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The volume of information untapped are locked up in huge volume of text documents (unstructured data) that could aid the economy, government, individuals and corporate organisation to improve on the state of life and develop better working system cannot be overemphasized, therefore the need to extract this information and give a structure that will facilitate its proper storage and access when required becomes so important. The target of this research is to explore Clustering as a Tool for Categorizing Unstructured Data (Text document). The K-Prototype Algorithm was applied for the purpose of clustering these unstructured data to give structure to it. There are two major phases involved in this: first is the pre-processing phase (Tokenization, Stemming, and Stop Word Removal) and secondly the clustering phase. The system built performed better as shown from the result, that it can be use to categorise text documents for proper and easy storage and accessibility.

Unstructured data, Clustering, Categorisation, K-Prototype Algorithm, pre-processing

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Ngor Gogo, E. O. Bennett, "Clustering as a Tool for Categorization of Unstructured Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.116-121, 2019.

Epilepsy is a neurological disorder in the human brain, which is characterized by chronic disorders and occurs at random to interrupt the normal function of the brain. The diagnosis and analysis of epileptic seizure is made with the help of Electroencephalography (EEG). In order to detect seizure, this study aims to construct an automatic seizure detection system to analyze epileptic EEG signals. The CHB-MIT Scalp EEG dataset is used for the experiment purpose. The Welch Fast Fourier Transform is applied to convert time-domain signals to frequency-domain. The statistical features are extracted from both time and frequency domains. The ANOVA based feature selection is used to select the most significant features. Data under-sampling and over-sampling techniques are used to balance the data. Eight machine learning algorithms, including Decision Tree, Extremely Randomized Decision Tree, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Random Forest, Gradient Boosting, Multilayer Perceptron, and Stochastic Gradient Descent are used to classify the data. The highest result is recorded as 99.48% of accuracy, 99.79% of sensitivity, and 99.17% of specificity for the Extremely Randomized Decision Tree. The system might be a helpful tool for physicians to make a more reliable and objective analysis of a patient`s EEG records.

Epilepsy, Electroencephalogram, Welch Fast Fourier Transform, Data Sampling techniques, Machine Learning Algorithms

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Mirwais Farahi, Doreswamy, "Performance Evaluation of Machine Learning Classifiers for Epileptic Seizure Detection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.122-129, 2019.

Obtaining quality of service (QoS) through several routing schemes attracts researchers in the field of MANETs. Optimized routing through energy aware load balanced schemes is plays a significant role in ensuring QoS as well as many real – time applications. In this phase of research work, Glowworm Swarm Optimization is used for performing clustering operation. An adaptive on – demand routing mechanism is also employed. Simulation settings are used for analyzing the performance of the GSO-COD-LBS with other routing protocols / solutions / schemes using the metrics packet delivery ratio, throughput, packets drop, overhead and delay. From the results that are obtained through simulations it is inferred that GSO-COD-LBS outperforms other existing routing protocols and our earlier proposed works.

routing, load balancing, energy, QoS, MANET

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[6]. Abin, Paul, Preetha, K.G., 2013. A cluster based leader election algorithm for MANETs. In: International Conference on Control Communication and Computing, pp. 496–499.
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[12]. Ibukunola, A., Modupea, Oludayo, O., Olugbarab, Abiodun, Modupea, 2013. Minimizing energy consumption in wireless adhoc networks with meta heuristics. In: Proceeding of 4th International Conference on Ambient Systems, Networks and Technologies, vol. 19, pp. 106–115.
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[14]. Jabbar W.A., Ismail M., Nordin R., 2017.Energy and mobility conscious multipath routing scheme for route stability and load balancing in MANETs. Simulation Modelling Practice and Theory. 77, 245-271.
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[16]. Aruna Devi P., Karthikeyan K., 2018, Bio Inspired Ant Colony Optimization Based NeighborNode Selection and Enhanced Ad Hoc on DemandDistance Vector to Defending Against Black HoleAttack by Malicious Nodes in Mobile AD HOC NetworksJournalofComputational and Theoretical Nanoscience. 15, 3011 – 3018.
[17]. Aruna Devi P., Karthikeyan K., 2019, Fuzzy Clustering and Constructive Relay-based Cooperative and Load Balanced Routing in MANET. Journal of Advanced Research in Dynamical and Control Systems. 11 (04), 1743 – 1753.
[18]. Aruna Devi P., Karthikeyan K., 2019, Grey Wolf Optimization based Clustered On – Demand Load Balancing Scheme (GWO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks. International Journal of Computer Sciences and Engineering, 7(6), 641-649.

P. Aruna Devi, K. Karthikeyan, "Glowworm Swarm Optimization based Clustered on – Demand Load Balancing Scheme (GSO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.130-136, 2019.

Recently, it`s observed that the crime is increasing across the world very rapidly and some technique is required for analysis of the crime data. Analysis of the crime data can be done through data mining (DM). DM techniques are applied to crime data for predicting features that affect the high crime rate. Using the method of data mining on previously collected data for predicting the features responsible for the crime in a locality or area, the Police Department and the Crimes Record Bureau Police Department may take the required measures to reduce the likelihood of the crime. In the current work, a new machine learning ensemble algorithm is opted for predicting feature that affects a high crime rate. It helps the police and citizens to take necessary and required action in decreasing the crimes rate. The ensemble algorithm can predict more accurate and significant features with higher accuracy and efficiency.

Crime investigation, Crime Prediction, Crime Prediction, Data Mining, Ensemble approach

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Sadhna sharma, Sanjiv sharma, "Identification of Accurate Classification Technique for Crime Investigation Using Ensemble Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.137-143, 2019.

In recent years exploration of wireless devices with small in size and platforms based on processing mobile have bring great attention towards the ad hoc networks. These Ad hoc networks are generally consists of temporary links connected among the nodes. MANET is such a dynamic network which compromises many good qualities to handle the wireless communication devices which are roaming randomly. MANET holds certain restriction on them because of the energy, battery consumption, load management, etc. Due to the nodes mobility nature and the characteristic of error prone nature the wireless medium pretense a lot of confronts, together with recurrent route change and packet losses, in the way of conferencing the necessities of QoS. Such disputes amplify packet delay, reduces throughput and lesser network failure. The first phase adapts the behavior of ants to discover the route and the intuitionistic fuzzy estimation is used to find the direction for searching the optimal route and the fault occurred in the node or link is handled using the check point and fault manger. In nature MANET are logically realized as a set of clusters by grouping together nodes which are in close proximity with one another or through another wireless node. The nodes communicate to the base station directly for data transfer. But it is not efficient in mobile environment because of the energy restriction on each node. The second phase focuses on such issue by creating the cluster topology to group similar nodes by adapting intuitionistic fuzzy k means and electing the cluster heads using the evolutionary algorithms known as genetic algorithm. Then elected cluster head collects the data from other nodes within the cluster and aggregates the data packets for transferring it to the base station directly or by gateway or cluster heads of other clusters. The simulation model for each phase is deployed using MAT lab and the outcome of the result shows that in each phase the proposed techniques performs better than other existing techniques discussed in each phase due to their enhancement in the proposed techniques.

Intuitionistic fuzzy, cluster head, optimal route

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S.P. Swornambiga, "Optimal Fault Tolerance Using Intuitionistic Fuzzy and Selection of Cluster Head in MANET," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.144-150, 2019.

The Maximum Power Point Tracking (MPPT) is a vital element in a Photovoltaic (PV) system to harvest maximum power from solar PV system. Therefore, it is of interest to us to develop a more effective and efficient system that can transfer maximum power to the load. Implementation of the new MPPT techniques such as Artificial Neural Network (ANN), Fuzzy Logic Control (FLC) and Cuckoo Search Algorithm (CSA) is carried out to identify the most efficient system and the results are compared with conventional Perturb and Observe (P&O) and are presented in this paper. The proposed models are simulated and obtained results are analyzed and compared with the conventional method using MATLAB/Simulink, while the real-time implementation of similar prototype is carried out using a novel dSPACE DS1202 MicroLabBox and highly accurate PV emulator. Further this paper also comprises the comparison of steady state accuracy of D.C. link voltage of proposed techniques i.e. ANN, FLC, CSA vis-a-vis conventional P&O using MATLAB/Simulink and dSPACE.

PV, MPPT, dSPACE, CSA, FLC, ANN, P&O, AI

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Srishti, Prerna Gaur, "Simulation and Real-Time Implementation of MPPT Techniques for PV System using High Precision PV Emulator and dSPACE DS1202 MicroLabBox," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.151-162, 2019.

Big Data and Internet of Things (IoT) are two popular technical terms in current IT industry. The computing of IoT applications data consumes more energy since it’s high velocity in real-time. The proposed methodology re-storm that addresses energy issues and response time of IoT applications data. It uses big data stream computing for re-storm against existing method storm. The ultimate goal of proposed system is to plan and develop complete strategies to improve the performance of BDSC Environment for IoT application datasets. The storm failed to address dynamic scheduling but re-storm deals with three different features, 1) Data stream graph optimization, 2) energy-efficient self-scheduling strategy, 3) Real-Time Data Stream Computing with Memory Level Dynamic Voltage and Frequency Scaling (DVFS). Proposed system handles different traffic arriving rate of streams and re-storm at multiple traffic levels for high energy efficiency, low response time. It deals at three levels, firstly, a mathematical model for high energy efficiency, low response time. Secondly, allocation of resources bearing in mind DVFS methods and existing effective optimal consolidation methods. Thirdly, online task allocation using hot swapping technique and streaming graph optimizing. Finally, the experimental results show that restorm has been improved the performance 30-40% against storm for real time data of IoT applications.

Internet of Things, Big Data Stream Computing, Hadoop Distributed File System, Virtual Machine

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Sharmila G., "Real-Time Internet of Things (IOT) Application Big Data Stream Graph Optimization Framework," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.163-167, 2019.

Distributed processing frameworks split a data intensive computation job into multiple smaller tasks, which are then executed in parallel on commodity clusters to achieve faster job completion. A natural consequence of such a parallel execution model is that slow running tasks, commonly called stragglers potentially delay overall job completion. Stragglers in general take more time to complete tasks than their peers. This could happen due to many reasons such as load imbalance, I/O blocks, garbage collections, hardware configuration etc. Straggler tasks continue to be a major hurdle in achieving faster completion of data intensive applications running on modern data-processing frameworks. The trouble with stragglers is that when parallel computations are followed by synchronizations such as reductions, this would cause all the parallel tasks to wait for others meaning that the parallel runtime is dominated by the slowest performing straggler. In a large-scale distributed system comprising a group of worker nodes, the stragglers` delay performance bottleneck, is caused by the unpredictable latency in waiting for slowest nodes (or stragglers) to finish their tasks. Such stragglers increase the average job duration by 52% in data clusters of Facebook and Bing even after these companies using state of the art straggler mitigation techniques[1]. This is because current mitigation techniques all involve an element of waiting and speculation. Existing straggler mitigation techniques are inefficient due to their reactive and replicative nature – they rely on a wait speculate- execute mechanism, thus leading to delayed straggler detection and inefficient resource utilization. Hence, full cloning of small jobs, avoiding waiting and speculation altogether is proposed in a system called as Dolly. Dolly utilizes extra resources due to replication.

Distributed network, latency, straggler detection, data clusters, slowest performing straggler

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Md. Nesar Rahman, Ayesha Siddika, Muhammad Shafiqul Islam, Md. Shahajada, "Straggler Problem –Tail Latancy in Distributed network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.168-178, 2019.

Human face images are very important for the identity of human faces and used for many applications such as authentication, or medical fields for analysis. The face retrieval and detection from the large database is a difficult problem. It becomes more challenging in the presence of makeup on the faces. Makeup is done in the different parts of the face such as lips, eyes, or on cheeks. Therefore, it is required to first detect the makeup on the image and then use efficient face recognition method. In this paper a novel texture and shape based feature extraction methods are presented using the wavelet based feature fusion for the efficient face recognition. The goal is to recognize the quarry face within the image database. The detection algorithm is very simple and fast to work for large databases. First a random quarry image is picked from database then features are extracted from both quarry and template images. Method first resizes the quarry and template images and then calculates features in RGB domain. For the texture analysis the Local Ternary Pattern (LTP) based feature are adopted in place of Local binary pattern (LBP). For feature enhancement the wavelet based fusion of lower and upper LTP patterns are proposed in the paper. Method is calculated and compared for images with and without makeup. To analyze the shape features Histogram of Gradient are plotted. The performance of our proposed feature extraction is tested using the Face images of man’s and women’s with heavy and light makeup and also without makeup.

Face Recognition, Makeup Detection, Feature extraction, Histogram of Gradient, Image binary patterns

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Rohita Singh, Monika Raghuwanshi, "A Novel Feature Extraction Method for Texture and Shape Analysis of Face Makeup Database," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.179-184, 2019.

Random forest are able to do classification on high performance through a classification ensemble with a decision trees that grow mistreatment at random elect subspaces of information. The performance of associate degree ensemble learner is very obsessed on the accuracy of every element learner and also the diversity among these parts. In random forest, organisation would cause incidence of unhealthy trees and should embrace related trees. This ends up in inappropriate and poor ensemble classification call. During this paper a shot has been created to enhance the performance of the model by applying material technique in a very random forest. Experimental results have shown that, the random forest are often more increased in terms of the classification accuracy.’

Random forest, Classification Accuracy, Bagging

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Vikas S., Thimmaraju S.N., "Enhancement of Data Classification Accuracy using Bagging Technique in Random Forest," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.185-188, 2019.