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In competitive electricity market worldwide raises many challenging tasks related to the economic and optimal operation of electric power systems. In deregulated market structure, the generation is being despatched by means of hourly power delivery. The penalty is improved on power producers, if they fail to attain the planned energy delivery. The inadequate hydel resources associated with environmental constraints of thermal plants necessiates a precise scheduling system to satisfy the ever growing power demand. The power generator in a hydrothermal has to manage the conflicting objectives of profit maximization and emission minimization. Normally, the multi-objective optimization problem is tuned for optimising the two or more conflicting objectives subject to some constraints. Short-term hydrothermal scheduling (STHTS) problem deals with more objective functions such as profit maximization and emission minimization. Hence it is necessary to evolve a constructive framework based on intelligent techniques. In this paper, a stochastic multi-objective model is derived for the flexible scheduling of hydrothermal plants with valve-point loading effects. A non-dominated sorting teaching learning based optimization (NSTLBO) algorithm is presented for solving STHTS problem. The proposed algorithm is applied to derive a pair of non-dominated results and then the fuzzy based methodology has been argued to choose the best solution. It is tested on a three thermal and four hydro test system with twenty four hour time period. The results are extracted by means of total profit and emission from the plants. Comparative studies have also been done to validate the viability of the proposed method.

Deregulation, Hydrothermal Scheduling, Profit Maximization, Emission Limitations, Non-dominated Sorting TLBO algorithm

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Pasupulati Baburao, R Ashok Kumar, Balamurugan G, K Asokan, "A Non-Dominated Sorting TLBO Algorithm for Multi-Objective Short-Term Hydrothermal Self Scheduling of GENCOs in a Competitive Electricity Market," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.191-203, 2018.

Intrusion detection is a fundamental part of security tools, for example, adaptive security appliances, intrusion detection systems, intrusion prevention systems and firewalls. Intrusion detection systems (IDS) plays a important role in detecting the attacks that occur in the PC or networks. Intrusion detection systems (IDS) are the network security mechanism that monitors network and system activities for malicious action.it become indispensable tool to keep information system safe and reliable. Different intrusion detection methods are used, but their performance is an problem. . Intrusion detection performance depends on accuracy, which needs to enhance to decrease false alarms and to increase the detection rate. Such procedures demonstrate limitations, are efficient for use in large datasets, for example, system, and network data. The intrusion detection system is used to analyzing huge traffic data, therefore efficient classification method is important to overcome the issue. Well-known machine learning techniques, namely, SVM, Multiclass SVM, k-NN, Binary Classification (BC) are applied. These techniques well known because of their capability in Classification. The NSL–knowledge discovery and data mining, dataset is used, which is considered a benchmark in the evaluation of intrusion detection mechanisms. The results indicate that Multiclass SVM outperforms other approaches.

Support vector machine SVM, Multiclass SVM, k-NN, Binary Classification, NSL-KDD

[1] Binhan Xu, Shuyu Chen, Hancui Zhang, Tianshu Wu,” Incremental k-NN, SVM Method in Intrusion Detection”
[2] Manjiri V. Kotpalliwar, 2Rakhi Wajgi” Classification of Attacks Using Support Vector Machine (SVM) on KDDCUP’99 IDS Database”
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[4] Sumaiya Thaseen, Ch.Aswani Kumar “Intrusion Detection Model Using fusion of PCA and
Optimized SVM”
[5]A.M.Chandrasekar, K.Raghuveer, Intrusion Detection Techniques by using K-Means, Fuzzy Neural Networks, SVM Classifier
[6] Noreen Kausar , Brahim Belhaouari Samir, Suziah Sulaiman, Iftikhar Ahmad , Muhammad Hussain “An Approach towards Intrusion Detection using PCA Feature Subsets and SVM”
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[8] Gong Shang-fu,Zhao Chun-lan “Intrusion Detection System Based on Classification”
[9] Nathan Shone, Tran Nguyen Ngoc, Vu Dinh Phai, and Qi Shi “A Deep Learning Approach to Network Intrusion Detection”
[10] Longjie Li, Yang Yu, Shenshen Bai, Ying Hou, and Xiaoyun Chen,”An Effective Two-Step Intrusion Detection Approach Based on Binary Classification and k-NN”
[11] Chuanlong Yin, Yuefei Zhu, Jinlong Fei, and Xinzheng He “Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks “
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[13] Abdulla Amin Aburomman, Mamun Bin Ibne Reaz “A novel SVM-kNN-PSO ensemble method for intrusion detection system “
[14]H. Wang, J. Gu, S. Wang, “An effective intrusion detection framework based on SVM with feature augmentation, Knowledge-Based Systems”, Volume 136, 2017, Pages 130-139, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2017.09.014.
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[16]A. A. Aburomman, M.B. Reaz, A novel SVM-kNN-PSO ensemble method for intrusion detection system, Applied Soft Computing, Volume 38, 2016, Pages 360-372, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2015.10.011.
[17]M.R. Raman, N. Somu, K. Kirthivasan, R. Liscano, V.S. Sriram, An efficient intrusion detection system based on hypergraph - Genetic algorithm for parameter optimization and feature selection in support vector machine, Knowledge-Based Systems, Volume 134, 2017, Pages 1-12, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2017.07.005.
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Kumar Parasuraman, A. Anbarasa Kumar, "Performance Comparison of Multi class SVM, Support Vector Machine, k-NN and Binary Classification for Intrusion Detection," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.204-211, 2018.

Knowledge exploration from the large set of data, generated as a result of the various data processing activities is an effective application of data mining. Text mining applications have also become important areas of application in the field of document processing. Document clustering has also become an important process for helping the information retrieval systems to organize vast amount of data. This can be tried with categorical data and for image categorization. At the same, time, frequent pattern mining has also become a very important undertaking in data mining. In the research work described in this paper, Apriori algorithm has been applied to generate frequent itemset and this method contains mainly two steps, viz. candidate generation and pruning techniques for the satisfaction of the desired objective. Aim of this paper is to focus on frequent itemset generation from dataset of variable length. Several steps have been executed to achieve the desired result. The primary goal has been to build a method which can be used to find significant items from a text database in an easy and efficient way.

Itemsets, Tokenization, Stemming, Apriori algorithm

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D.Datta, A. Mitra, D. Nag, N. Roy Choudhury, "Text Categorization using Apriori Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.212-217, 2018.

This work deals with robustness analysis of Grey Wolf Optimization (GWO) / Proportional-Integral-Derivative (PID) approach in control of ball hoop (BH) system with integral of time multiplied absolute error (ITAE) objective function. The robustness analysis of GWO/PID approach has been carried out with ±5% perturbation in the locations of the poles of the BH system. It has been observed that proposed GWO/PID approach with ITAE objective function gives satisfactory performance with ±5% perturbation.

BH System, PID, GWO, ITAE, Robustness

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Mridul Porwal, Girish Parmar, Rajesh Bhatt, "Robustness Analysis of GWO/PID Approach in Control of Ball Hoop System with ITAE Objective Function," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.218-222, 2018.

In modern days, more and more community joins social networks to contribute to information with others. At the same time, the in sequence sharing/spreading becomes far more frequent and convenient due to the wide usage. The research contented of computer networks comprises arrangement topology, network interchange uniqueness, and the authority of the network behavior on the whole set of connections. The spread and avoidance of network malware knowledge studied in network and have been one of the majority prolific fields in complex network dynamics research. Through our research, we found that some individuality of workstation network virus proliferation is similar to real world outbreak spread. Therefore, any misinformation should be exposed in time when it does not increase to a large group of populace. All preceding works deliberate either how the in succession is extend in the social complex or how to inhibit the further pervasion of an observed misinformation. However, no works considered how to discover the broadcasting of misinformation in time. A possible explanation is to set observers across the network to determine the suspects of misinformation established by the optimization problematic is NP-hard and deliver approximation assurances for an avaricious answer for various meanings of this problem by provides evidence that they are sub modular. In this accomplishment, a novel method to decide on a set of spectator in a social network with the minimum cost, where these observers assurance any misinformation can be discovered with a high likelihood before it reaches a surrounded number of users.

Information sharing, Misinformation, Online Social Network, Suspects, Optimization, Privacy

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N. Sindhuja, K. Ravi Kumar, "Malware Dissemination and Anticipation Model for Ensuring Privacy in Time-Varying Population Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.223-227, 2018.

Mobile cloud computing (MCC) has been provided as a viable technique to the derived boundaries of mobile computing. Those barriers include battery lifetime, garage potential and processing power. By using MCC, the storage and the processing of speedy cell device responsibilities will take in the cloud gadget. The outcomes may be repeated to the cellular device. It decreases the required energy and time for reaching such intensive jobs. But, cellular devices connecting with the cloud suffers from the excessive community latency and the massive transmission strength intake mainly whilst the usage of 3G/LTE connections. Then again, multimedia packages are the maximum not unusual packages in modern day mobile gadgets; such packages require excessive computing resources. In this paper, it`s been analyzed a cloudlet-based MCC totally machine that determines the cloudlets aiming at reduce the energy intake and a network put off of multimedia applications even as using MCC.

MCC, Cloudlets, LTE Network, Multimedia

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C. Abinaya, E. Ramaraj, "Offloading Scheme for Cloudlets Computation Tasks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.228-232, 2018.

The aim of the research paper is to study the impact of digital tools among the visual artists. Due to the fast changes in the information and communication technology tools, the digital tools develops and changes day by day. Lack of proper training related to software and tools affects the visual artists with the age groups 35 to 45. The traditional way of doing visual works requires special skills such as eye, hand and mind coordination. Working in the digital system needs other skills such as technical, digital and software knowledge. Even though the pre-production, production and post-production process simplified, it develops a pressure among the traditional visual artist to change their perspective and view of the fine arts. Updating the software version and the new digital tools’ related knowledge makes them depend on the software and technical system rather than the free expression and creativity. Particularly, the artists between the age groups 35 to 45 favor the concept of creating visuals by using brushes and other traditional media. Working with the system develops a stress among the artists. Many of the above age groups give feedback which does not favor the concept of digital graphics. A digital tool gives more flexibility in all the stages of graphic design, drawing & painting, 2D, and 3D animation process. Furthermore, it reduces the complexity of the process of editing, retouching, storing, printing, copying and developing, etc. Working with computer the system for long hours is difficult for the visual artists of age groups 35 to 45. Many artists express their views in favor due to the medium and comfort was given by the traditional brushes and other materials. Handling keyboard and mouse requires more muscular movements than the application of brushes.

Visual artist, Digital tools, Flexibility, Stress, Traditional brushes, Physical pain, Opinion of visual artis

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S. Kalaiselvan, A. Kalimuthu, B. Senthil Kumar, "A Critical Analysis of Digital Tools Among The Visual Artists," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.233-235, 2018.

Nowadays biometrics based authentication could be a one amongst the necessary technology during this world. Authentication means whether that person is genuine or not. Based on this authentication so many physical and behavioural characteristics are used. Physical characteristics are face, fingerprint, iris, retina, palm print, finger vein and finger knuckle print and behavioural characteristics means that basic actions done by the person like voice, giant, ECG, EEG, keystroke, handwriting and lip movements. Based on this physical traits finger knuckle print is one of the most interest research arena of the researchers nowadays. Because it can be easily accessed, low cost devices are used to capture the image; it cannot be stolen by others, etc. In finger knuckle print based authentication we have got to survive such a lot of papers. Per this study the finger knuckle print encompasses a heap of deserves and limitations. To provide a comprehensive survey, this paper presents a summary of finger knuckle print biometric system, their applications and limitations.

Biometrics, biological, behavioural, identification, techniques

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L. Sathiya, V. Palanisamy, "A Survey on Finger Knuckle Print based Biometric Authentication," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.236-240, 2018.

Everything stored on the cloud could potentially be a knowledge source used for e-learning. Given learners` profiles, desires and feedback on what they have already learned, a new form of personalized e-learning emerges, namely Cloud E-Learning (CeL). CeL should be able to choose from structured to totally unstructured learning material but needs to make them useful for each individual. Existing metadata standards cannot facilitate composition of personalized learning paths as a series of learning objects. In this paper, we present the structure of CeL Learning Objects (CeLLOs), which include an additional set of metadata suitable for each phase of CeL development.

Cloud E-learning, computing for Education, Electronic Learning

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Faiz Akram, Rajeev Kumar, "Accuracy of Retrieval Files in Learning Objects using Cloud E-Learning," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.241-244, 2018.

Cyber forensics includes the areas of computer forensics, network forensics and internet forensics. In Cyber forensics, digital images have been widely used for retrieving criminal images, fingerprints, crime incident images and so on. Since the current cyber forensic tools are not very much furnished with the course of action of huge image data, retrieving of image evidences to prosecute the criminal becomes a big issue, most of the evidence is available in the form of raw semantics. Cyber forensic investigators are often faced with the task of manually examining a huge amount of image data to identify potential evidence by the help of this semantics. Thus semantic based image retrieval system (SBIR) is the latest and best alternative to overcome this drawback. The main objective of this paper is to perform extensive literature survey on the different existing methods of semantic based image retrieval system, to find the pitfalls in the existing methods and to propose a SBIR framework for the development of cyber forensic tools.

Cyber Forensics SBIR, Digital image, Semantics and Evidence

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Ramesh Babu P, E. Sreenivasa Reddy, "A Comprehensive Survey on Semantic based Image Retrieval Systems for Cyber Forensics," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.245-250, 2018.