartati, Suprapto, Bambang Sumiarto

Pigs have become an essential part of the cultural and economic life of the people in Nusa Tenggara Timur (NTT) Province. Diseases in pigs significantly affect the success of pig farming. Identification of disease in pigs is a classification problem. Metaheuristic algorithms are widely used in Neural Network (NN) optimization to solve classification problems. Flower Pollination Algorithm (FPA) is grouped into a metaheuristic algorithm that has been commonly used in optimization cases in the real world. To improve FPA performance, this study proposes replacing the FPA step vector parameter, namely Levy distribution, with Newton Polynomial Quadratic Interpolation (NPQI), known as Quadratic Interpolation Flower Pollination (QIFP). Quadratic Interpolation Flower Pollination Neural Network (QIFPNN), Flower Pollination Neural Network (FPNN), Bat Neural Network (BANN), and Particle Swarm Optimization Neural Network (PSONN) algorithms were used to train NN in real cases of disease identification in pigs, covering 11 diseases with 68 clinical symptoms. The results showed that the proposed algorithm, namely QIFPNN, outperformed FPNN, BANN, and PSONN in classification accuracy. QIFPNN is also able to improve classification accuracy and speed of convergence when compared to FPNN. QIFPNN and FPNN, respectively, provide 82.6159 % and 67.4766 % accuracy, and the training time is 6056.240 seconds and 6555.179 seconds. QIFPNN accuracy increased by 22.40%, and training time was 7.61 % faster. It concluded that QIFPNN could be used as a complementary model in disease identification in pigs.

642

Authors: Raghad M. Eid, Eman k. Elsayed, Fatma T. Ghanam

More than seventy million protein sequences exist now, with just around 1% of their functions known. Multi-domain protein prediction is one of the problems in the Bioinformatics field, which was conducted to find the function of proteins. As a result, the researchers attempt to develop algorithms that predict protein functions based on their sequences. We might think of the tremendous rise in sequencing and structural genomics as providing us with a lot of data to expose the complicated sequence, structure, and functional correlations that exist in proteins. However, because of the critical functions that these macromolecules perform in biological mechanisms, acquiring a thorough understanding of their activity has recently emerged as a major challenge. Furthermore, multi-domain protein function prediction approaches are more efficient than methods that are fully processed based on protein sequencing. In this research, we used a model for predicting multi-domain protein function using a Fuzzy Convolutional Neural Network. In this research, we used a novel hybrid CNN and Fuzzy for sequence labelling, specifically to predict the function of the protein. Our hybrid model managed to outperform recent previous studies in terms of accuracy (95.02%) and performance on the UniProtKB dataset, and they showed significant improvements, in the execution time spent in the prediction process. It's crucial to keep your attention on this strategy. Further, study will be needed.

666

Authors: Dinu Calin Secui, Gabriel Bendea, Monica Liana Secui, Cristina Hora, Codruta Bendea

Economic dispatch problem is found in the recent focus of specialists, solving this problem being based especially on metaheuristic algorithms. Social Group Optimization (SGO) is a new metaheuristic algorithm that models human behavior to solve a certain problem, and it has been successfully applied to optimize mathematical functions. This paper investigates the behavior of the SGO algorithm equipped with five chaotic maps (Logistic, Double, Iterative, Singer and Cat), applied to solve the economic dispatch problem with multiple fuel sources. The new algorithm, called Chaotic Social Group Optimization (CSGO), results from including the sequences generated by chaotic maps in the heuristics specific to the SGO algorithm. The performance of the CSGO chaotic algorithm is tested on four systems having various dimensions (10-units, 20-units, 30-units, and 40-units) and characteristics. The SGO and CSGO algorithms are compared with each other and with other algorithms. The obtained results indicate that CSGO algorithms equipped with any of the chaotic maps mentioned has a higher average performance than SGO algorithm. The application of CSGO, compared to SGO, leads to a reduction of the fuel cost between (0.6-6.7) $/h for the analyzed systems.