International Journal of Advanced Intelligence Paradigms (13 papers in press)

Regular Issues

• Big data secure storing in cloud and privacy preserving mechanism for outsourced cloud data  
  by Dr B. Renuka 
  Abstract: Big data is a buzz word in this decade it gets tremendous concentration in these days by the researchers because of the characteristics and features. And also big data gives lot of challenges to the world that is storage, processing and security. In any technology security is the prime concern in this manuscript, we map to misuse new complications of enormous information regarding security, further more, confer our thought toward viable and insurance protecting enlisting in the immense data time. Specifically, we at first formalize the general building of gigantic data examination, recognize the relating security necessities, and present a capable and assurance sparing outline for immense data which is secured in cloud.
  Keywords: Privacy Preserving; Security; Big data; Cloud Computing; outsourced data.
  
  
• DEVELOPMENT OF ELECTRONIC PEST CONTROL IN PADDY FIELDS  
  by Ram Chandran.M, Vishnu Priya.R. 
  Abstract: Worldwide, agriculture is considered as the backbone of a countrys economy. The contribution of agriculture to Indias economy is steadily declining with Indias economic growth. Still, agriculture plays a significant role in the broadest economic sector. The major objective of the present work is to repel the pests that affect the agricultural fields especially paddy field. The pests that mainly affect the paddy fields are small insects, grasshoppers, and moths. Initially, we capture the images of the field and process these images using MATLAB software where we developed codes that find out whether the small insects are high or low. If the insects are high, then an appropriate pesticide is sprinkled over the field which is controlled by a PIC16F877A microcontroller. Further, we collected the frequency sensitivities of grasshoppers and moths to repel them. The time period of the day at which the grasshoppers and moths are maximum is obtained from previously published paper. Using this information we generate these frequencies at appropriate timings. As a result, the paddy field is free from grasshoppers and moths.
  Keywords: Frequency bandwidth; grasshoppers; image processing; moths; and pest control.
  
  
• Machine Learning Approach to Predict Purchase Decision of Bank Products and Services  
  by Saumya Chaturvedi, Vimal Mishra 
  Abstract: We propose a machine learning approach to predict purchase decision of bank products and services. The data were collected from May 2008 to May 2014 of a Portuguese bank. This investigation will help to predict the business of the bank and financial inflation and recent trends in bank product and services. The investigation is focused on the classification and prediction of bank telemarketing calls for term deposit product. We have analyzed a large data set of 41188 observations related with bank client, product, services and socioeconomic attributes. Initially, the dataset was having 150 features and we have selected 21 most relevant features using standard adaptive forward selection and intelligence quotient. We have also compared four machine learning approaches Conditional Inference trees(Ctree), recursive partitioning (Rpart), Support Vector Machines(SVM) and RandomForest. The paper contains an impact analysis of changing training data set and training time of a model. Observatory study shows the integration of both parameters: accuracy and model learning time to form a generalized and optimized solution for predicting bank business.
  Keywords: Machine Learning;Business Intelligence;Data Mining;Decision support systems.
  
  
• Optimized Feature Selection and Categorization of Medical Records with Multi Kernel Boosted Support Vector Machine  
  by Lakshmii Prasannaa 
  Abstract: With the fast growth of Internet and mobile usage, huge volumes of medical documents, which contain information of patients, diagnostic, past disease history and medication, are being generated electronically. In the field of text mining, document categorization has become one of the emerging techniques due to large volume of documents in the form of digital data. The main objective of the proposed work is to identify disease treatment relationships and predict the diseases among medical articles. In this paper, highly relevant and more correlated features have been extracted using Probabilistic Latent Dirichlet Allocation (P-LDA) and randomized iterative feature selection approach. These features were classified with Multi Kernel Boosted Support Vector Machine (MKB-SVM), and then their performance was evaluated on both PubMed and MEDLINE databases. Performance evaluation of the proposed approach on DB-1 and DB-2 was 98.7% and 92%, respectively. The evaluation illustrated that the proposed approach outperformed the existing state-of-the-art classification methods.
  Keywords: Allocation of Latent Dirichlet ; Classification of Medical Text; SVM classification; Ada-Boost.Multi-kernel.
  
  
• Optimization of Sparse Linear Array Using State Transition Algorithm  
  by Pratistha Brahma, Banani Basu 
  Abstract: State Transition Algorithm (STA) has been used for sparse antenna array designing. A sparse linear array consisting of different core elements has been optimized using STA. The optimal solution is searched by changing the number of sparse elements and current excitation values of the core elements under a set of practical constraints. Number and position of sparse elements are optimized in order to achieve minimum Side Lobe Level (SLL) for a given Half Power Beam Width (HPBW) using various design examples. The paper has been studied the tradeoff between SLL and Directivity of the array for different numbers and positions of the sparse elements. Results obtained using STA has been statistically compared with that of the Particle Swarm Optimization (PSO) algorithm and Artificial Bee Colony (ABC) algorithm and ensures improved performances.
  Keywords: Sparse Antenna Array; STA; PSO; ABC; SLL; Directivity.
  
  
• An inventive and Innovative Integrated home network media system A novel approach  
  by Aswin Tekur, K.V. Shriram 
  Abstract: A common problem in TV viewing is watching programs on a single TV set because only one program can be watched at a time. Streaming is continuous and real-time, causing interruptions and clash among viewers (who wish to view different channels at the same time) and ultimately ruin the viewing experience. Our objective is to present a hardware unit (that is compatible with a smart TV set) to accommodate a range of features, such as: allowing simultaneous transmission of video from multiple channels (to network connected devices over a Limited Area Network), with pause/ play options, recording for certain time duration (made possible using memory buffers). These above functionalities require modification in the TV architecture of currently available systems, to support these functionalities. One component of our system (the transmission unit) can either be an additional hardware component in the TV set, or in the design of a portable flash drive (functioning similar to that of a dongle), for purposes of convenience and portability. The proposed system is aimed at making huge strides in the field of Television viewership and offers meaningful convenience features to enrich viewers experience, with minimal modification to presently used Television systems and make more utilization of network connected devices (such as laptops, tablets, smartphones). By eliminating need for multiple TV connections and using our system instead, one can save money, time and obtain many new features that are not offered by currently available Television sets.
  Keywords: Simultaneous video transmission; Advanced TV system; TV system over Limited Area Network; Home environment Television system; Multi viewer multi program TV system;.
  
  
• A Novel Variant of Bat Algorithm Inspired from CATD-Pursuit Strategy & Its Performance Evaluations  
  by Shabnam Sharma, Sahil Verma, Kiran Jyoti 
  Abstract: This paper presents a novel nature inspired optimization technique, which is a variant of Standard Bat Algorithm. This optimization technique is inspired from the pursuit strategy of microchiroptera bats and their efficient way of adaptation according to dynamic environment. Here dynamic environment describes different movement strategies adopted by prey (target), during their pursuit. Accordingly, bats have to adopt different pursuit strategies to capture the prey (target). In this research work, a variant of Bat Algorithm is proposed considering the pursuit strategy Constant Absolute Target Detection (CATD), adopted by bats, while targeting preys moving erratically. The proposed algorithm is implemented in Matlab. Results obtained are validated in comparison to Standard Bat Algorithm on the basis of best, mean, median, worst and standard deviation. The results demonstrate that the proposed algorithm provides better exploration and avoid trapping in local optimal solution.
  Keywords: Bat Algorithm; Constant Absolute Target Detection (CATD); Computational Intelligence; Echolocation; Meta-heuristic; Nature-Inspired Intelligence; Optimization; Pursuit Strategy; Swarm Intelligence.
  DOI: 10.1504/IJAIP.2021.10030248
   
  
• Wireless Smart Automation Using IOT Based Raspberry Pi  
  by Vasu Goel, Akash Deep, Madireddy Vivek Reddy, Yedukondala Rao Veeranki 
  Abstract: In this paper we propose a smart door lock system and lighting system for home automation. This door lock system and lighting system is controlled by Radio Frequency Identification (RFID) reader which is programmed by Raspberry Pi to detect the input swipe through our university combo card or a RFID tag and wirelessly sends the signal to the Espruino (ESP) Wi-Fi module and Node Microcontroller Unit (MCU) which in turn activates the lighting system and door lock system. The mainstream application of the system will be in hostel rooms or in our homes wherever door locks are there so that doors can be opened anytime we want without disrupting our work or getting up from our places in case of any injury with a swipe of card
  Keywords: Internet-Of-Things; Raspberry pi; Radio-Frequency Identification; Home automation; MQTT.
  DOI: 10.1504/IJAIP.2019.10026853
   
  
• Data Mining Techniques and Fuzzy Logic to Build a Risk Prediction System for Stroke  
  by Farzana Islam, M. Rashedur Rahman 
  Abstract: Nowadays, by using different computational system medical sector predict diseases. These systems not only aid medical experts but also normal people. In recent years stroke becomes life threatening deadly cause and it increased at global alarming state. Early detection of stroke disease can be helpful to make decision and to change the lifestyle of people who are at high risk. There is a high demand to use computational expertise for prognosis stroke. Research has been attempted to make early prediction of stroke by using data mining techniques. This paper proposes rule based classifier along with other techniques. The dataset is collected from Dhaka medical college, situated in Dhaka, Bangladesh To build a more accurate and acceptable model the system uses different classification methods likely- Decision tree, Support vector machine, Artificial neural network and fuzzy model. K-means, EM and fuzzy C-means clustering algorithm are used to label the dataset more accurately. Fuzzy inference system is also built to generate rules. ANFIS provides the most accurate model.
  Keywords: stroke; decision tree; SVM; MLP; artificial neural network; support vector machine; fuzzy model; FIS; ANFIS; data-mining; fcm; clustering; EM clustering; k-means; Bangladeshi dataset; fuzzy rule.
  DOI: 10.1504/IJAIP.2021.10054275
   
  
• An optimized fuzzy edge detector for image processing and their use in modular neural networks for pattern recognition  
  by Isidra Espinosa-Velazquez, Patricia Melin, Claudia Gonzalez, Frumen Olivas 
  Abstract: In this paper, the development of a fuzzy edge detector optimized with the metaheuristics: Genetic Algorithms and Particle Swarm Optimization is presented, based on the sum of differences method, using as inputs the absolute values of the difference from the pixels in the image. The Pratts figure of merit metric was used to know the performance of the proposed fuzzy edge detector. A modular neural network was designed for the recognition of faces in benchmark images and comparisons were made with different works carried out with other fuzzy edge detection systems. The main contribution of this research work is the development of a new fuzzy edge detector method optimized.
  Keywords: fuzzy logic; fuzzy edge detector; optimization; GA; genetic algorithm; PSO; Particle swarm Optimization; Neural networks.
  
  
• PEBD: Performance Energy Balanced Duplication Algorithm for Cloud Computing  
  by Sharon Priya Surendran, Aisha Banu W 
  Abstract: With the increasing demand of cloud data, efficient task scheduling algorithms are required with minimal power consumption. In this paper, the Performance-Energy Balanced Duplication (PEBD) scheduling approach is proposed for energy conservation at the point of task duplication. Initially, the resources are preprocessed with the Manhattan distance based Fuzzy Clustering (MFC).Then resources are scheduled using a Novel duplication aware fault tolerant based League-BAT algorithm and faults expected during job executions can be handled proactively. The fault adaptive firefly optimization is used for minimizing faults and it keeps information about resource failure. Consequently, the optimization ensures that performance is improved with the help of task duplication with low energy consumption. The duplications are restricted and they are strictly forbidden if they provide significant enhancement of energy consumption. Finally, enhanced compress & Join algorithm is used for efficient compression processing. It considers both schedule lengths and energy savings to enhance the scheduling performance with less power consumption. The performance of energy consumption and makespan for the proposed approach is increased with 6% and 0.5 % respectively
  Keywords: Manhattan distance; Fuzzy clustering; Resource scheduling; Duplication; fault tolerance; energy conservation.
  
  
• A Community Based Trusted Collaborative Filtering Recommender Systems Using Pareto Dominance Approach  
  by Anupama Angadi, Satya Keerthi Gorripati 
  Abstract: Recommender System algorithms provided clarification to information overload problem suffered by netizens. The Collaborative Recommender Filtering approach takes the user-item rating matrix as an input and recommends items based on the perceptions of similar neighbours. However, sparsity issue in the rating matrix leads to untrustworthy predictions. However, the conventional Collaborative Recommender Filtering method chooses ineffective descriptive users as neighbours for each target user. This hints that the recommendations made by the system remain inaccurate. The proposed approach addresses this issue by applying a pre-filtering process and integrates community detection with Pareto dominance, which considers trusted neighbours from the community into which the active user pertains and eliminates dominant users from the neighbourhood. The results on the proposed framework showed a noteworthy improvement in all the accuracy measures when related to the traditional approaches.
  Keywords: Community Detection; Recommender Systems; Sparsity; Pareto dominance; Cold Start; Trust propagation;.
  
  
• Web Server Workload Prediction using Time Series Model  
  by Mahendra Pratap Yadav, Akanksha Kunwar, Ram Chandra Bhushan, Dharmendra Kumar Yadav 
  Abstract: In distributed systems, multi-tier storage systems and cloud data-centers are used for resource sharing among several clients. To fulfill the clients request, the cloud providers share it's resources and manage the workload, which introduces many performance challenges and issues. One of the main challenges is resource provisioning in virtual machine (VMs or Container) since VMs are subjected to meet the demand of users with different profiles and Quality of Service (QoS). This proactive resource management approach requires an appropriate workload prediction strategy for real-time series data. The time series model exhibits prominent periodic patterns for the workload that evolves from one point of time to another with some short of time in random fluctuation. In this paper, a solution for the prediction of web server load problem has been proposed, which is based on seasonal ARIMA (Autoregressive Integrated Moving Average Model) model. ARIMA is a forecasting technique which predicts the future value based on its inertia. In seasonal ARIMA, seasonal AR and MA are used to predict the value xt (CPU workload time series) with the help of data values and errors at time lags that are multiple to the span of seasonality. We have evaluated our proposed method using real-world web workload data.
  Keywords: Cloud Computing; Elasticity; Auto-scaling; Time Series; Machine Learning.
  DOI: 10.1504/IJAIP.2022.10034175