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Different research in animal behavior demonstrate that amygdala, a heterogynous region in brain is related to sensitive response, incorporating input signals and initiating events associated to them. Impairment in this region can be responsible for emotional disorders in humans and social behavior. There are many experiments performed using rodents throughout the globe every day. Most of these experiments involve the death of rodents. These rodents are specifically kept for experiments related to immune system. The transgenic rodents have weak immune system as this system is disabled in them by manipulating the neurons and DNA associated with the immune system. Fear has been selected as a behaviour trait because it is widely considered as an attribute increasing survival and reproductive progress. This review article studies and highlights different research related to identify memory storage sites in rodents, its history, development and different procedures used for the process.

Alzheimer's Disease (AD) is one of the most common types of diseases amongst older adults. The primary reason for death in senior citizens is Alzheimer's related. To prevent Alzheimer's and provide early treatment, we have to accurately diagnosis Alzheimer's Disease and its prophase, which is called Mild Cognitive Impairment (MCI) in the healthcare sector. In this study, we have used seven machine learning classification methods for the prediction of Alzheimer's Disease. To recognize the type or stage of disease, it is essential to classify medical data and potentially develop a prediction model or system. The framework that we have developed consists of machine learning methods with Particle Swarm Optimization (PSO) and has been successfully applied to the classification of AD and dementia. For the prediction of Alzheimer's Disease, we have used seven machine learning Algorithms such as Support Vector Machine Classification, Random Forest Classification, XgBoost Classifier, Decision Tree Classification, Adaboost Classifier, K-Neighbour Classifier, and Logistic Regression. Our best-proposed method is the Random Forest Classifier, which achieves the greatest accuracy of 85.71%.

Parkinson’s disease is a widespread disease among elder population worldwide effecting approximately 6.3 million people across all genders, races and cultures. It is caused by dopamine loss, a chemical mediator that is responsible for body’s ability to control the movements. The disease reduces quality of life because of motor and non-motor complications. In this article Adaptive Boosting and Classification Tree based soft computing models are implemented to diagnose Parkinson’s disease using voice features. The soft computing models performances are evaluated on performance measures viz. true positive, false positive, false negative, true negative, accuracy, sensitivity, specificity, RMSE on training and datasets. Finally a comparison is performed to identify the most efficient model and dataset combination. Adaptive Boosting model outperformed others on reduced feature vector dataset obtained by selecting prominent 15 principal components using principal component analysis, where, it demonstrated 100% accuracy, 100% sensitivity, 100% specificity, 0.0 RMSE on training dataset and 67.00% accuracy, 67.35% sensitivity, 66.67% specificity, 0.5745 RMSE on testing dataset.

The report of World Health Organization (WHO) specifies that the diagnosis and treatment of cardiovascular diseases are challenging tasks. To study the electrical conductivity of the heart, Electrocardiogram (ECG) which is an inexpensive diagnostic tool, is used. Classification is the most well-known topic for arrhythmia detection related to cardiovascular disease. Many algorithms have been evolved for the classification of heartbeat arrhythmia in the previous few decades using the CAD system. In this paper, we have developed a new deep CNN (11-layer) model for automatically classifying ECG heartbeats into five different groups according to the ANSI-AAMI standard (1998) without using feature extraction and selection techniques. The experiment is performed on publicly available Physionet MIT-BIH database and evaluated results are then compared with the existing works mentioned in the literature. To handle the problem of minority classes as well as the class imbalance problem, the database has been oversampled artificially using SMOTE technique. The augmented ECG database was employed for training the model while the testing was performed on the unseen dataset. On evaluation of the results from the experiment, we found that the proposed CNN model performed better in comparison to the experiments mentioned in other papers in terms of accuracy, sensitivity, and specificity. abstract environment.

Traditional agricultural system involves large scale application of various chemical like fertilizers and pesticides for obtaining constant high yields. Though, alternatives are required for this system owing to the concerns related to environmental protection and human health issues. Also, there is a decline in the availability and efficiency of synthetic chemical pesticides as a result of novel legislation and the development of resistance in pest communities. Thus, other pest management strategies are needed. Biopesticides represents a very good alternative to traditional pesticides. They are pest management agents derived from living microbes or natural products. They promise potential roles in pest management and are widely applied across the globe. In this mini-review, brief introduction is provided for biopesticides followed by their different mode of interaction and their future prospective.

In this present era of high speed developing world of VLSI, data comparison is broadly used in computing system. In a computation system the receiving data needed to compare with deposited information to trace the identical entry. Comparison of data is a technique which fetches data together from various sources and compares it. When this matching differ the stored data, the use of proprietary matching algorithms is used to compare and correct the mismatch result. As per the survey, different researches have been done and still going on till date. In this study, it is found that the data matching can be done both in encoder and decoder end. The kept data preserved through error correcting codes (ECC code word) is used to relate with arriving data after decryption. Furthermore, in a Renovated butterfly weighted accumulator (BWAR), a type of reversible logic gate called peres gate is proposed to modify the half adder with disparate algorithm to compute hamming distance with reduced complexity and improved efficiency with testable feature. For a ECC code (16, 11) the proposed BWAR architecture minimizes the hardware complexity by 30.4% approximately with 13% of improved efficiency. In this brief, we have used a Xilinx 13.2 for Verilog coding for data matching algorithm.