Species classification of aquatic plants using GRNN and BPNN

Computer-aided plant species identification acts significantly on plant digital museum system and systematic botany, which is the groundwork for research and development of plants. This work presents a method for plant species identification using the images of flowers. It focuses on the stable feature extraction of flowers such as color, texture and shape features. Color-based segmentation using k-means clustering is used to extract the color features. Texture segmentation using texture filter is used to segment the image and obtain texture features. Sobel, Prewitt and Robert operators are used to extract the boundary of image and to obtain the shape features. From 405 images of flowers, color, texture and shape features are extracted. Classification of the plants into dry land plants and aquatic plants, the aquatic plant species into wet and marsh aquatic plants, wet aquatic plants into Iridaceae and Epilobium family and marsh aquatic plants into Malvaceae and Onagraceae family, the Iridaceae family is again classified into Babiana and Crocus species, the family Epilobium into Canum and Hirsutum, the family Malvaceae into Mallow and Pavonia, the family Onagraceae into Fuschia and Ludwigia species are done using general regression neural network and backpropagation neural network classifiers.     

Preventing the impact of selfish behavior under MANET using Neighbor Credit Value based AODV routing algorithm

Mobile Ad hoc Network (MANET) nodes exchange information using the multi-hop wireless communications without the need for any pre-existing infrastructure. Routing protocols of MANET are designed with an assumption that the nodes will cooperate in routing process. To achieve high throughput and reliable communication, the nodes are expected to cooperate with each other. Routing protocol plays a crucial role in an effective communication between nodes and operates on the assumption that the nodes are fully cooperative. Due to the open structure and limited battery-based energy in MANET, some nodes may not cooperate correctly or behave maliciously and such kind of misbehavior impacts the fairness, reliability and efficiency in MANET. Previous work addressed the ways to overcome these kinds of node misbehaviors and attacks. Most of the existing works need time to analyse the neighbor traffic and decide whether a neighbor is behaving maliciously or not. Further, the existing credit-based detection mechanisms may mark a genuine idle node as a malicious node. This work addresses a simple Neighbor Credit Value based AODV (NCV-AODV) routing algorithm for the detection of selfish behavior which avoids such false detection. The proposed idea is implemented in Ad hoc On Demand Distance Vector (AODV) routing protocol and an extensive analysis on the performance of the proposed detection mechanism against the selfish behavior of some MANET nodes are conducted.     

Abnormal gait recognition using exemplar based algorithm in healthcare applications

In healthcare applications, gait plays a major role in identification of the normal or abnormal person in different situations. Human gait refers to the walking style of the person, and it may also refer as locomotion using human limbs. The abnormal gait has irregular patterns of stance and swing phases. Without any clinical impairment, this paper proposes a novel approach to classify the person as normal or the person as suffering from neurological disorders from the videos using their gait videos. In addition, neurological gait disorders such as Parkinson gait, hemiplegic gait, and neuropathic gait has been identified using the gait features. Many systems are designed to detect and identify gait disorders using head, hip, heel, and toe behavior analysis from the bidirectional gait videos. As motivated by previous mechanisms, this paper proposes a novel vision based algorithm to recognize the gait abnormalities using model free approaches and significant feature vector generation from complete silhouette images of one gait cycle of a person. Here, a lean angle and ramp angle are considered as distinguishing and prominent features, and the results of these features are properly classified into normal or abnormal gait through the design of an unsupervised classifier.     

Automated human behavior analysis from surveillance videos: a survey

With increasing crime rates in today’s world, there is a corresponding awareness for the necessity of detecting abnormal activity. Automation of abnormal Human behavior analysis can play a significant role in security by decreasing the time taken to thwart unwanted events and picking them up during the suspicion stage itself. With advances in technology, surveillance systems can become more automated than manual. Human Behavior Analysis although crucial, is highly challenging. Tracking and recognizing objects and human motion from surveillance videos, followed by automatic summarization of its content has become a hot topic of research. Many researchers have contributed to the field of automated video surveillance through detection, classification and tracking algorithms. Earlier research work is insufficient for comprehensive analysis of human behavior. With the introduction of semantics, the context of a surveillance domain may be established. Such semantics may extend surveillance systems to perform event-based behavior analysis relevant to the domain. This paper presents a survey on research on human behavior analysis with a scope of analyzing the capabilities of the state-of-art methodologies with special focus on semantically enhanced analysis.     

Trends in word sense disambiguation

The problem and process of identifying the meaning of a word as per its usage context is called word sense disambiguation (WSD). Although research in this field has been ongoing for the past forty years, a distinct change of techniques adopted can be observed over time. Two important parameters govern the direction in which WSD research progresses during any period. These are the underlying requirement of the kind of sense disambiguation, or the domain, and the robustness of available knowledge in the form of corpora or dictionaries. This paper surveys the progress of WSD over time and the important linguistic achievements that enabled this progress.     

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

Nowadays, commercial transactions and customer reviews are part of human life and various business applications. The technologies create a great impact on online user reviews and activities, affecting the business process. Customer reviews and ratings are more helpful to the new customer to purchase the product, but the fake reviews completely affect the business. The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information. Therefore, in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity. Here, Amazon Product Kaggle dataset information is utilized for investigating the customer review. The collected information is analyzed and processed by batch normalized capsule networks (NCN). The network explores the user reviews according to product details, time, price purchasing factors, etc., ensuring product quality and ratings. Then effective recommendation system is developed using a butterfly optimized matrix factorization filtering approach. Then the system’s efficiency is evaluated using the Rand Index, Dunn index, accuracy, and error rate.     

Optimized Deep Learning Methods for Crop Yield Prediction

Crop yield has been predicted using environmental, land, water, and crop characteristics in a prospective research design. When it comes to predicting crop production, there are a number of factors to consider, including weather conditions, soil qualities, water levels and the location of the farm. A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting. The combination of data mining and deep learning creates a whole crop yield prediction system that is able to connect raw data to predicted crop yields. The suggested study uses a Discrete Deep belief network with Visual Geometry Group (VGG) Net classification method over the tweak chick swarm optimization approach to estimate agricultural production. The Network’s successively stacked layers were fed the data parameters. Based on the input parameters, a crop production prediction environment is constructed using the network architecture. Using the tweak chick swarm optimization technique, the best characteristics of input data are preprocessed, and the optimal output is used as input for the classification process. Discrete Deep belief network with the Visual Geometry Group Net classifier is used to classify the data and forecast agricultural production. The suggested model correctly predicts crop output with 97 percent accuracy, exceeding existing models by maintaining the baseline data distribution.     

Detection of Choroidal Neovascularization (CNV) in Retina OCT Images Using VGG16 and DenseNet CNN

Wireless Personal Communications - In this study, we intend to diagnose Choroidal Neovascularization in retinal Optical Coherence Tomography (OCT) images using Deep Learning Architectures. OCT...