There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Survey on Image Segmentation Techniques

Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population's health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study's primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.

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A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques

Fog computing has emerged as a promising technology that can bring the cloud applications closer to the physical IoT devices at the network edge. While it is widely known what cloud computing is, and how data centers can build the cloud infrastructure and how applications can make use of this infrastructure, there is no common picture on what fog computing and a fog node, as its main building block, really is. One of the first attempts to define a fog node was made by Cisco, qualifying a fog computing system as a mini-cloud, located at the edge of the network and implemented through a variety of edge devices, interconnected by a variety, mostly wireless, communication technologies. Thus, a fog node would be the infrastructure implementing the said mini-cloud. Other proposals have their own definition of what a fog node is, usually in relation to a specific edge device, a specific use case or an application. In this paper, we first survey the state of the art in technologies for fog computing nodes as building blocks of fog computing, paying special attention to the contributions that analyze the role edge devices play in the fog node definition. We summarize and compare the concepts, lessons learned from their implementation, and show how a conceptual framework is emerging towards a unifying fog node definition. We focus on core functionalities of a fog node as well as in the accompanying opportunities and challenges towards their practical realization in the near future.

What is a Fog Node A Tutorial on Current Concepts towards a Common Definition

Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: Review, taxonomy, research findings, challenges and future directions

Due to the advent of computer technology image-processing techniques have become increasingly important in a wide variety of applications. Image segmentation is a classic subject in the field of image processing and also is a hotspot and focus of image processing techniques. With the improvement of computer processing capabilities and the increased application of color image, the color image segmentation are more and more concerned by the researchers. Several general-purpose algorithms and techniques have been developed for image segmentation. Since there is no general solution to the image segmentation problem, these techniques often have to be combined with domain knowledge in order to effectively solve an image segmentation problem for a problem domain. This paper presents a comparative study of the basic image segmentation techniques i.e, EdgeBased, KMeans Clustering, Thresholding and Region-Based techniques.

/pdf/colour-image-segmentation-techniques-and-issues-an-approach-1cwdykegiw.pdf

Colour image segmentation techniques and issues: an approach

Implementation of Fruit Fly Optimization Algorithm (FFOA) to escalate the attacking efficiency of node capture attack in Wireless Sensor Networks (WSN)

Image compression is an implementation of the data compression which encodes actual image with some bits. Thepurpose of the image compression is to decrease the redundancy and irrelevance of image data to be capable to record or send data in an effective form. Hence the image compression decreases the time of transmit in the network and raises the transmission speed. In Lossless technique of image compression, no data get lost while doing the compression. To solve these types of issues various techniques for the image compression are used. Now questions like how to do mage compression and second one is which types of technology is used, may be arises. For this reason commonly two types’ of approaches are explained called as lossless and the lossy image compression approaches. These techniques are easy in their applications and consume very little memory. An algorithm has also been introduced and applied to compress images and to decompress them back, by using the Huffman encoding techniques.

/pdf/various-image-compression-techniques-lossy-and-lossless-2fl3v8bnej.pdf

Various Image Compression Techniques: Lossy and Lossless

 Abstract—Intelligent transportation clouds could provide Services such as autonomy, mobility, decision support and the standard development Environment for traffic management strategies, and so on. With mobile agent technology, an urban-traffic management system based on Agent-Based Distributed and Adaptive Platforms for Transportation Systems (Adapts) is both feasible and effective. However, the large-scale use of mobile agents will lead to the emergence of a complex, powerful organization layer that requires enormous computing and power resources. To deal with this problem, we propose a prototype urban-traffic management system using multi agent based intelligent traffic clouds. Cloud computing can help us to handle the large amount of storage resources and mass transportation of data effectively and efficiently.

Cloud Computing for Intelligent Transportation System

A clustering architecture increases network lifetime, fault tolerance and results in more efficient use of network resources. Mobile Ad-hoc Networks (MANETs) are extremely vulnerable to a variety of misbehaviors because of their basic features like: lack of communication infrastructure, dynamic network topology and short transmission range. To detect and reduce those misbehaviors, numerous trust management schemes have been proposed for MANETs. To effectively set up MANET, security is one of the main challenges that must be undertaken. The weight based clustering approach is based on combined weight metric that takes into account of several system parameters like the mobility, degree difference, transmission range and battery power of the node. One way to support efficient communication between nodes is to partition ad hoc networks into clusters. Various clustering schemes have been proposed to form clusters. Proposed IWCA algorithm can enhance the trust of cluster formation followed by malicious node removal from cluster head or member selection.

Manish Shrivastava

Papers

Colour image segmentation techniques and issues: an approach

Implementation of Fruit Fly Optimization Algorithm (FFOA) to escalate the attacking efficiency of node capture attack in Wireless Sensor Networks (WSN)

Various Image Compression Techniques: Lossy and Lossless

Cloud Computing for Intelligent Transportation System

Cluster formation through improved weighted clustering algorithm (IWCA) for mobile ad-hoc networks