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

Plant disease is a major problem for food security, but in many parts of the world their rapid prediction remains difficult because of lack of the infrastructure required. New advances in machine vision achieved via deep learning have paved the way for diagnosis of AI-assisted diseases.\n To help determine the extent of plant disease, provide agricultural specialists with a digital archive with photos of diseased leaves. Estimate depends on Standard Area Diagrams (SADs), a collection of diseased leaf images, each of which includes an incrementally more diseased leaf compared\n to the previous one. Every SAD shows seriousness of the disease in terms of the percentage of the diseased leaf. Users then turn to the field for a leaf. For eg, equate it to SADs and use it to measure the severity of the disease. “This app is useful for crop consultants and research\n scientists looking to cut costs and improve the time and accuracy for assessing disease severity in plants.”

Plant Disease Detection and Classification Using Image Processing and Neural Networks

Proposed approach illustrates Detection touch-less heartbeat and a cardiopulmonary sign A microwave frame is attempted to find a good way off of 1 m from the pulse sound of a person by using a vector organize analyzer The proposed method requires the ability to distinguish pulse patterns with both recurrence and the plausibleness of the force tuning Calculations are made at 24, 58, 10, 16, and 60 GHz, and also at varying force rates between 0 and −27 dBm A classification that applies cardiopulmonary activity is defined in terms of the air and heart beatings statistics Using wavelet and wide channels, heartbeat rate and heart rate changeability are isolated from the test signal for SNRs between 0 and −20 dB

Touch-Less Heart Beat Detection on Improved LBP Algorithm

In city zonsiimproper manner prompting duplicating of germs that can cause ailment. There is a distinction in how as often as possible they utilize these litter canisters in different locales. Intermittent checking with timing holes doesn't help. They may flood at times significantly sooner than anticipated. To evade this and to improve the cleaning, a shrewd trash observing technique to discover when the trash bin is full is proposed in this paper. The strategy's plan assembles such data just as communicates it by means of an organization of remote sort. In this proposed technique, detecting of the dustbin's refuse level is finished by sensors just as data is sent to the authority portable station by means of GSM and GPS module. On the web observing of the trash level at control room is made utilizing a WiFi Module. Arduino processor interfaces the arrangement of sensors with the organization modules. This information helps contracted cleaners as well as the individuals who give trash bins, speedily void to keep up the territory perfect.

IoT based Smart Garbage System

Edge computing is a promising paradigm that enhances the capabilities of cloud computing in healthcare. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Edge computing is a promising model for improving cloud computing’s capabilities. It is important to maintain a positive climate free of all forms of protection and privacy violations in order to continue using computing services. The edge computing environment’s security and privacy concerns have hampered the technology’s overall acceptance as a reliable paradigm.The blockchain technology emerges as a viable solution because it provides influential features such as continuous secrecy, authentication, and robustness. This proposed work propose a blockchain-enabled distributed security architecture based on edge cloud. Security attacks are detected at the cloud layer, and as a result, security attacks at the IoT network’s edge layer are decreased. The gateway provides dynamic network traffic flow management, which aids in the detection of security attacks by identifying suspicious network traffic flows and reducing security attacks by blocking suspicious flows.For the next-generation IoT, the proposed security architecture has specified state-of-the-art contributions. The proposed algorithm is based on ECC based proxy encryption technique. Proxy re-encryption is a modern encryption scheme designed for protection in a distributed environment, including such smart applications that use the Internet of Things.

Integrated Security Framework for Healthcare Using Blockchain and Fog Computing

Security and privacy must be ensured for WBAN. Various key management and distribution schemes are developed to provide the security in WBAN. The key distribution methods distribute the keys for secure communication in WBAN. However, these methods are not suitable for the body sensors, owing to the limited sensor resources. The proposed chapter introduces the wavelet transform a frequency time domain technique for separating pertinent signals, and these signals will be compressed by the lossless compression algorithm. Thus the compressed data will be encrypted using proposed SPECG encryption algorithm. Attribute-based encryption is applied for secure data access in cloud for accessing the medical data. Security and privacy are maintained in three levels of communication. The proposed algorithm is evaluated in terms of false acceptance rate (FAR), false rejection rate (FRR), and half total error rate (HTER). The system is evaluated with varying level of tolerance. The simulation results show that the proposed work is secured than the existing system. Analysis of the experimental results leads to the conclusion that the proposed work requires the least time for carrying out encryption compared with any other algorithms.

A. Sivasangari

Papers

Plant Disease Detection and Classification Using Image Processing and Neural Networks

Touch-Less Heart Beat Detection on Improved LBP Algorithm

IoT based Smart Garbage System

Integrated Security Framework for Healthcare Using Blockchain and Fog Computing

Security and privacy in wireless body sensor networks using lightweight cryptography scheme