ABSTRACT - The main purpose of object detection is to know and work for one or more effective targets from still image or video data. Object detection is a key ability required by most computer and robot vision systems. The very recent research and works on this topic has been making great progress in many directions and different ways. In the current manuscript, we give an overview of past research on object detection depending on the weather conditions, outline the current main research strategies, and discuss open problems and possible future directions and views. In this paper, we address the object detection problem in the presence of fog by introducing a novel dual-subnet network (DSNet) that can also be trained and learnt three things: visibility improvement, object differentiation, and object localization. Contact:
 +91-9008001602
 080-40969981

ABSTRACT - Diseases and pests cause huge economic loss to the apple industry every year. The identification of various apple diseases is challenging for the farmers as the symptoms produced by different diseases may be very similar, and may be present simultaneously. This paper is an attempt to provide the timely and accurate detection and identification of apple diseases. In this study, we propose a deep learning based approach for identification and classification of apple diseases. The first part of the study is dataset creation which includes data collection and data labelling. Contact:
 +91-9008001602
 080-40969981

ABSTRACT - Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases Contact:
 +91-9008001602
 080-40969981

ABSTRACT - In this paper, Recurrent Neural Networks (RNN) have been applied for classifying the normal and abnormal beats in an ECG. The primary aim of this paper was to enable automatic separation of regular and irregular beats. The MITBIH Arrhythmia database is being used to classify the beat classification performance. The methodology used is carried out using huge volume of standard data i.e. ECG time-series data as inputs to Long Short Term Memory Network . We divided the dataset as training and testing sub-data. The effectiveness, accuracy and capabilities of our methodology ECG arrhythmia detection is demonstrated and quantitative comparisons with different RNN models have also been carried out. pretty much since the opening of first supermarket. Contact:
 +91-9008001602
 080-40969981

ABSTRACT - An automated teller machine is an electronics telecommunications device which is utilized by people, mostly to withdraw money. In the present scenario, a fair amount of the population using an ATM machine to withdraw cash are facing a problem of robberies and theft due to lack of security guards. Surveillance cameras being used in the ATM cells, however monitoring capabilities of law enforcement agencies has not kept pace. So, in this system anomalous behavior is detected using CNN and LSTM on the surveillance videos. Accurate recognition of anomalous behavior at a point in time is the most challenging problem for systems. The anomaly as well as non-anomaly dataset is fed to a machine and trained to identify abnormal behavior. Contact:
 +91-9008001602
 080-40969981

ABSTRACT - The feature extraction technique plays a very critical and crucial role in automatic leaf disease diagnosis system. Many different feature extraction techniques are used by the researchers for leaf disease diagnosis which includes colour, shape, texture, HOG, SURF and SIFT features. Recently Deep Learning is giving very promising results in the field of computer vision. In this manuscript, two feature extraction techniques are discussed and compared. In first approach, the Gray Level Covariance Matrix (GLCM) is used which extracts 12 texture features for diagnosis purpose. In second approach, the pretrained deep learning model, Alexnet is used for feature extraction purpose. There are 1000 features extracted automatically with the help of this pretrained model. Contact:
 +91-9008001602
 080-40969981

ABSTRACT - The Internet of Things (IoT) has enabled the invention of smart health monitoring systems. These health monitoring systems can track a person’s mental and physical wellness. Stress, anxiety, and hypertension are key causes of many physical and mental disorders. Age-related problems such as stress, anxiety, and hypertension necessitate specific attention in this setting. Stress, anxiety, and blood pressure monitoring can prevent long-term damage by detecting problems early. This will increase the quality of life and reduce caregiver stress and healthcare costs. Determine fresh technology solutions for real-time stress, anxiety, and blood pressure monitoring using discreet wearable sensors and machine learning approaches. This study created an automated artefact detection method for BP and PPG signalsContact:
 +91-9008001602
 080-40969981

ABSTRACT - Now a day some bird species are being found rarely and if found classification of bird species prediction is difficult. Naturally, birds present in various scenarios appear in different sizes, shapes, colors, and angles from human perspective. Besides, the images present strong variations to identify the bird. species more than audio classification. Also, human ability to recognize the birds through the images is more understandable. So this method uses the CaltechUCSD Birds 200 [CUB-200-2011] dataset for training as well as testing purpose. By using deep convolutional neural network (DCNN) algorithm an image converted into grey scale format to generate autograph by using tensor flow, where the multiple nodes of comparison are generated. Contact:
 +91-9008001602
 080-40969981

ABSTRACT - Bitcoin, a type of cryptocurrency is currently a thriving open-source community and payment network, which is currently used by millions of people. As the value of Bitcoin varies everyday, it would be very interesting for investors to forecast the Bitcoin value but at the same time making it difficult to predict. Bitcoin is a cryptocurrency technology that has attracted investors because of its big price increases. This has led to researchers applying various methods to predict Bitcoin prices such as Support Vector Machines, Multilayer Perceptron, RNN etc. To obtain accuracy and efficiency as compared to these algorithms this research paper tends to exhibit the use of RNN using LSTM model to predict the price of crypto currency. The results were computed by extrapolating graphs along with the Root Mean Square Error of the model which was found to be 3.38. Contact:
 +91-9008001602
 080-40969981

ABSTRACT - In this work we compare the performance of a number of vessel segmentation algorithms on a newly constructed retinal vessel image database. Retinal vessel segmentation is important for the detection of numerous eye diseases and plays an important role in automatic retinal disease screening systems. A large number of methods for retinal vessel segmentation have been published, yet an evaluation of these methods on a common database of screening images has not been performed. To compare the performance of retinal vessel segmentation methods we have constructed a large database of retinal images. The database contains forty images in which the vessel trees have been manually segmented. Contact:
 +91-9008001602
 080-40969981

ABSTRACT - : In this paper, we investigate problem reduction techniques using stochastic sampling and machine learning to tackle large-scale optimization problems. These techniques heuristically remove decision variables from the problem instance, that are not expected to be part of an optimal solution. First we investigate the use of statistical measures computed from stochastic sampling of feasible solutions compared with features computed directly from the instance data. Two measures are particularly useful for this: 1) a ranking-based measure, favoring decision variables that frequently appear in high-quality solutions; and 2) a correlation-based measure, favoring decision variables that are highly correlated with the objective values. To take this further we develop a machine learning approach, called Machine Learning for Problem Reduction (MLPR), that trains a supervised learning model on easy problem instances for which the optimal solution is known. Contact:
 +91-9008001602
 080-40969981

ABSTRACT - Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern. In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model to attack various time series classification models. The proposed attack on the classification model utilizes a distilled model as a surrogate that mimics the behavior of the attacked classical time series classification models. Our proposed methodology is applied onto 1-Nearest Neighbor Dynamic Time Warping (1-NN DTW) and a Fully Convolutional Network (FCN), all of which are trained on 42 University of California Riverside (UCR) datasets. In this paper, we show both models were susceptible to attacks on all 42 datasets. Contact:
 +91-9008001602
 080-40969981