In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.

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An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical reports, and medical images at a lesser time but with greater accuracy. There are even some instances where certain abnormalities cannot be directly recognized by humans. In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden relationships or abnormalities which are not visible to humans. Implementing algorithms to perform such tasks itself is difficult, but what makes it even more challenging is to increase the accuracy of the algorithm while decreasing the required time for the algorithm to execute. In the early days, processing of large amount of medical data was an important task which resulted in machine learning being adapted in the biological domain. Since this happened, the biology and biomedical fields have been reaching higher levels by exploring more knowledge and identifying relationships which were never observed before. Reaching to its peak now the concern is being diverted towards treating patients not only based on the type of disease but also their genetics, which is known as precision medicine. Modifications in machine learning algorithms are being performed and tested daily to improve the performance of the algorithms in analysing and presenting more accurate information. In the healthcare field, starting from information extraction from medical documents until the prediction or diagnosis of a disease, machine learning has been involved. Medical imaging is a section that was greatly improved with the integration of machine learning algorithms to the field of computational biology. Nowadays, many disease diagnoses are being performed by medical image processing using machine learning algorithms. In addition, patient care, resource allocation, and research on treatments for various diseases are also being performed using machine learning-based computational decision making. Throughout this paper, various machine learning algorithms and approaches that are being used for decision making in the healthcare sector will be discussed along with the involvement of machine learning in healthcare applications in the current context. With the explored knowledge, it was evident that neural network-based deep learning methods have performed extremely well in the field of computational biology with the support of the high processing power of modern sophisticated computers and are being extensively applied because of their high predicting accuracy and reliability. When giving concern towards the big picture by combining the observations, it is noticeable that computational biology and biomedicine-based decision making in healthcare have now become dependent on machine learning algorithms, and thus they cannot be separated from the field of artificial intelligence.

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Involvement of Machine Learning Tools in Healthcare Decision Making.

Breast cancer is type of tumor that occurs in the tissues of the breast. It is most common type of cancer found in women around the world and it is among the leading causes of deaths in women. This article presents the comparative analysis of machine learning, deep learning and data mining techniques being used for the prediction of breast cancer. Many researchers have put their efforts on breast cancer diagnoses and prognoses, every technique has different accuracy rate and it varies for different situations, tools and datasets being used. Our main focus is to comparatively analyze different existing Machine Learning and Data Mining techniques in order to find out the most appropriate method that will support the large dataset with good accuracy of prediction. The main purpose of this review is to highlight all the previous studies of machine learning algorithms that are being used for breast cancer prediction and this article provides the all necessary information to the beginners who want to analyze the machine learning algorithms to gain the base of deep learning.

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Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis

At the moment, the most prevalent form of cancer diagnosed in women across the globe is breast cancer. It develops in the breast tissue and is one of the most frequent causes of women’s death. This cancer can be cured if it is diagnosed at preliminary stage. Malignant and benign are two types of tumor found in case of breast cancer. Malignant tumors are deadly as their rate of growth is much higher than benign tumors. So, early identification of tumor type is pivotal for the appropriate treatment of a patient having breast cancer. In this work, Wisconsin Breast Cancer Dataset has been used which was collected from UCI repository. Our goal is to analyze the dataset and evaluate the performance of various machine learning algorithms for predicting breast cancer. Here, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Tree, Naive Bayes and Random Forest classifiers have been implemented for classifying tumors into benign and malignant. The accuracy of each algorithm is calculated and compared to find the most suitable one. Based on the analysis, Random Forest and Support Vector Machine outperform other classifiers with accuracy of 96.5%. These classifiers can be used to build an automatic diagnostic system for preliminary diagnosis of breast cancer.

Malignant and Benign Breast Cancer Classification using Machine Learning Algorithms

Humans can identify a speaker by listening to their voice, over the telephone, or on any digital devices. Acquiring this congenital human competency, authentication technologies based on voice biometrics, such as automatic speaker recognition (ASR), have been introduced. An ASR recognizes speakers by analyzing speech signals and characteristics extracted from speaker’s voices. ASR has recently become an effective research area as an essential aspect of voice biometrics. Specifically, this literature survey gives a concise introduction to ASR and provides an overview of the general architectures dealing with speaker recognition technologies, and upholds the past, present, and future research trends in this area. This paper briefly describes all the main aspects of ASR, such as speaker identification, verification, diarization etc. Further, the performance of current speaker recognition systems are investigated in this survey with the limitations and possible ways of improvement. Finally, a few unsolved challenges of speaker recognition are presented at the closure of this survey.

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A Survey of Speaker Recognition: Fundamental Theories, Recognition Methods and Opportunities

Breast cancer is one of the most common and leading causes of cancer among women. Currently, it has become the common health issue, and its incidence has increased recently. Prior identification is the best way to manage breast cancer results. Computer-aided detection or diagnosis (CAD) systems plays a major role in prior identification of breast cancer and can be used for reduction of death rate among women. The main intention of this paper is to make use of the recent advances in the development of CAD systems and related techniques. The mainstay of the project is to predict whether the person is having breast cancer or not. Machine learning is nothing but training the machines to learn and perform by itself without any explicit program or instruction. So here, predicting whether a person is suffering with breast cancer or not is done with the help of the trained data.

Breast Cancer Prediction via Machine Learning

Liver disease is one of the key causes of high numbers of deaths in the country and is considered a life-threatening disease, not just anywhere, but worldwide. Liver disease can also impact peoples early in their life. More than 2.4 per cent of annual Indian deaths are due to liver disorders. It is also difficult to detect liver disease due to mild symptoms in the early stages. If it is too late the signs always come to light. Thus liver-related disease poses more problems for people living and is more important nowadays to recognize the causes, and identification phase. So, for early detection of liver disease, an automated program is needed to build with more accuracy and reliability. Specific machine learning models are developed for this purpose to predict the disease. In this paper, the methods of Support Vector Machines (SVM), Decision Tree (DT) and Random Forest (RF) is proposed to predict liver disease with better precision, accuracy and reliability.

Diagnosis of Liver Disease using Machine Learning Models



In creating nations like India, there are in excess of 10 administrators
giving versatile administration in each circle. With the presentation of number convenience portable
client are progressively changing starting with one administrator then onto the next. This conduct
is called beat. The explanation behind beat might be many like valuing isn't alluring, visit call
drops, message drops, more client care calls and so forth. Presently the administrator in INDIA is
aware of the need of client. At that point, it is past the point of no return as the client has officially
settled on choice and hard to persuade and retain. So a robotized instrument is needed at administrator
end to predict which client may beat with high exactness.



With fast utilization of outfit classifiers to enhance exactness, we additionally propose
a gathering cross breed classifier that predicts with more precision.



Hybrid model contains regression, perceptron and confrontation both regression and
perceptron run parallel after completion execution both the results will be compared in a confrontation
level.



The report of customer who are predicted to churn and the reason for churning if reported.
Also it will store aggregate reporting HBASE database.


Prediction of Customer Plan using Churn Analysis for Telecom Industry

People with autism spectrum disorders have difficulties with communicating and socially interacting through facial expressions, even with their parents. The proposed approach applies person identification and emotion recognition. The objective of this work is to monitor and identify the people with autism spectral disorder based on sensors and machine learning algorithm. Our proposed system uses neurological sensor to collect the EEG data of patients and Q sensor for measuring stress level. The proposal integrates the facial recognition for identifying emotion recognition. The experimental results obtained from the proposed work performance evaluation are discussed, considering each for Autism Patient and the emotion labels. Proposed work shown the experimental results that can detect emotion with good accuracy compared to other classifiers. The proposed work achieves a 6% better accuracy for Proposed Model than Support Vector machine and 8% more accuracy than back Propagation algorithm.

Emotion recognition system for autism disordered people

In Today's World Women Safety is the most Important Issue In very Country. Ladies Are Harassed and pained and Sometimes when the earnest assistance is required, there is no necessary area of the ladies with the goal that individuals can help. Its need that we are generally mindful of significance of ladies’ security, yet we should dissect that they ought to be appropriately ensured. The prior existing framework is useful in identifying the ladies' area after the wrongdoing has been submitted. Right now, will utilize the ladies' purse where we will fix camera focal points, and which will be conveyed anyplace they go. At whatever point she interacts with any individual outside; the exercises of the individual can be observed consistently. In the event that the feelings of the individual fluctuate bringing about any destructive activity, at that point our framework will recognize it and procedure the caught picture and it will send to the police and relatives with GPS area followed from IP address. This application isn't utilized for cases like assaults and any sick people prodding young ladies, yet this additionally causes them from any terrible condition or any medical issue like blacking out of nowhere. GPS is to follow the area of the person in question and to send messages, the area of the injured individual to the close by police headquarters and the telephone quantities of the family members of the person in question. This application encourages ladies to conquer their dread in going out and do things what they like to do.

A. Sivasangari

Papers

Breast Cancer Prediction via Machine Learning

Diagnosis of Liver Disease using Machine Learning Models

Prediction of Customer Plan using Churn Analysis for Telecom Industry

Emotion recognition system for autism disordered people

Third Vision For Women Using Deep Learning Techniques