Volume-07 , Special Issue-10 , May - 2019 Go Back

The paper investigates the problem of overshoot estimation decreasing for underdamped second-order control systems. A new technique to control the overshoot is proposed, which is based on Posicast control and proportional integral and derivative (PID) control, which performs switching between two controllers. The aim is to use open-loop feedforward control to increase tracking performance and PID control to deal with disturbance rejection. It has been shown that the proposed control scheme can have some advantages over the classical approaches without switching capabilities.

PID control, Posicast control, Second-order systems, Anti-windup

[1]. Chiang Loh P., Gajanayake C. J., Vilathgamuwa D. M. and Blaabjerg F., (2008). Evaluation of Resonant Damping Techniques for Z-Source Current-Type Inverter. IEEE Transactions on Power Electronics, Vol. 23, No. 4, pp. 2035-2043.
[2]. Hanus, Kinnaert M. and Henrotte J. L., (1987). Conditioning technique, a general anti-windup and bumpless transfer method. Automatica, Vol.23,
[3]. Kucera V. and Hromcik M., (2011), Delay-based input shapers in feedback interconnections, Preprints of the 18th IFAC World Congress, pp. 7577-7582.
[4]. Singhose, W. (2009). Command Shaping for Flexible Systems: A Review of the First 50 Years, Int. Journal of Precision Eng. and Manufacturing, Vol. 10, No. 4, pp. 153-168.
[5]. Yildiz Y., Annaswamy A., Kolmanovsky I. and Yanakiev D., Adaptive posicast controller for time-delay systems with relative degree n*≤2, (2010), Automatica 46, pp. 279-289.

Rakesh Mishra, J.P. Upadhyay, Surekha Gupta, "Estimation of Underdamped Overshoot in Second-Order Control Systems", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.97-100, 2019.

Data mining an non-trivial extraction of novel, implicit, and actionable knowledge from large data sets is an evolving technology which is a direct result of the increasing use of computer databases in order to store and retrieve information effectively. This paper gives an idea of optimization algorithm by which the efficient result can be fetched. Optimization is a dire need for a huge amount of data processing. So that optimization is a challenging issue in data mining. It seems to be that there are many different approaches has been proposed by authors in order to optimize the results. Partial swam optimization and genetic algorithms are some sort of approach which can be used for optimization.

Data Mining, Clustering, Optimized Algorithm, PSO, GA

[1] Satyasai Jagannath Nanda, Ganapati Panda, Automatic clustering algorithm based on multi-objective Immunized PSO to classify actions of 3D human models, Engineering Applications of Artificial Intelligence, Volume 26, Issues 5–6, May–June 2013, Pages 1429-1441
[2] Tuğrul Çavdar, PSO tuned ANFIS equalizer based on fuzzy C-means clustering algorithm, AEU - International Journal of Electronics and Communications, Volume 70, Issue 6, June 2016, Pages 799-807,
[3] Amin Khatami, Saeed Mirghasemi, Abbas Khosravi, Chee Peng Lim, Saeid Nahavandi, A new PSO-based approach to fire flame detection using K-Medoids clustering, Expert Systems with Applications, Volume 68, February 2017, Pages 69-80
[4] Hui-Liang Ling, Jian-Sheng Wu, Yi Zhou, Wei-Shi Zheng, How many clusters? A robust PSO-based local density model, Neurocomputing, Volume 207, 26 September 2016, Pages 264-275
[5] Chen Jinyin, Lin Xiang, Zheng Haibing, Bao Xintong, A novel cluster center fast determination clustering algorithm, Applied Soft Computing, Volume 57, August 2017, Pages 539-55
[6] Xiaojun Chen, Xiaofei Xu, Joshua Zhexue Huang, and Yunming Ye “TW-k-Means: Automated Two-Level Variable Weighting Clustering Algorithm for Multi-view Data” in IEEE Transactions On Knowledge And Data Engineering, Vol. 25, No. 4, April 2013
[7] Enmei Tu , Longbing Cao , Jie Yang , Nicola Kasabov “A novel graph-based k-means for nonlinear manifold clustering and representative selection” in Elsevier transaction of Neuro computing 143 (2014) 109–122
[8] T. Velmurugan “Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data” in Elsevier transaction of Applied Soft Computing 19 (2014) 134–146
[9] Grigorios Tzortzis “The Min Max k-Means clustering algorithm” in Elsevier transaction of Pattern Recognition 47 (2014) 2505–2516
[10] Cheng-Huang Hung , Hua-Min Chiou b, Wei-Ning Yang “Candidate groups search for K-harmonic means data clustering” in Elsevier transaction of Applied Mathematical Modelling 37 (2013) 10123–10128
[11] Yi Ding, Xian Fu, Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm, Neuro Computing, Volume 188, 5 May 2016, Pages 233-238
[12] A. Mukhopadhyay, U. Maulik and S. Bandyopadhyay, "Multiobjective Genetic Algorithm-Based Fuzzy Clustering of Categorical Attributes," in IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 991-1005, Oct. 2009.
[13] J. Zhang, H. S. H. Chung and W. L. Lo, "Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms," in IEEE Transactions on Evolutionary Computation, vol. 11, no. 3, pp. 326-335, June 2007.

Amitosh Patel, Shuchita Mudgil, "GA-PSO Based Clustering Algorithm For Multi View Data: A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.101-106, 2019.

The generation of electricity through renewable sources has exponentially increased in recent years in response to environmental pollution caused by fossil fuels. When we talk about renewable energy is difficult not to refer to solar energy, wind energy, biomass energy and Pedal generation due to its high rate of growth compared to their peers and potential. This is associated with the fact that the government is implementing a process of changing the production of energy and country`s energy matrix where one of the fundamental pillars is to eliminate dependence on fossil resources and promote the use of renewable energy especially for rural areas. Therefore, this makes clear that it is important and needed to study the effects and possible problems related to renewable energy that entails the use of photovoltaic systems connected to the network with hybrid one like pedal system generation which is easily available. In this context, the work focuses on power quality by connecting photovoltaic to low-power electrical system with using solar and pedal generation simultaneously. Objective of this research based on other sources of energy i.e. renewable energy, because we have limited sources of non renewable sources of energy i. e. coal diesel, petrol etc. Energy crises need to search for other source of energy that is specifically renewable energy. Human power praise is more because of health benefit as a source of energy. And in this peoples are more aware to his health point of view. More effective use of human power could be achieved through properly design approach. Human power as prime mover used to operate working unit is termed as human powered machine (bicycle) and this machine is different from conventional bicycle because it can operate with different arrangement with the help of hand or leg or in special case with the help of animal power and in developed machine combination of gear arrangement and chain are used. In this paper two models are simulated one for solar energy and other for pedal energy both are the alternate sources of energy and easily available. In the developed system energy security is also maintained with continuity of supply with safe operation

Power Quality, Power Filter, power electronic Controller, Harmonics Compensation, Total Harmonic Distortion

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Ajay Kumar Mishra, Bharat Mishra, A. K. Sharma, "Hybrid Power System for Power Quality Improvement and Security Analysis", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.107-110, 2019.

Blockchain is the chain of blocks and each block consists a time stamp, transaction data and a cryptographic hash of previous block. It is a decentralized distributed network in which each peer node connected with each other and shares history of all the transactions. Each new block added to blockchain whenever a transaction occur need to be verified by all the peer node and transaction executed successfully if more that 50% allows. There are some vulnerabilities which are present in Blockchain because of its publicly open network and lack of security certification. We tried to modify the Blockchain with the use of smart contracts with the existing blockchain network and using X.509 Certification for specifying the permission allotted to each peer node at the time it is added to the network.

Blockchain,Block,Node,X.509Certificate,Cryptographic,Hash,Vulnerabilities

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[3]LichengWang,XiaoyingShen,JingLi,JunShao,YixianYangCryptographic primitives in blockchains,Journal of Network and computerApplicationsVolume127, 2019, Pages 43-58
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[7]V.Y.Kulkarni,R.A.Rane,P.Mestr,S.Panchal, Risk Rating System of X.509 Certificates, Procedia Computer Science, Volume 89, 2016, Pages 152-161
[8] Adam MihaiGergelyBogdanCrainicu, The Concept of a Distributed Repository for Validating X.509 Attribute Certificates in a Privilege Management Infrastructure Procedia Technology,Volume 22, 2016, Pages 926-930
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focuses on An Introduction to Methods of Backup and Disaster Recovery for Cloud Computing .

Vivek Sharma, Nagendra Kumar, "Improving Security of Blockchain Through Authorisation", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.111-114, 2019.

Micro blogging websites like Twitter and Facebook, in this new era, is loaded with opinions and data. One of the most widely used micro-blogging site, Twitter, is where people share their ideas in the form of tweets and therefore it becomes one of the best sources for sentimental analysis. Opinions can be widely grouped into three categories good for positive, bad for negative and neutral and the process of analyzing differences of opinions and grouping them in all these categories is known as Sentiment Analysis. Data mining is basically used to uncover relevant information from web pages especially from the social networking sites. Merging data mining with other fields like text mining, NLP and computational intelligence we are able to classify tweets as good, bad or neutral. In order to improve classification results in the domain of sentiment analysis, we are using ensemble machine learning techniques for increasing the efficiency and reliability of proposed approach. For the same, we are using Linear Support Vector Machine and experimental results prove that our proposed approach is providing better classification results in terms of f-measure and accuracy in contrast to individual classifiers. We also use accuracy comparison framework for comparing algorithms based on execution time.

Sentiment Analysis,Twitter,Adjective analysis,Naïve Bayes,Linear SVM

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[14]. Zhijun Yan, Meiming Xing, Dongsong Zhang, and Baizhang Maa, “EXPRS: An extended pagerank method for product feature extraction nbbn from online consumer reviews,” Information & Management, vol. 52, pp. 850–858, 2015.
[15]. Ayoub Bagheri, Mohamad Saraee, and Franciska de Jong, “Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews,” Knowledge Based Systems, vol.52, pp. 201–213, 2013.
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[20]. Shahrukh Teli M-Tech Student, Prashasti Kanikar Assistant Professor, MPSTME SVKM’SNMIMS University, Mumbai, India.“A Survey on Decision Tree Based Approaches in Data Mining”, 2015.
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Shradha Gautam, Brajesh Patel, "Improving the Performance of Sentiment Analysis by Using Feature Combinations with Machine Learning", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.115-121, 2019.

In this paper, we describe an application K-MEAN++ clustering algorithm and Data Encryption Standard algorithm for security of information and large volumes data. Data are highly complex multidimensional signals, with rich and complicated information content Data science. For this reason they are difficult to analyze through a unique automated approach. However a K-MEAN++ scheme & Data Encryption Standard are helpful for the understanding of security of data content in Data science. In any system that captures, stores, analyzes, manages, and presents data that are linked to location and like Image satellite sensors acquire huge volumes of imagery to be processed and stored in big archives. Technically, a data science is a data modelling that includes mapping software and its application to data set , land surveying, aerial photography, mathematics, geography, and tools that can be implemented with Data science software Building a hierarchy is a fruitful area if one likes the challenge of having difficult technical problems to solve. Some problems have been solved in other technologies such as database management. However, Data science throws up new demands, therefore requiring new solutions. In this paper we have examine difficult problems, and to be solved and gives some security methods to solve the problem of data security using clustering algorithm.

K-MEAN++, Data science, Security Data Encryption Standard algorithm

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Sarita Patel, Atul Garg, Vandana Tripathi, "K-MEAN++ Applied To Solve Problems of Data Security in Data Science", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.122-126, 2019.

Cloud computing is a modern paradigm to provide services through the Internet. Nowadays, cloud computing is emerging field in information technology, next generation of computing. It provides very extensive measure of computing and storage Service gave to users through the internet which follows pay-as-you-go model. Major problems faced in the cloud are resource discovery, fault tolerance, load balancing and security. Load balancing is one of the main challenges, important technique, and critical issue and plays an important role which is required to distribute workload or task equally across the nodes or servers. Load balancing is a key aspect of cloud computing and avoids the situation in which some nodes become overloaded while the others are idle or have little work to do. Load balancing can improve the Quality of Service (QoS) metrics, including response time, cost, throughput, performance and resource utilization. Scheduling in Cloud computing infrastructure contain several challenging issues like computation time, budget, load balancing etc. Out of them, load balancing is one the major challenges for Cloud platform. Load balancing basically balances the load to achieve higher throughput and better resource utilization. Since scheduling task is NP-complete problem, so heuristic and meta heuristic approaches are preferred options to solve the same. In this dissertation, we chose a meta-heuristic approach to solve the task scheduling problem in cloud environment focussing mainly on two objectives, i.e., minimizing the makespan/ computation time and better load balancing.

Load balancing, Execution time, response time, Task Transfer

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Aakriti Pathak, Anshul Khurana, "Load Balancing Algorithm Based On Task Transfer and Information Policy", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.127-130, 2019.

Nowadays, cloud computing is very important because of its benefits. But it is also prone to attacks due to vulnerabilities of the websites. DDoS is distributed denial of service where Trojan infected computers are used to target a system to cause traffic jams and genuine users can not able get the service of websites. DDOS affects internet services like e-commerce, e-banking, education, medicine, reservations, agriculture etc. In this way both the end systems are controlled by hackers. There are three categories – volume-based attacks, protocol attacks and application attacks. The companies they are implementing no. of solutions to defend the attacks and continuously updating their techniques but attackers also updating their techniques and methods of attacks. So, in this paper the system is made to collect data online and with the help of association rule mining ddos attack are detected and prevented. Here techniques are used to properly detect attackers and genuine users so that cloud services can be given the users.

Component, Formatting, Style, Styling, Insert (key words)

[1] Mohd. Azahari Mohd Yusof, Fakariah Hani ali, and Mohd. Yusof Darus. Detection and Defense Algorithms of Different types of DDoS Attack. International Journal of Engineering and Technology, October 2017.
[2] Sunny Behal. Characterization and comparison of DDoS attack tools and traffic generators. International Journal of network security, April 2017.
[3] Zhu Limiao, Haung Hua and Zheng Hao. Research on Intrusion Detection System Model based on data mining. Fourth International Conference on Multimedia Information Networking and security, 2012.
[4]Desheng Fu, Shu Zhou, Phing Guo. The design and implementation of a distributed network intrusion detection system based on data mining. World Congress on software engineering.
[5] Surbhi K. Solanki, Jalpa T. Patel. A survey on association rule mining. Fifth International Conference on Advanced computing & communication technologies.
[6]Nivedha and M. Naveen Nanda, Two layer cloud security set architecture on hypervisor. Second International Conference on Advances in Electronics, Computer and Cmmunications , 2018.
[7]Kemal Hajdarevic, Survey on machine learning algorithms as cloud services for CIDPS, 25th telecommunications forum TELFOR, Serbia, 2017.
[8]V. Priyadharshini and Dr. K.Kuppusamy, Prevention of DDOS attacks using New Cracking Algorithm, International Journal of Enginnering Research and Application, may-june 2012.
[9]Jigang ZHENG and Jingmei ZHANG, Association rule mining in DoS attack detection and defense in the application of network, 5th International Conference on Education, Management, Information and Medicine, 2015.
[10] Yaping Chi, Tingting Jiang, Xiao Li and Cong Gao, Design and implementation of cloud platform intrusion prevention system based on SDN, Department of communication engineering Beijing electronic science and technology institute, 2017.
[11] Dina Moloja and Noluntu Mpekoa, Securing M-voting using cloud Intrusion Detection and Prevention system: A New Dawn, IST, International Information Management Corporation Africa, 2017.

Anjulee Gupta, Brajesh Patel, "A Detection and Prevention of Ddos Attacks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.131-124, 2019.

Sentiment analysis is the computational study of opinions, sentiments, evaluations, attitudes, views and emotions expressed in text. It refers to a classification problem where the main focus is to predict the polarity of words and then classify them into positive or negative sentiment. Sentiment analysis over Twitter offers people a fast and effective way to measure the public’s feelings towards their party and politicians. The primary issues in previous sentiment analysis techniques are classification accuracy, as they incorrectly classify most of the tweets with the biasing towards the training data. Sentiment analysis is considered to be a category of machine learning and natural language processing. It is used to extricate, recognize, or portray opinions from different content structures, including news, audits and articles and categorizes them as positive, neutral and negative. It is difficult to predict election results from tweets in social media using different platforms. We performed data (text) mining on thousands of tweets collected over a period of a month that referenced five national political parties in India, during the campaigning period for general state elections in 2018. We made use of both supervised and unsupervised approaches. We utilized Dictionary Based, Logistic Regression algorithm as the main algorithm to build our classifier and classified the test data as positive, negative and neutral. We identified the sentiment of Twitter users towards each of the considered Indian political parties. The result of the analysis was for the BJP (Bhartiya Janta Party). Proposed algorithm predicted a chance that the BJP would win more elections in the general election. Therefore, here we adopt a lexicon based sentiment analysis method, which will exploit the sense definitions, as semantic indicators of sentiment. Our method also uses a negation handling as a pre-processing step in order to achieve high accuracy.

Negation Handling; Sentiment Analysis; WordNet; SentiWordNet; Word Sense Disambiguation

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Deeksha Vishnoi, Brajesh Patel, "A Proposed Method for Predicting Indian Loksabha Election Using Machine Learning in Social Media", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.135-143, 2019.

Social network is a platform of connected peoples where peoples share relation, emotions, activities etc. Second generation social network come in existence with lots of emerging applications, which also support service oriented environment, during all kind of activities massive information is generated. These all information are in the form of post. Hence it is necessary to find category of post. A post may be legitimate or malicious. In this dissertation we are trying to find malicious post on the basis of reaction and share on particular post. All the post collected by facebook through app known as Netvizz. Entire concept implemented in R studio which is an IDE of R programming. Dissertation also contain statistical analysis of page network with the help of well known tool gephi, It is based on predefine parameter such as Eigen vector centrality,closeness ,betweenness etc.

Facebook,Netvizz,Gephi,Visualization

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Rubina khan, Anshul khurana, "Reaction Based Approach to find Malicious Posts in Online Social Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.144-148, 2019.