THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

IEEE Transactions on Pattern Analysis and Machine Intelligence

We present an efficient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. However, convolutional neural networks are supervised and require labels as learning signals. We propose a spatiotemporal architecture for anomaly detection in videos including crowded scenes. Our architecture includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features. Experimental results on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of our method is comparable to state-of-the-art methods at a considerable speed of up to 140 fps.

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

Machine learning technology in biodiesel research: A review

Neuro-fuzzy systems have attracted the growing interest of researchers in various scientific and engineering areas due to its effective learning and reasoning capabilities. The neuro-fuzzy systems combine the learning power of artificial neural networks and explicit knowledge representation of fuzzy inference systems. This paper proposes a review of different neuro-fuzzy systems based on the classification of research articles from 2000 to 2017. The main purpose of this survey is to help readers have a general overview of the state-of-the-arts of neuro-fuzzy systems and easily refer suitable methods according to their research interests. Different neuro-fuzzy models are compared and a table is presented summarizing the different learning structures and learning criteria with their applications.

Recent advances in neuro-fuzzy system: A survey

Smart cities with smart infrastructure is a rapidly flourishing field of research in the modern days. Open areas, agricultural land, forests, office, homes and several areas can have occurrences of fire accidents leading to loss of significant resources. Unmanned Aerial Vehicle (UAV) and wireless sensor network technologies are used fir detection of fire at an early stage in this paper. This helps in avoiding serious fire accidents. The environmental parameters are monitored using the sensor architecture. The sensors uses IoT based applications for processing the gathered environmental data. Cloud computing, IoT sensors, wireless technology and UAVs are combined for the purpose of fire detection in this paper. In order to improve the accuracy of the system, integration of image processing schemes is done in this system. The rules are formulated such that the true detection rate is improved. The existing state-of-the-art models are compared with the proposed system. The simulation results show that the rate of fire detection of the proposed system is improved for up to 98% when compared to the traditional models.

https://www.irojournals.com/jscp/V2/I3/04.pdf

Real Time Monitoring and Fire Detection using Internet of Things and Cloud based Drones

Performing dimensionality reduction in the camera captured images without any loss is remaining as a big challenge in image processing domain. Generally, camera surveillance system is consuming more volume to store video files in the memory. The normally used video stream will not be sufficient for all the sectors. The abnormal conditions should be analyzed carefully for identifying any crime or mistakes in any type of industries, companies, shops, etc. In order to make it comfortable to analyze the video surveillance within a short time period, the storage of abnormal conditions of the video pictures plays a very significant role. Searching unusual events in a day can be incorporated into the existing model, which will be considered as a supreme benefit of the proposed model. The massive video stream is compressed in preprocessing the proposed learning method is the key of our proposed algorithm. The proposed efficient deep learning framework is based on intelligent anomaly detection in video surveillance in a continuous manner and it is used to reduce the time complexity. The dimensionality reduction of the video captured images has been done by preprocessing the learning process. The proposed pre-trained model is used to reduce the dimension of the extracted image features in a sequence of video frames that remain as the valuable and anomalous events in the frame. The selection of special features from each frame of the video and background subtraction process can reduce the dimension in the framework. The proposed method is a combination of CNN and SVM architecture for the detection of abnormal conditions at video surveillance with the help of an image classification procedure. This research article compares various methods such as Journal of Soft Computing Paradigm (JSCP) (2021) Vol.03/ No.02 Pages: 55-69 http://irojournals.com/jscp/ DOI: https://doi.org/10.36548/jscp.2021.2.001 56 ISSN: 2582-2640 (online) Submitted:6.03.2021 Revised: 30.03.2021 Accepted: 21.04.2021 Published: 10.05.2021 background subtraction (BS), temporal feature extraction (TFE), and single classifier classification methods.

https://irojournals.com/jscp/V3/I2/01.pdf

An Efficient Dimension Reduction based Fusion of CNN and SVM Model for Detection of Abnormal Incident in Video Surveillance

Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network

In various real time applications, several assisted services are provided by the human-robot interaction (HRI). The concept of convergence of a three-dimensional (3D) image into a plane-based projection is used for object identification via digital visualization in robotic systems. Recognition errors occur as the projections in various planes are misidentified during the convergence process. These misidentifications in recognition of objects can be reduced by input processing scheme dependent on the projection technique. The conjoining indices are identified by projecting the input image in all possible dimensions and visualizing it. Machine learning algorithm is used for improving the processing speed and accuracy of recognition. Labeled analysis is used for segregation of the intersection without conjoined indices. Errors are prevented by identifying the non-correlating indices in the projections of possible dimension. The inputs are correlated with related inputs that are stored with labels thereby preventing matching of the indices and deviations in the planes. Error, complexity, time and recognition ratio metrics are verified for the proposed model.

https://irojournals.com/iroiip/V3/I1/01.pdf

3D Image Processing using Machine Learning based Input Processing for Man-Machine Interaction

Aspect-level sentiment classification is the aspect of determining the text in a given document and classifying it according to the sentiment polarity with respect to the objective. However, since annotation cost is very high, it might serve a big obstacle for this purpose. However, from a consumer point of view, this is highly effective in reading document-level labelled data such as reviews which are present online using neural network. The online reviews are packed with sentiment encoded text which can be analyzed using this proposed methodology. In this paper a Transfer Capsule Network model is used which has the ability to transfer the knowledge gained at document-level to the aspect-level to classify according to the sentiment detected in the text. As the first step, the sentence is broken down in semantic representations using aspect routing to form semantic capsule data of both document-level and aspect-level. This routing approach is extended to group the semantic capsules for transfer learning framework. The effectiveness of the proposed methodology are experimented and demonstrated to determine how superior they are to the other methodologies proposed.

https://www.irojournals.com/aicn/V2/I3/03.pdf

Akey Sungheetha

Papers

Real Time Monitoring and Fire Detection using Internet of Things and Cloud based Drones

An Efficient Dimension Reduction based Fusion of CNN and SVM Model for Detection of Abnormal Incident in Video Surveillance

Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network

3D Image Processing using Machine Learning based Input Processing for Man-Machine Interaction

TransCapsule Model for Sentiment Classification