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Human action recognition (HAR) from videos is a significant and has more research focus in the domain of Computer vision. The purpose of human action recognition in videos is to detect and recognize the human actions from the sequence of frames. Human action recognition undertakes many difficulties such as differences in human shape, cluttered background, moving cameras, illumination conditions, motion, occlusion, and viewpoint variations. In previously, local features or deep learned features are used to recognize the action. In the proposed work, both the features are used to recognize action and for analysis. From sequences of frames background is subtracted using Multi-frame averaging method. Two kinds of feature extraction are done. Shape based feature extraction, Optical flow feature extraction are some of the hand-crafted features performed and classification is done using HMM. The other one is deep learned features. Convolutional Neural Network extracts the features from frames in each layer. It extracts the features such as line, edge, color, texture and Classification is done using SVM. For human action recognition, hand-crafted features attain good result but it fails on large set of data. Deep learned features such as CNN have been used for large dataset and good result is obtained on recognition. To improve the human action recognition result, CNN is proposed. We compared both the approaches CNN and HMM and the results were analyzed. CNN results better accuracy while comparing with HMM.

Background Subtraction, Convolutional Neural Network, Canny Edge Detection, Optical Flow, Hidden Markov Model

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G. Augusta Kani, P. Geetha, A. Gomathi, "Human Activity Recognition using Deep with Gradient Fused Handcrafted Features and categorization based on Machine Learning Technique", International Journal of Computer Sciences and Engineering, Vol.06, Issue.07, pp.1-7, 2018.

Ear recognition is an active area of research and automatic ear recognition is one of the challenging areas in biometric and forensic domains. Human ear contains large amount of unique features for recognition of an individual. There are different approaches and descriptors that achieve relatively good results in ear biometric recognition. Studies show that there is poor recognition performance in case of occlusion, illumination variation and pose variation. This paper presents an overview of different local texture descriptors in the field of automatic ear recognition. The local descriptors which calculate features from small local patches have proven to be more effective in real world situations compared to the global descriptors which extract features from whole image.

Ear, Biometric, Texture Descriptors, Feature Extraction, LBP, GLCM, LPQ

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Resmi K R, G Raju, "A Review on Texture Descriptors in 2D Ear Recognition", International Journal of Computer Sciences and Engineering, Vol.06, Issue.07, pp.8-12, 2018.

Facial age estimation has got more consideration in the area of computer vision for the past few years. Age estimation is a troublesome task since the distinction between facial pictures with age variations is difficult. In this work, we analyze the problem of age prediction by means of SVR Model and deep learning technique. This paper attempts to find out the efficiency of SVR and Convolution neural network (CNN) on age estimation. Local features such as wrinkles and texture are extracted using Gabor filter, Local Binary Pattern (LBP) and Local Phase Quantization (LPQ). The three features are combined together and the dimension of the feature vector is reduced using Principle Component Analysis. Support Vector Regression (SVR) is utilized to predict the age of an individual. In CNN, the datasets are fine-tuned utilizing the pre-trained VGG-16 model which can group pictures into 1000 categories. The experimental results on the IMDB-WIKI dataset, the ICCV datasets and MORPH 2 dataset shows that CNN outperforms the local feature based SVR model in predicting the age.

Convolutional Neural Network, Local binary Pattern, Local Phase Quantization, Gabor Filter, Support Vector Regression

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A. Annie Micheal, P. Geetha , A. Saranya, "Comparative Evaluation on Supervised Learning Based Age Estimation", International Journal of Computer Sciences and Engineering, Vol.06, Issue.07, pp.13-18, 2018.

During the last few decades, there has been a rush in research in the area of deep learning. In this paper we have made a review on the limitations of deep learning in physical robotic systems, using currently available examples. It is mainly focused on the recent advances made in robotics community and application of deep learning in robotics.

Deep neural networks; artificial intelligence; human-robot interaction

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Shimi P S, Shajan P X, "A Review on Deep Learning in Robotics", International Journal of Computer Sciences and Engineering, Vol.06, Issue.07, pp.19-25, 2018.

Image processing is used to transform images into digital form and carry out some process on it, and get an improved image or obtain some useful details from it . Image processing has a lot of applications especially in agricultural, food industries, quality control and classification of products. Quality control in apple-based industries and marketing plays an important role in producing high quality products. Traditionally, apple quality inspection is performed by human experts. But the accuracy of them is low. To solve this there are different apple grading techniques and each techniques follow the same steps. Apple graded in three or more quality grades .These grades are AAA, AA and A; A, B, C. This review deals with compare different methods used in each step of apple grading and identify the best methods.

Image processing, Apple grading, Digital images

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Anu V Kottath , "A Review On Various Steps In Apple Fruit Grading", International Journal of Computer Sciences and Engineering, Vol.06, Issue.07, pp.26-30, 2018.

Data mining is process to analyses number of data sets and then extracts the meaning of data. It helps to predict the patterns and future trends, allowing business in decision making. Data mining provides methods and techniques for transformation of the data into useful information for decision making. These techniques can make process fast and take less time to predict the heart disease with more accuracy. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. By Applying data mining techniques to heart disease data which requires to be processed, we can get effective results and achieve reliable performance which will help in decision making in healthcare industry. It will help the cardiologist to diagnose the disease in less time and predict probable complications well in advance.

Data mining, disease prediction, KNN, Decision tree, SVM

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[2] M.A.Nishara Banu and B.Gomathy,” Disease Forecasting System Using Data Mining Methods”, 2014
[3] Aqueel Ahmed, Shaikh Abdul Hannan, “Data Mining Techniques to Find Out Heart Diseases”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-1, Issue-4, September 2012.
[4] Ms. Ishtake S.H, Prof. Sanap S.A., “Intelligent Heart Disease Prediction System Using Data Mining Techniques”, International J. of Healthcare & Biomedical Research, 2013
[5] Chitra R and Seenivasagam V, “Review of Heart Disease Prediction System Using Data Mining And Hybrid Intelligent Techniques”, Issn: 2229-6956(Online) Ictact Journal On Soft Computing, July 2013, Volume: 03, Issue: 04, 2013
[6] Nidhi Bhatla and Kiran Jyoti, “An Analysis of Heart Disease Prediction using Different Data Mining Techniques”, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181,Vol. 1 Issue 8, October – 2012
[7] Shadab Adam Pattekari and Asma Parveen, prediction system for heart disease using naïve bayes, International Journal of Advanced Computer and Mathematical Sciences, 2012
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[9] Abhishek taneja, Heart Disease Prediction System Using Data Mining Techniques, Oriental Scientific Publishing Co., India, 2013.
[10] Rashedur M. Rahman, Farhana Afroz, Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis, Journal of Software Engineering and Applications, 2013
[11] Nidhi Bhatla Kiran Jyoti, An Analysis of Heart Disease Prediction using Different Data Mining Techniques, International Journal of Engineering Research & Technology (IJERT), 2012.
[12] Humar Kahramanli, Novruz Allahverdi, Design of a hybrid system for the diabetes and heart diseases, Elsevier, 2008.
[13] Marcel A.J. van Gerven, Predicting carcinoid heart disease with the noisy-threshold classifier, Elsevier, 2007.
[14] Mohammad Taha Khan, Dr. Shamimul Qamar and Laurent F. Massin, A Prototype of Cancer/Heart Disease Prediction Model Using Data Mining, International Journal of Applied Engineering Research, 2012
[15] M.Akhil jabbar, Dr.Priti Chandra, Dr.B.L Deekshatulu, Heart Disease Prediction System using Associative Classification and Genetic Algorithm, International Conference on Emerging Trends in Electrical, Electronics and Communication Technologies, 2012
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Akhila Anikumar, Shajan P X, "Heart Disease Detection Using Data Miningtechniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.07, pp.31-33, 2018.

Swarm Intelligence explores swarms of autonomous robots or simulated agents. Little work, however, has been done on swarms of networked humans. Artificial Swarm Intelligence (ASI) strives to facilitate the emergence of a super-human intellect by connecting groups of human users in closed-loop systems modeled after biological swarms. Early studies have shown that “human swarms” can make more accurate predictions than traditional methods for tapping the wisdom of groups, such as votes and polls. Artificial Swarm Intelligence enables groups to form real-time systems online, connecting as ‘human swarms’ from anywhere in the world. A combination of real-time human input and A.I. algorithms, a Swarm Artificial Swarm Intelligence based system combines the knowledge, wisdom, opinions, and intuitions of live human participants as a unified emergent intelligence that can generate optimized predictions, decisions, insights, and judgments. Simply put, Swarm A.I. technology creates amplified intelligence while keeping humans in the loop.

Swarm intelligence, Human Swarming, ASI Algorithms

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Adhina Raju, Shajan P X, "Human Swarming with Artificial Swarm Intelligence using a hybrid approach", International Journal of Computer Sciences and Engineering, Vol.06, Issue.07, pp.34-37, 2018.