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Internet of Things is an innovative computerized system which is used for several applications across industries through their flexibility and capabilities. Independently living old age population is increasing now a days. They are requiring medical assistance in time, really it is a big issue. Continuous monitoring is an essential process to solve this issue. Medical devices have been enriched by incorporating cyber and physical capabilities to provide better health care services. With the help of wireless communication protocols, the IoT provide the interconnection facilities between the medical devices and other monitoring hardware. Supporting with the technology and smart devices, the IoT applications perform data collection, automation, and operations. IoT play a vital role in improving the health, safety and care of patients through healthcare monitoring system. Through the healthcare monitoring system the patient getting immediate help from the medical persons. In addition they may get medical assistance and recommendations also. This paper presents the strengths, weaknesses, challenges and overall suitability for a wearable IoT health care system.

Internet of Things, Healthcare monitoring system, Wearable devices, Wireless sensor networks

[1]. J. Höller, V. Tsiatsis, C. Mulligan, S. Karnouskos, S. Avesand, and D. Boyle, “From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence. Amsterdam, The Netherlands”: Elsevier, 2014.
[2]. Sapna Tyagi,Amit Agarwal,Piyush Maheshwari from “A conceptual framework for IoT-based healthcare system using cloud computing”,2016 IEEE
[3]. P. Middleton , P. Kjeldsen and J. Tully, "Forecast: The Internet of Things, Worldwide," Gartner, 2013
[4]. Dr. M.Umashankar “Privacy Preserving Healthcare System using secured cloud environment “ International Journal Of Engineering And Computer Science ISSN:2319- 7242 Volume 6 Issue 6 June 2017, Page No. 21814-21817
[5]. Adithkiran Bandarkavte, Jagadeesh Sai. D, “NFC Based Secure HealthCare Monitoring System” , International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issue 4, April 2016
[6]. Ahmed E. Youssef, “A Framework For Secure Healthcare Systems Based On Big Data Analytics In Mobile Cloud Computing Environments”, International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.2, June 2014 .
[7]. Mrs. Ramya.R, Mrs.Shruthi.G, “Privacy Preserving Of Health Monitoring Services In Cloud”, International Journal of Advance Research In Science And Engineering IJARSE, Vol. No.3, Issue No.6, June 2014
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[9]. S. Gusmeroli, S. Piccione, D. Rotondi, “A capability-based security approach to manage access control in the internet of things”, Mathematical and Computer Modelling 58 (5) (2013) 1189 .
[10]. Annlin JebaS.V., 2Gnana KingD.R, “Combining Trust with Authentication Information for Routing in Wireless Sensor Networks”, International Journal od Scientific Research inNetwork Security and Communication, Vol.6, Issue.5, pp.38-46, Oct-2018.
[11]. Bhanudas Suresh Panchabhai, Anand Jayantilal Maheshwari, Sunil DhonduMone ,“ The Road Map of Cloud Computing to Internet of Things”, Vol.06 , Special Issue.01 , pp.37-42, Jan-2018

M. Umashankar , "The Role of Internet of Things in the Healthcare Industry," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.730-733, 2019.

Stress management systems play a vital role in detecting the stress levels that disrupts an individual socioeconomic lifestyle. According to the World Health Organization (WHO), stress refers the mental health problem that affects the life of an individual. The stress levels can be measured based on the questionnaire by medical and physiological experts. This method fully depends on the answers given by individuals to detect whether they are stressed or not. During the past decades, Electrodermal Activity (EDA) analysis has been performed to measure the changes in the electrical conductivity of the skin. The changes in EDA may be produced by different physical and emotional stimuli that trigger variations in sweat-gland activity. To measure the changes in EDA, different sensors were also designed and many techniques have been developed to analyze the human stress. This paper presents a detailed survey of human stress detection based on EDA analysis to detect their stress levels. Initially, different stress detection methods using EDA analysis are studied in brief. Then, a comparative analysis is conducted to understand the drawbacks in those methods and suggest a new solution to enhance the stress and emotional monitoring system with high accuracy

Stress management, Electrodermal activity, Skin conductance response, Skin conductance level, Electrodermal level

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[3] Chaitanya Gupte, Shruti Gadewar, "Diagnosis of Parkinson’s Disease using Acoustic Analysis of Voice", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.14-18, 2017.
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[5] C. Kappeler-Setz, F. Gravenhorst, J. Schumm, B. Arnrich, G. Tröster, “Towards long term monitoring of electrodermal activity in daily life”, Personal and Ubiquitous Computing, Vol.17, Issue.2, pp.261-271, 2013.
[6] I. V. Bornoiu, O. Grigore, “Kohonen Neural Network Stress Detection Using Only Electrodermal Activity Features”, Advances in Electrical and Computer Engineering, Vol.14, Issue.3, pp.71-78, 2014.
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[8] A. Sano, R. W. Picard, R. Stickgold, “Quantitative analysis of wrist electrodermal activity during sleep”, International Journal of Psychophysiology, Vol.94, Issue.3, pp.382-389, 2014.
[9] T. Chaspari, A. Tsiartas, L. I. Stein, S. A. Cermak, S. S. Narayanan, “Sparse representation of electrodermal activity with knowledge-driven dictionaries”, IEEE Transactions on Biomedical Engineering, Vol.62, Issue.3, pp.960-971, 2015.
[10] P. Ghaderyan, A. Abbasi, “An efficient automatic workload estimation method based on electrodermal activity using pattern classifier combinations”, International Journal of Psychophysiology, Vol.110, pp.91-101, 2016.
[11] Sumitra Das, Deepak Gupta, "Perfomance Analysics of Human Gait Recognition Based on Principal Component", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.5, pp.12-15, 2016.
[12] L. Giromini, R. Morese, A. Salatino, M. Di Girolamo, D. J. Viglione, A. Zennaro, “Rorschach Performance Assessment System (R-PAS) and vulnerability to stress: A preliminary study on electrodermal activity during stress”, Psychiatry Research, Vol.246, pp.166-172, 2016.
[13] R. Zangróniz, A. Martínez-Rodrigo, J. M. Pastor, M. T. López, A. Fernández-Caballero, “Electrodermal Activity Sensor for Classification of Calm/Distress Condition”, Sensors, Vol.17, Issue.10, p.2324, 2017.
[14] S. Jain, U. Oswal, K. S. Xu, B. Eriksson, J. Haupt, “A compressed sensing based decomposition of electrodermal activity signals”, IEEE Transactions on Biomedical Engineering, Vol.64, Issue.9, pp.2142-2151, 2017.

P. Sudarsan, A. Niranjil Kumar, "A Survey on Human Stress Monitoring Technique using Electrodermal Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.734-737, 2019.

Crop growing is measured as the stamina of India, is the improvement of plant for foodstuff, bio-fuel, counteractive plants and other harvest for behind and enhancing human life. Farming is an unique business crop creation which is contingent on different attributes such as soil, climate, irrigation, precipitation, insect killer weeds, fertilizers, nurturing, temperature, harvesting and other factors. An accurate crop yield prediction helps support decision makers in the agriculture sector to envisage the yield effectively. Data mining techniques play a vital role in the study of data for crop yield prediction. Data mining is the computing method of discovering patterns in hefty datasets involving methods at the connection of machine learning, artificial intelligence, record and system database. This piece of writing presents a detailed examination of various techniques planned for crop yield prediction. At first, dissimilar techniques developed by previous researchers are calculated in detail. Then, a relative analysis is carried out to know the precincts of each technique and afford a suggestion for further enhancement in crop yield prediction successfully.

Agriculture, crop yield prediction, data mining, machine learning technique

[1] Bhanose, S. S., Bogawar, K. A., Dhotre, A. G., & Gaidhani, B. R. “Crop and Yield Prediction Model”, International Journal of Advance Scientific Research and Engineering Trends, 1(1), pp.23-28, 2016.
[2] Ramesh, D., & Vardhan, B. V. “Analysis of crop yield prediction using data mining techniques”, International Journal of Research in Engineering and Technology, 4(1), pp.47-473, 2015
[3] Gandhi, N., Armstrong, L. J., & Petkar, O. “Predicting Rice crop yield using Bayesian networks”, In Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on pp. 795-799, 2016.
[4] Shastry, K. A., Sanjay, H. A., & Deshmukh, A. “A Parameter Based Customized Artificial Neural Network Model for Crop Yield Prediction”, Journal of Artificial Intelligence, vol,9, pp. 23-32, 2016.
[5] Pantazi, X. E., Moshou, D., Alexandridis, T., Whetton, R. L., & Mouazen, A. M. “Wheat yield prediction using machine learning and advanced sensing techniques”, Computers and Electronics in Agriculture, 121, pp.57-65, 2016.
[6] Wang, J., Li, Z., Qin, X., Yang, X., Gao, Z., & Qin, Q. (2014, July). “Hyperspectral predicting model of soil salinity in Tianjin costal area using partial least square regression”, In Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International pp. 3251-3254, 2014.
[7] Liu, H. L., Liu, H. B., Lei, Q. L., Zhai, L. M., Wang, H. Y., Zhang, J. Z., ... & Liu, X. X. “Using the DSSAT model to simulate wheat yield and soil organic carbon under a wheat-maize cropping system in the North China Plain” Journal of Integrative Agriculture, 16(10), pp. 2300-2307, 2017.
[8] Garg, B., Aggarwal, S., & Sokhal, J. “Crop yield forecasting using fuzzy logic and regression model”. Computers & Electrical Engineering, 2017.
[9] Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., ... & Kim, S. H. “Random forests for global and regional crop yield predictions”, PLoS One, 11(6), e0156571. 2016.
[10] Villanueva, M. B., & Salenga, M. L. M. “Bitter Melon Crop Yield Prediction using Machine Learning Algorithm”, International Journal of Advanced Computer Science and Applications (IJACSA), 9 (3), pp.1-6, 2018
[11] Brdar, S., Culibrk, D., Marinkovic, B., Crnobarac, J., & Crnojevic, V. (2011, “Support vector machines with features contribution analysis for agricultural yield prediction. In Second International Workshop on Sensing Technologies in Agriculture”, Forestry and Environment (EcoSense 2011), Belgrade, Serbia pp. pp.43-47, 2011.
[12] Huang, X., Huang, G., Yu, C., Ni, S., & Yu, L. “A multiple crop model ensemble for improving broad-scale yield prediction using Bayesian model averaging”, Field Crops Research, 211, pp.114-124, 2017.
[13] Asha Patil, Kalyani Patil, Kalpesh Lad, “Leaf Disease detection using Image Processing Techniques”, International Journal of Scientific Research in Computer Science and Engineering” Vol:6 pp.33-36, 2018.

R. Beulah , "A Survey on Different Data Mining Techniques for Crop Yield Prediction," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.738-744, 2019.

Many software improvement agencies attempt to beautify the productivity of their builders. All too regularly, efforts geared towards increasing developer productiveness are undertaken without a proper knowledge of how exactly builders spend their time at their work and how it impacts their own belief of productivity. Verifying earlier findings, we try to found that developers pay their time on a good type of tasks and switch frequently among them, succeeding in particularly fragmented work. Our findings enlarge past existing studies therein we tend to correlate builders’ work conduct with perceived fecundity. Although productiveness is based on individuals, developers may be roughly gathered in morning sessions, low at lunch and afternoon. A continuous linear regression per participant found that greater grade persons usually use a high-quality, and emails, deliberate meetings and unrelated web sites with a terrible belief of productivity. We discuss opportunities of our findings, the capability to expect high and occasional productiveness and endorse layout tactics to create higher tool guide for planning builders’ workdays and enhancing their work productivity.

quantification, perceived productivity, regression

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J Rajeshwar, "An Approach to Quantify the Productivity of Software Developers towards the Perceived Productivity," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.745-748, 2019.

Current socio-economic situations around the world have seen a drastic rise in crime rate all over the world, which proves difficult to control for any type of authorities and most of the resources are wasted on crimes which have been already committed. The emphasis of our system is to stop the crime before it is even anticipated ensuring safety of a loved individual and also saving the extra resources the authorities may encounter. Our system proposes an immediate live assistance in case of an emergency by special armed task force designed to encounter dangerous situations and kill the crime in the cradle.

IoT, Arduino, GPS, GSM, Safety, Live Assistance, Tracking, Cloud, Police alert

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[5] S N Gadhave “Electronic jacket for Women Safety” , International Journal of Application Innovation in Engineering in Engineering Management, Volume: 04 Issue: 05 May 2017.
[6] E M Abd, “GIS based Decision support system for criminal tracking”, Institute of Electrical and Electronics Engineers, 978-1-4673-2824-1/12/31.00 , 2012.
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[8] S P Purnachandran, “Context Aware Public Safety in Pervasive Environment Wireless Public Safety Networks”, International Journal of Application or Innovation in Engineering Management, Pages 99-111, 2017.

P. V. Katkhede, V. Todmal, D. Pandey, A. Saini, B.A. Patil, "Live Assistance And Tracking System(LATS) using Cloud and IoT," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.749-751, 2019.

Data mining has effectively and tremendously enhanced the service in diverse areas, such as health care, business analysis, and social media. It is used to extract useful information from a huge volume of data by using various techniques like pre-processing, feature extraction, feature selection, and classification. One of the important research issues of the data mining and machine learning is a classification model. This model is to learn a classifier from a given trained dataset to predict the class of test dataset. Decision trees have become one of the most well-known classification methods for extracting classification rules from data, on account of their excellent learning capability. This especially focuses on to examine the various decision tree techniques to support data mining environments. The main objective of this survey is to study different decision tree methods used for detecting and solving classification issues. Finally, comparisons are made for different decision tree techniques in data mining

Data mining, Decision tree, Classification, Knowledge extraction, Machine learning

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V. Nirmala, A. Nithya, "A Survey on Different Decision Tree Methods for Solving Classification Issues," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.752-756, 2019.

Speech is that the essential and therefore the most profitable ways for correspondence between people. Speech is an emerging technology and automatic speech recognition has created advances in recent years. It provides the flexibility to a machine for responding properly to spoken language. Keyword Spotting could be a very important strategy in audio mining that is employed to recover of all occurrences of a given keyword within the knowledge talked expressions. It has transformed into a fascinating and testing zone as the proportion of an audio substance in the web, telephone and diverse sources growing rapidly. It can be viewed as a subproblem of automatic speech recognition where only partial information has got to be extracted from speech utterances. KWS is closely associated with the task of speech transcription and offers several advantages for certain applications. The main aim of this study is to understand the various approaches used for keyword spotting of speech in order that we can find out the methods that provide better accuracy and performance. Additionally, we have quickly examined the Keyword spotting framework and Audio mining system in this paper

Audio Mining, Keyword Spotting, Automatic Speech Recognition, Audio Indexing

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B.K. Deka, P. Das, "A Review of Keyword Spotting as an Audio Mining Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.757-769, 2019.

A video is the most powerful medium in the propagation of information and important part of the video for exchanging the information is audio, which is an important aspect of the video on which the whole message depends and as it is used in all field like Teaching, Entertainment, Conference Meeting, News Broadcast. So converting the Audio into Text in Documented format make easy for referring purpose as it is difficult to search the said word in the video as compared to the transcript. The main objective of developing this system is to present an automated way to generate the transcript for audio and video. As it is not possible to make the same informative video in all Languages. So this the place where our System plays an important role. It will extract the audio from the given video and transcript is generated based on which it can be translated into any desired language. It can be very useful for people who speak the language which is not used by the majority of the population. In this way, it has much application in all field where information exchange is happening based on Video

Neural Network, Audio extraction, Speech recognition, Time synchronization, Automatic Transcript generation, Natural language processing, Connectionist Temporal Classification (CTC), Hidden Markov Model (HMM).

[1]Houssem chamber, Marlon Oliveira, Kevin McGuinness, Suzanne Little, Keisuke Kameyama
“Educational video classification by using a transcript to image transform and supervised learning.”
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[10] Nikolas Lee, Jia Wern Yong,”Automated Transcript Generation for the video conferences” 11-Nov 2017
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Fatima Ansari,Ramsakal Gupta, Uday Singh, Fahimur Shaikh, "Transcripter-Generation of the transcript from audio to text using Deep Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.770-773, 2019.

Medical image processing is a highly challenging field. Medical imaging techniques are used to image the inner portions of the human body for medical diagnosis. MR images are widely used in the diagnosis of brain tumor. In this paper, we present an automated method to detect and segment the brain tumor regions. The proposed method consists of three main steps: initial segmentation, modeling of energy functions and optimize the energy function. To make our segmentation more reliable we use the information present in the T1 and FLAIR MRI images. We use Conditional random field (CRF) based framework to combined the information present in T1 and FLAIR in probabilistic domain. A main advantage of CRF based framework is we can model complex shapes easily and we incorporate the observations in energy function

Conditional random field (CRF), Fuzzy-C-Means algorithm, Fuzzy C Means Clustering Algorithm

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Vulavabeti Raghunath Reddy, Shaik Anusha, K Ravindra Reddy, "Brain Tumor Detection and Segmentation Using Conditional Random Field," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.774-779, 2019.

Computer security is the protection of computing systems and the data that is stored or accessed. It is very important to ensure that the information remains confidential and only those who should access that information can. Username and password alone are insufficient in complex applications. Hence, a strong authentication method such as Biometric authentication method is required to verify one’s identity using the unique biological characteristics of an individual. Existing security approaches can be strengthened by one of the behavioral biometric based technique known as Keystroke Dynamics. The main objective of this paper is to explore particularly on Keystroke Dynamics based Authentication (KDA). This technique can be applied in different domains like intrusion detection, online learning and assessment, e-banking etc., to authenticate the users. This paper presents a review of its applications using fixed-text (static passwords) and free-text (continuous). Comparing these two types of keystroke authentication methods, Free-text kind of authentication process was found to be better as it is not limited with username and password during the log-in session; it is continued until the end of the log-on session.

Authentication, Keystroke dynamics, Behavioral Biometrics, Fixed Text, Free text, Biometric authentication

[1] M. L. Ali, J. V. Monaco, C. C. Tappert, and M. Qiu, “Keystroke Biometric Systems for User Authentication,” Journal of Signal Processing Systems, vol. 86, no. 2–3, pp. 175–190, 2017.
[2] A. Alsultan and K. Warwick, “Keystroke Dynamics Authentication: A Survey of Free-text Methods,” International Journal of Computer Science, vol. 10, no. 4, pp. 1–10, 2013.
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[4] A. Alshehri, F. Coenen, and D. Bollegala, “Iterative Keystroke Continuous Authentication: A Time Series Based Approach,” KI - Künstliche Intelligenz, pp. 1–13, 2018.
[5] J. Kim, H. Kim, and P. Kang, “Keystroke dynamics-based user authentication using freely typed text based on user-adaptive feature extraction and novelty detection,” Applied Soft Computing Journal, vol. 62, pp. 1077–1087, 2018.
[6] I. Tsimperidis, A. Arampatzis, and A. Karakos, “Keystroke dynamics features for gender recognition,” Digital Investigation, vol. 24, pp. 4–10, 2018.
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[24] J. Roth, X. Liu, A. Ross, and D. Metaxas, “Biometric authentication via keystroke sound,” Proceedings - 2013 International Conference on Biometrics, ICB 2013, 2013.

M. Rathi, A. V. Senthil Kumar, "Exploration of Keystroke Dynamics Based Authentication on Fixed-Text and on Free-Text," International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.780-785, 2019.