Volume-06 , Special Issue-03 , Apr - 2018 Go Back

Why to do hard work? When smart work pays off! There are about 7.6 billion people in the world who do many tasks every day, in which most of the tasks are repetitive. Repetitive tasks can be assisted and done by employing machine learning. Data is generated from these repetitive tasks, and this voluminous data is managed by Big Data Analytics and it is analyzed by Machine Learning and provides smart solutions. First of all Machine Learning creates a study pattern based on our daily routines and this data will be at a level of complexity that human minds will fail to comprehend. Machine Learning will make it possible for automated system to outthink the human brain by integrating broad information sets and finding correlations. A large number of repetitive tasks that involve manual labor can be automated through Machine Learning. Advances in Machine learning signify a future when devices run on self-learning algorithms and operate independently. They may deduce their own conclusions within certain parameters and develop a context based behavior to interact with human more directly than before. This could mean automating tasks of professionals like doctors (analyzing reports), advocates (for analyzing vast number of judgments and concluding outcomes), etc., even for routine jobs Machine Learning could uncover new potentials and enable human to make the best of their talents. In this article we would focus on how to minimize the time and energy spent on the repetitive and tedious tasks by assigning them to smart assistants using Machine Learning.

Smart work, Machine Learning, Automating, Smart assistants

[1] ABDULSALAM YASSINE, SHAILENDRA SINGH and ATIF ALAMRI, “Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications”
[2] Taking the Human Out of the Loop: A Review of Bayesian Optimization The paper introduces the reader to Bayesian optimization, highlighting its methodical aspects and showcasing its applications. By Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas
[3] Decentralizing Privacy: Using Block chain to Protect Personal Data Guy Zyskind MIT Media Lab Cambridge, Massachusetts Email: guyz@mit.edu Oz Nathan Tel-Aviv University Tel-Aviv, Israel Email: oznathan@gmail.com Alex ’Sandy’ Pentland MIT Media Lab Cambridge, Massachusetts Email: pentland@mit.edu
[4]Analytics vidya ( website)
Available: https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/
[5] Digital doughnut (website ) Available: https://www.digitaldoughnut.com/articles/2017/june/machine-learning-accelerates-transformation
[6] Matlab&Simulink (website)
Available:
https://www.mathworks.com/discovery/machine-learning.html
[7] kdnuggets (website)
Available:
https://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-engineers.html
[8] dezyre (website)
Available:
https://www.dezyre.com/article/top-10-machine-learning-algorithms/202

G. Shobana, K. Pradeepa, D. Subashree , "Harnessing the power of Machine Learning for Automating the Repetitive Tasks", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.108-112, 2018.

In recent times, there is a huge increase in the usage of social media to share one’s opinion, feelings and even daily activities. By predicting the mood of the users in social media, we can identify the users who discuss or express suicide-related information. Prediction of user’s mood based on the likes, shares and status posted by them on social media is a challenging task as the mood of users change frequently. In this paper, a scheme is proposed to predict the user’s mood based on the likes, shares and status posted in social media and identify the users in the state of depression. This scheme classifies the mood of user as happy, sad, neutral, angry etc. using deep learning. It presents news feeds to keep the depressed user happy and enthusiastic. When the user is in a prolonged state of depression, the alert system alerts the top five users in his/her friend list. This scheme predicts the mood of the users with accuracy around 87%. Further, time critical information is sent to some users who regularly share information such that it reaches all the users within a certain period of time.

Mood Prediction, Social Media, Alert System, Time Critical Information, Depression, News Feed

[1] M. Roshanaei, R. Han, S. Mishra, “Features for mood prediction in social media”, In the proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1580-1581, 2015.
[2] A. Hepburn, “Facebook statistics, stats & facts for 2011”, Digital Buzz, Accessed 31 January 2018.
[3] D. Noyes, “The top 20 valuable Facebook statistics”, Zephoria, Florida, Accessed 10 February 2018.
[4] P. Chiranjeevi, V. Gopalakrishnan, P. Moogi, “Neutral face classification using personalized appearance models for fast and robust emotion detection”, IEEE Transactions on Image Processing ,vol. 24, no. 9, pp.2701-2711, 2015.
[5] G. T. Giancristofaro, A. Panangadan, “Predicting Sentiment towards Transportation in Social Media using Visual and Textual Features”, In the proceedings of the 19th IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 2113-2118, 2016.
[6] Md. Z. Uddin, W. Khaksar, J. Torresen, “Facial Expression Recognition Using Salient Features and Convolutional Neural Network”, IEEE Access, vol. 5, pp.26146-26161, 2017.
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[8] A. Cernian, A. Olteanu, D. Carstoiu, C. Mares “Mood Detector – On Using Machine Learning to Identify moods and Emotions”, In the proceedings of the 21st International Conference on Control Systems and Computer Science, pp. 213-216, 2017.
[9] Z. Zhu, H. F. Satizabal, U. Blanke, A. Perez-Uribe, G.Troster “Naturalistic Recognition of Activities and Mood Using Wearable Electronics”, IEEE Transactions on Affective Computing, vol. 7, no.3, pp.272-285, 2016.
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[15] L.Breiman, “Bagging predictors”, Machine learning, vol.24, no.2, pp.123-140, 1996.
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M.K.Sandhya, V.Soundarya, R.Swarnalakshmi, I.Swathi, "Prediction of Social Media User’s Mood using Deep Learning", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.113-119, 2018.

The task of dealing with human body is one of the most complex one especially when it comes to curing someone suffering from heavy torment of malignant tumors. Cancer therapies are currently limited to surgery, radiation, and chemotherapy. All three methods risk damage to normal tissues or incomplete eradication of the cancer. Computational methods involves the development and application of data-analytical and theoretical methods, and computational techniques to the study of biological, and behavioral systems, and with Nanotechnology it offers the means to target chemotherapies directly and selectively to cancerous cells and neoplasms, guide in surgical resection of tumors, and enhance the therapeutic efficacy of radiation-based and other treatment modalities. All of this can add up to a decreased risk to the patient and an increased probability of survival. “Nano programmed carriers” or “NPC” carrying effective drugs are deployed in the blood stream. They are programmed to hit only the right spot through DNA computing which is interface to biochemical processes and are highly directional. On the other hand cancer abrupt can be predicted through Cancer computational biology. It aims to determine the future mutations in cancer through an algorithmic approach .It allows the gathering of data points using nano programmed robots and other sensing devices. This data is collected from DNA, RNA, and other biological structures. Areas of focus include determining the characteristics of tumors, analyzing molecules that are Involved in causing cancer, and understanding how the human genome relates to the causation of cancer

nano programmed carriers, nanotechnology, programmed bio sensors, CAS, cancer computational biology

[1] R. Weissleder, M.C. Schwaiger, S.S. Gambhir, H. Hricak, “Imaging approaches to optimize molecular therapies”, Science Translational Medicine, Vol. 8, Issue. 355, pp. 355-371, 2016.
[2] Phillips E, Penate-Medina O, Zanzonico PB, Carvajal RD, Mohan P, Ye Y, Humm J, Gönen M, Kalaigian H, Schöder H, Strauss HW, Larson SM, Wiesner U, Bradbury MS, “Clinical translation of an ultrasmall inorganic optical-PET imaging nano particle probe”, Science Translational Medicine, Vol. 6, Issue. 260, pp. 260ra149, 2016.
[3] Haun JB, Castro CM, Wang R, Peterson VM, Marinelli BS, Lee H, Weissleder R. , “Micro-NMR for rapid molecular analysis of human tumor samples”, Science Translational Medicine, Vol. 3, Issue.71, pp.71ra16, 2011.
[4] Maeda H1, Nakamura H, Fang J, “The EPR effect for macromolecular drug delivery to solid tumors: Improvement of tumor uptake, lowering of systemic toxicity, and distinct tumor imaging in vivo”, Advanced Drug Delivery Reviews, Vol. 65, Issue. 1, pp. 71-79, 2013.
[5] Bertrand, N., Wu, J., Xu, X., Kamaly, N. & Farokhzad, O. C. "Cancer nanotechnology: the impact of passive and active targeting in the area of modern cancer biology" Advanced Drug Delivery Reviews, Vol.66, pp. 2–25, 2014.
[6] C He, X Duan, N Guo, C Chan, C Poon, RR Weichselbaum, W Lin, “Core-shell nano scale coordination polymers combine chemotherapy and photodynamic therapy to potentiate checkpoint blockade cancer immunotherapy

Smith S Trivedi, "Computational Biology- Nano Programmed Carrier and Computed Aided Surgical for Cancer", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.120-122, 2018.

Cloud is an online storage application mainly used to store documents, media and various files. However, due to large and easy access of cloud there arise various security issues such as data stealing and authentication issues when trying to share data on public platform. Fine-grained sharing of encrypted data is achieved through Attribute-based encryption. This is done with a methodology to provide data confidentiality and integrity using key generation, encryption and decryption. There are various techniques used to share data in cloud and each of these techniques employ different procedures or steps to achieve the end result. One among those techniques are public key encryption techniques which is a form of asymmetric key encryption where both public and private keys are used to keep the data or document secure. A security model is built through verifiable decryption algorithm. This is achieved by introducing a verification key from the output of the encryption algorithm. Finally, we present an approach to securely share data in an efficient manner through the ABE scheme.

cloud computing, attribute-based encryption, Key Exchange, Data Sharing

[1] Yifeng Zheng, Xingliang Yuan, Xinyu Wang, Jinghua Jiang, Cong Wang, Xiaolin Gui, “Towards Encrypted Cloud Media Center with Secure Deduplication” IEEE Transactions on Multimedia, Vol.19, Issue.2, 2017.
[2] Nicolae Paladi, Christian Gehrmann, Antonis Michalas, “Providing User Security Guarantees in Public Infrastructure Clouds” IEEE Transactions on Cloud Computing, Vol.5, Issue.3, 2017.
[3] Jianghong Wei, Wenfen Liu, Xuexian Hu, “Secure Data Sharing in Cloud Computing Using Revocable-Storage Identity-Based Encryption” IEEE Transactions on Cloud Computing, Vol.PP, Issue.99, 2015. doi: 10.1109/TCC.2016.2545668
[4] Mazhar Ali,Revathi Dhamotharan,Eraj Khan,Samee U.Khan,Athanasios V.Vasilakos,Keqin Li and Albert Y.Zomaya, “SeDaSC: secure data sharing in clouds” IEEE Systems Journal, Vol.11, Issue.2, 2017.
[5] Mazhar Ali,Saif U.R.Malik and Samee U.Khan, “DaSCE: Data Security for Cloud Environment with Semi-Trusted Third Party” IEEE Transactions on Cloud Computing Vol.5, Issue.4, 2017.
[6] Ruixuan Li; Chenglin Shen; Heng He; Zhiyong Xu; Cheng-Zhong Xu, “A Lightweight Secure Data Sharing Scheme For Mobile Cloud Computing” IEEE Transactions on Cloud Computing, Vol.PP, Issue.99, 2017. doi: 10.1109/TCC.2017.2649685
[7] Jian Shen , Tianqi Zhou, Xiaofeng Chen, Jin Li, and Willy Susilo, “Anonymous And Traceable Group Data Sharing In Cloud Computing” IEEE transactions on information forensics and security, Vol.13, Issue.4, 2018.
[8] Joseph K. Liu, Man Ho Au,Xinyi Huang, Rongxing Lu, and Jin Li, “Fine-Grained Two-Factor Access Control For Web-Based Cloud Computing Services” IEEE Transactions on Information Forensics and Security, Vol.11, Issue.3, 2016.
[9] Bernardo Ferreira, Joao Rodrigues, Joao Leitao, Henrique Domingos, “Practical Privacy-Preserving Content-Based Retrieval In Cloud Image Repositories” IEEE Transactions on Cloud Computing, Vol.PP, Issue.99, 2017. doi: 10.1109/TCC.2017.2669999
[10] Ghassan O. Karame, Claudio Soriente, Krzysztof Lichota, Srdjan Capkun, “Securing Cloud Data Under Key Exposure” IEEE Transactions on Cloud Computing, Vol.PP, Issue.99, 2017. doi: 10.1109/TCC.2017.2670559
[11] Sikhar Patranabis, Yash Shrivastava, Debdeep Mukhopadhyay, “Provably Secure Key-Aggregate Cryptosystems With Broadcast Aggregate Keys For Online Data Sharing On The Cloud”
IEEE Transactions on Computers, Vol.66, Issue.5, 2017.
[12] Baodong Qin, Robert H. Deng, Shengli Liu, Siqi Ma, “Attribute-Based Encryption With Efficient Verifiable Outsourced Decryption” IEEE Transactions On Information Forensics And Security, Vol.10, Issue.7, 2015.

S. Nivetha, R. Sowmya, B. Monica Jenefer, "A Survey on Sharing Data in Cloud", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.123-126, 2018.

The main aim is to survey all the necessary papers that helps in building a system that spreads awareness among the people during an epidemic to reduce its severity by giving them general information regarding the epidemic diseases and predictions about its severity. There is no proper information among the public regarding the symptoms, precautions and treatment information of any epidemic disease during its spread which leads to unnecessary panic among the public. An android application specially tailored to provide prompt information to the public during the spread of an epidemic disease will be an efficient solution to diminish its spread. This application uses state of the art technology like firebase cloud messaging, firestore, python web scraper and parsehub to accomplish its goal. Thus, the proposed system has all the necessary features to create appropriate awareness among the public during the spread of any epidemic thereby reducing unwanted panic leading to reduction in the severity of the disease.

E-health, firebase cloud messaging, firestore, web scraper, parsehub

[1] Christoforos, Hadjichrysanthou, Kieran, J.Sharkey, “Epidemic control analysis: Designing targeted intervention strategies against epidemics propagated on contact networks”, Journal of Theoretical Biology Volume 365, 21 January 2015

[2] Fabon Dzogang, Thomas Lansdall-Welfare, FindMyPast Newspaper Team, Nello Cristianini, “Discovering Periodic Patterns in Historical News”, PLOS ONE, Volume 11, id e0165736 (2016).

[3] Huijuan Wang, Chuyi Chen, Bo Qu1, Daqing Li and Shlomo Havlin, “Epidemic mitigation via awareness propagation in communication networks: the role of time scales”, l 2017 New Journal Physics.

[4] LixiaZuo and MaoxingLiu, "Effect of Awareness Programs on the Epidemic Outbreaks with Time Delay", in Abstract and Applied Analysis 2014

[5] Michael C. Smith, David A. Broniatowski, Michael J. Paul, Mark Dredze, "Towards Real-Time Measurement of Public Epidemic Awareness: Monitoring Influenza Awareness through Twitter",2015, Association for the Advancement of Artificial Intelligence

[6] MuhannadQuwaider, YaserJararweh, “Multi-tier cloud infrastructure support for reliable global health awareness system”, Simulation Modelling Practice and Theory,2016

[7] NehaSrivastava, Uma Shree, Nupa Ram Chauhan, Dinesh Kumar Tiwari, "Firebase Cloud Messaging (ANDROID)", International Journal of Innovative Research in Science, Engineering and Technology 2017

[8] Nicholas Thapen, DonalSimmie, Chris Hankin, Joseph Gillard, "DEFENDER: Detecting and Forecasting Epidemics Using Novel data-Analytics for Enhanced Response" Journal Plos one 2016

[9] Peninah M. Munyua, R. MbabuMurithi, Peter Ithondeka, Allen Hightower, Samuel M. Thumbi, Samuel A. Anyangu, JusperKiplimo, Bernard Bett, Anton Vrieling, Robert F. Breiman, M. KariukiNjenga, "Predictive Factors and Risk Mapping for Rift Valley Fever Epidemics in Kenya",,Journal Plos one 2016

[10] Sebastian Funk, ErezGilad, Chris Watkins and Vincent A. A. Jansen, "Spread of awareness and its impact on Epidemic Outbreaks", PNAS 2009 April, 106 (16) 6872-6877.

[11] Simon Dellicour, Rebecca Rose and Oliver G. Pybus, "Explaining the geographic spread of emerging epidemics: a framework for comparing viral phylogenies and environmental landscape data”, BMC Bioinformatics, 2016

R. Dhanalakshmi, Suprajah S, Swetha S, Vaishnavi Kanna S, "State of the Art Technologies to Broadcast Epidemic Awareness using Web Crawling", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.127-131, 2018.

Special Relativity prohibits masses from moving faster than the speed of light. Einstein makes a plausibility argument for this, illustrating that time would appear to go backward at super luminal velocities. However, this argument includes nothing from special relativity, and only contains the assumption that light travels at a finite speed. Thus, we may use the Galilean transformation along with this assumption so as to avoid the imaginary time problem at super luminal speeds. In this document we run a computer simulation of the observation of a clock from two distinct inertial frames. We run a relativistic simulation as well as a non-relativistic simulation. We compare the two and observe a clue to time dilation inherent in non-relativistic mechanics. Besides this, some interesting qualitative observations are made. Finally, for super luminal velocities, we use only the Galilean transform, and make observations, keeping in mind Einstein’s argument.

Relativity, Einstein, Galileo, simulation, time travel, special relativity

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Pitambar Sai Goyal, "Can a computer simulation determine what happens at super luminal speeds?", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.132-134, 2018.

This paper explores the concept of software and user data portability by tackling device driver issues caused due to diversity in computer hardware. The objective of this paper is to implement a universal interface between the hardware and the software intended to minimize time and manpower. Drivers are required for software-hardware integration and different hardware manufactured by OEMs require device drivers designed specifically for the hardware component. Development of a new platform requires the software manufacturer to target a variety of hardware. Upgrading existing software requires the device drivers to be checked if they are compatible with the newer version of the software. Device drivers have to be written for each software platform separately. This results in a lot of time being consumed which directly affects the consumer such as delayed software and firmware updates, security patches, bug fixes etc. This interface addresses the driver development issues by providing a standard hardware platform for which the software can be developed.

software, hardware, portability, platform, interface, drivers, compatibility

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Pon Rahul M, Rishi Shree S, "Enhancement of Software and Data Portability by Normalizing Variations in Hardware", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.135-140, 2018.

Now-a-days in any business field we are hearing about the word ‘competition’. So, by competitive analysis we can analyze the competitors and can assess the strengths and weakness of a competitor. Competition is necessary in marketing to know which companies are primary competitors and also know which company is competing with itself. So by this we make our products, services and marketing stands out well in business. Competitiveness between two items can be defined based on market segments that they can both cover. Competitiveness is evaluated in large review datasets and address the problem of finding top-k competitors. For evaluating of competitiveness, it utilizes customer reviews which are abundantly available in wide range of domains. There are so many efficient methods for addressing the problem of finding top-k competitors in terms of scalability, accuracy.

Data Mining, Competitor Mining, Competitors, Information search and retrieval

[1] Valkanas, George, Theodoros Lappas, and Dimitrios Gunopulos."Mining Competitors from Large Unstructured Datasets." IEEE Transactions on Knowledge and Data Engineering vol.29,Issue 9,pp 1971-1984, 2017.
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[13] Kumar, B. Senthil, and Nisha Joseph. "A Review on Competitor Mining and Unstructured Dataset Handling Techniques." Journal of Network Communications and Emerging Technologies (JNCET) vol.7, no. 9,pp22-26 2017.

B.Lasya Reddy, Shaik Salam, "A Review on Mining Large Unstructured Datasets to Find Top-K Competitors", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.141-143, 2018.

Sequential pattern mining is the imperative data mining problem with expansive application from text analysis to market basket analysis. It is the way towards extricating certain sequential patterns whose support surpasses a predefined limit which is defined by the user according to their interest. With frequent pattern mining, pattern is viewed as fascinating if its event surpasses users determined limit. Notwithstanding, users interest may identify with numerous components that are not really communicated as far as the event recurrence. Since the quantity of sequences can be huge, and users have distinct interest and prerequisites, to get the most fascinating sequential pattern, generally a minimum base support is predefined by clients. Utility mining is a new advancement of data mining innovation. It developed as of late to address the confinement of frequent pattern mining by thinking about the client`s desire or objective and in addition the crude information. An efficient algorithm is to be developed for extracting high utility sequential patterns.

Data mining, Frequent Pattern Mining, High Utility Itemset mining, sequential pattern mining

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D. Divyashree, G. Sunitha, "A Review On High Utility Itemset Mining", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.144-147, 2018.

We live in an era of digital data explosion over Internet. Data warehouses deal with numerical databases than textual sources. Nearly eighty percent of digital data is either in semi or un-structured textual form. Several knowledge mining techniques developed over the past decade and those that are being developed now continue to draw attention to transform such textual data into desirable information and useful knowledge. This knowledge and information is used to benefit many fields of applications such as: social network, business management, customer care management system, market analysis, search engines, fraud detection, just to name a few. Text Mining (TM) is what is needed if desired information is to be obtained from such voluminous data. TM is multi-disciplinary in nature. Several TM techniques are deployed in the process of extracting knowledge from textual sources. Input text for such techniques needs to be pre-processed and cleaned. This survey briefly presents pre-processing tools for TM in general and Natural Language Processing (NLP) in particular. Also presents the broad categories of TM techniques used. The focus of this paper is to explore and analyze several features of text preprocessing techniques and tools that would interest researchers in the area of TM.

Text Mining, Pre-processing techniques, Pre-processing Tools, Natural Language Processing

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Ravi Lourdusamy, Stanislaus Abraham, "A Survey on Text Pre-processing Techniques and Tools", International Journal of Computer Sciences and Engineering, Vol.06, Issue.03, pp.148-157, 2018.