Diabetes Classification using Fuzzy Logic and Adaptive Cuckoo Search Optimization Techniques

Diabetic patients can be detected now a days globally. It�s main reason of growth is the incapability of body to produce enough insulin. So, majority of people today are either diabetic or pre-diabetic. Therefore, it is very much required to develop a system that can detect and classify the diabetes in optimal time period effectively and efficiently. So, proposed system make use of fuzzy logic and adaptive cuckoo search optimization algorithm (ACS) for diabetes classification. This work has been carried out in various steps. Firstly, the training dataset�s dimensionality reduction and optimal fuzzy rule generation via ACS optimization technique. Next is fuzzy model design and testing of fuzzified testing dataset. In this paper, outcome of FF-BAT algorithm has been compared with ACS algorithm. Experimental results were examined and it is noticed that ACS algorithm seems to perform better than FF-BAT algorithm

An AI-based Intelligent System for Healthcare Analysis Using Ridge–Adaline Stochastic Gradient Descent Classifier

Recent technological advancements in information and communication technologies introduced smart ways of handling various aspects of life. Smart devices and applications are now an integral part of our daily life; however, the use of smart devices also introduced various physical and psychological health issues in modern societies. One of the most common health care issues prevalent among almost all age groups is diabetes mellitus. This work aims to propose an Artificial Intelligence (AI) – based intelligent system for earlier prediction of the disease using Ridge Adaline Stochastic Gradient Descent Classifier (RASGD). The proposed scheme RASGD improves the regularization of the classification model by using weight decay methods, namely Least Absolute Shrinkage and Selection Operator(LASSO) and Ridge Regression methods. To minimize the cost function of the classifier, the RASGD adopts an unconstrained optimization model. Further, to increase the convergence speed of the classifier, the Adaline Stochastic Gradient Descent classifier is integrated with Ridge Regression. Finally, to validate the effectiveness of the intelligent system, the results of the proposed scheme have been compared with state-of-art machine learning algorithms such as Support Vector Machine and Logistic Regression methods. The RASGD intelligent system attains an accuracy of 92%, which is better than the other selected classifiers

A review on classification of imbalanced data for wireless sensor networks

© The Author(s) 2020. Classification of imbalanced data is a vastly explored issue of the last and present decade and still keeps the same importance because data are an essential term today and it becomes crucial when data are distributed into several classes. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers, that is, classifiers become biased toward the class having larger amount of data. The data generated from wireless sensor networks will have several imbalances. This review article is a decent analysis of imbalance issue for wireless sensor networks and other application domains, which will help the community to understand WHAT, WHY, and WHEN of imbalance in data and its remedies

An efficient sound and data steganography based secure authentication system

The prodigious advancements in contemporary technologies have also brought in the situation of unprecedented cyber-attacks. Further, the pin-based security system is an inadequate mechanism for handling such a scenario. The reason is that hackers use multiple strategies for evading security systems and thereby gaining access to private data. This research proposes to deploy diverse approaches for authenticating and securing a connection amongst two devices/gadgets via sound, thereby disregarding the pins’ manual verification. Further, the results demonstrate that the proposed approaches outperform conventional pin-based authentication orQR authentication approaches. Firstly, a random signal is encrypted, and then it is transformed into a wave file, after which it gets transmitted in a short burst via the device’s speakers. Subsequently, the other device/gadget captures these audio bursts through its microphone and decrypts the audio signal for getting the essential data for pairing. Besides, this model requires two devices/gadgets with speakers and a microphone, and no extra hardware such as a camera, for reading the QR code is required. The first module is tested with real-time data and generates high scores for the widely accepted accuracy metrics, including precision, Recall, F1 score, entropy, and mutual information (MI). Additionally, this work also proposes a module helps in a secured transmission of sensitive data by encrypting it over images and other files. This steganographic module includes two-stage encryption with two different encryption algorithms to transmit data by embedding inside a file. Several encryption algorithms and their combinations are taken for this system to compare the resultant file size. Both these systems engender high accuracies and provide secure connectivity, leading to a sustainable communication ecosystem.peer-reviewe

Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset

Stroke is enlisted as one of the leading causes of death and serious disability affecting millions of human lives across the world with high possibilities of becoming an epidemic in the next few decades. Timely detection and prompt decision making pertinent to this disease, plays a major role which can reduce chances of brain death, paralysis and other resultant outcomes. Machine learning algorithms have been a popular choice for the diagnosis, analysis and predication of this disease but there exists issues related to data quality as they are collected cross-institutional resources. The present study focuses on improving the quality of stroke data implementing a rigorous pre-processing technique. The present study uses a multimodal stroke dataset available in the publicly available Kaggle repository. The missing values in this dataset are replaced with attribute means and LabelEncoder technique is applied to achieve homogeneity. However, the dataset considered was observed to be imbalanced which reflect that the results may not represent the actual accuracy and would be biased. In order to overcome this imbalance, resampling technique was used. In case of oversampling, some data points in the minority class are replicated to increase the cardinality value and rebalance the dataset. transformed and oversampled data is further normalized using Standardscalar technique. Antlion optimization (ALO) algorithm is implemented on the deep neural network (DNN) model to select optimal hyperparameters in minimal time consumption. The proposed model consumed only 38.13% of the training time which was also a positive aspect. The experimental results proved the superiority of proposed model

Location Based Business Recommendation Using Spatial Demand

Business locations is most important factor to consider before starting a business because the best location attracts more number of people. With the help of web search engines, the customers can search the nearest business location before visiting the business. For example, if a customer need to buy some jewel, he makes use of search engines to find the nearest jewellery shop. If some entrepreneur wants to start a new jewellery shop, he needs to find a best area where there is no jewellery shop nearby and there are more customers in need of jewel. In this paper, we propose an algorithm to find the best place to start a business where there is high demand and no (or very few supply). We measure the quality of recommendation in terms of average service time, customer-business ratio of our new algorithm by implementing in benchmark datasets and the results prove that our algorithm is more efficient than the existing kNN algorithm

A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors. BC can offer decentralized and secure data storage, while CV allows machines to learn and understand visual data. The integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed

A survey on Zero touch network and Service Management (ZSM) for 5G and beyond networks

Abstract Faced with the rapid increase in smart Internet-of-Things (IoT) devices and the high demand for new business-oriented services in the fifth-generation (5G) and beyond network, the management of mobile networks is getting complex. Thus, traditional Network Management and Orchestration (MANO) approaches cannot keep up with rapidly evolving application requirements. This challenge has motivated the adoption of the Zero-touch network and Service Management (ZSM) concept to adapt the automation into network services management. By automating network and service management, ZSM offers efficiency to control network resources and enhance network performance visibility. The ultimate target of the ZSM concept is to enable an autonomous network system capable of self-configuration, self-monitoring, self-healing, and self-optimization based on service-level policies and rules without human intervention. Thus, the paper focuses on conducting a comprehensive survey of E2E ZSM architecture and solutions for 5G and beyond networks. The article begins by presenting the fundamental ZSM architecture and its essential components and interfaces. Then, a comprehensive review of the state-of-the-art for key technical areas, i.e., ZSM automation, cross-domain E2E service lifecycle management, and security aspects, are presented. Furthermore, the paper contains a summary of recent standardization efforts and research projects towards the ZSM realization in 5G and beyond networks. Finally, several lessons learned from the literature and open research problems related to ZSM realization are also discussed in this paper