Segmentation of the breast region and pectoral muscle are fundamental subsequent steps in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region and pectoral muscle are considered a difficult task, particularly in mammogram images because of artefacts, homogeneity among the region of the breast and pectoral muscle, and low contrast along the region of breast boundary, the similarity between the texture of the Region of Interest (ROI), and the unwanted region and irregular ROI. This study aims to propose an improved threshold-based and trainable segmentation model to derive ROI. A hybrid segmentation approach for the boundary of the breast region and pectoral muscle in mammogram images was established based on thresholding and Machine Learning (ML) techniques. For breast boundary estimation, the region of the breast was highlighted by eliminating bands of the wavelet transform. The initial breast boundary was determined through a new thresholding technique. Morphological operations and masking were employed to correct the overestimated boundary by deleting small objects. In the medical imaging field, significant progress to develop effective and accurate ML methods for the segmentation process. In the literature, the imperative role of ML methods in enabling effective and more accurate segmentation method has been highlighted. In this study, an ML technique was built based on the Histogram of Oriented Gradient (HOG) feature with neural network classifiers to determine the region of pectoral muscle and ROI. The proposed segmentation approach was tested by utilizing 322, 200, 100 mammogram images from mammographic image analysis society (mini-MIAS), INbreast, Breast Cancer Digital Repository (BCDR) databases, respectively. The experimental results were compared with manual segmentation based on different texture features. Moreover, evaluation and comparison for the boundary of the breast region and pectoral muscle segmentation have been done separately. The experimental results showed that the boundary of the breast region and the pectoral muscle segmentation approach obtained an accuracy of 98.13% and 98.41% (mini-MIAS), 100%, and 98.01% (INbreast), and 99.8% and 99.5% (BCDR), respectively. On average, the proposed study achieved 99.31% accuracy for the boundary of breast region segmentation and 98.64% accuracy for pectoral muscle segmentation. The overall ROI performance of the proposed method showed improving accuracy after improving the threshold technique for background segmentation and building an ML technique for pectoral muscle segmentation. More so, this article also included the ground-truth as an evaluation of comprehensive similarity. In the clinic, this analysis may be provided as a valuable support for breast cancer identification.

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Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images

The rapid growth of the Internet of Things (IoT) has accelerated strong interests in the development of low-power wireless sensors. Today, wireless sensors are integrated within IoT systems to gather information in a reliable and practical manner to monitor processes and control activities in areas such as transportation, energy, civil infrastructure, smart buildings, environment monitoring, healthcare, defense, manufacturing, and production. The long-term and self-sustainable operation of these IoT devices must be considered early on when they are designed and implemented. Traditionally, wireless sensors have often been powered by batteries, which, despite allowing low overall system costs, can negatively impact the lifespan and the performance of the entire network they are used in. Energy Harvesting (EH) technology is a promising environment-friendly solution that extends the lifetime of these sensors, and, in some cases completely replaces the use of battery power. In addition, energy harvesting offers economic and practical advantages through the optimal use of energy, and the provisioning of lower network maintenance costs. We review recent advances in energy harvesting techniques for IoT. We demonstrate two energy harvesting techniques using case studies. Finally, we discuss some future research challenges that must be addressed to enable the large-scale deployment of energy harvesting solutions for IoT environments.

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Energy Harvesting Techniques for Internet of Things (IoT)

Supply chains are becoming increasingly complex, and many companies and researchers are looking to nature for finding solutions. In this paper, we provide a narrative synthesis and systematic revie...

Nature inspired supply chain solutions: definitions, analogies, and future research directions

Websites phishing is a cyber-attack that targets online users to steal their sensitive informationincluding login credentials and banking details. Attackers fool the users by presenting the masked webpage as legitimate or trustworthy to retrieve their essential data. Several solutions to phishing websites attacks have been proposed such as heuristics, blacklist or whitelist, and Machine Learning (ML) based techniques. This paper presents the state of art techniques for phishing website detection using the ML techniques. This research identifies solutions to the website's phishing problem based on the ML techniques. The majority of the examined approaches are focused on traditional ML techniques. Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and Ada Boosting are the powerful ML techniques examined in the literature. This survey paper also identifies deep learning-based techniques with better performance for detecting phishing websites compared to the conventional ML techniques. Challenges to ML techniques identified in this work include overfitting, low accuracy, and ML techniques' ineffectiveness in case of unavailability of enough training data. This research suggests that Internet users should know about phishing to avoid cyber-attacks. This paper also points out the proposal for an automated solution to phishing websites.

Machine LearningTechniquesfor Detection of Website Phishing: A Review for Promises and Challenges

Agriculture is the backbone of society as it mainly functions to provide food, feed and fiber on which all human depends to live. Precision agriculture is implemented with a goal to apply sufficient treatments at the right place in the right time with the purpose to provide low-input, high efficiency and sustainable agricultural production. In precision agriculture, automation and robotics have become one of the main frameworks which focusing on minimizing environmental impact and simultaneously maximizing agricultural produce. The application of automation and robotics in precision agriculture is essentially implemented for precise farm management by using modern technologies. In the past decades, a significant amount of research has focused on the applications of mobile robot for agricultural operations such as planting, inspection, spraying and harvesting. This paper reviews the recent applications of automation and robotics in agriculture in the past five years. In this paper, the recent implementations are divided into four categories which indicates different operations executed for planting management starting from a seed until the product is ready to be harvested. Towards the end of this paper, several challenges and suggestions are described to indicate the opportunities and improvements that can be made in designing an efficient autonomous and robotics system for agricultural applications. Based on the conducted review, different operations have different challenges thus require diverse solutions to solve the specific operational problem. Therefore, the development process of an efficient autonomous agricultural robotic system must consider all possibilities and challenges in different types of agricultural operation to minimize system errors during future implementation. In addition, the development cost needs to be fully considered to ensure that the farmers will be able to invest their capital as a consumer. Therefore, it will become highly possible for the autonomous agricultural robotic system to be widely implemented throughout the world in the future.

http://arqiipubl.com/ojs/index.php/AMS_Journal/article/download/130/88

Robotics and Automation in Agriculture: Present and Future Applications

A Nature-inspired Decision System for Secure Cyber Network Architecture -- Optimizing Resource Allocation in Next-Generation Wireless Networks Considering Carrier Aggregation Using Evolutionary Programming -- Artificial Feeding Birds (AFB): a new metaheuristic inspired by the behavior of pigeons -- Nature – Inspired Algorithms for Medical Image Processing -- Firefly Algorithm applied to the Estimation of Parameters of Photovoltaic Systems -- Realization of PSO-based Adaptive Beamforming Algorithm for Smart Antennas -- A Multi-objective Analysis and Comparison of Bio-inspired Approaches for the Cluster-Head Selection problem in WSN -- An Energy Efficient Cluster Head Selection using Artificial Bees Colony Optimization for Wireless Sensor Networks -- Modified Krill Herd Algorithm for Global Numerical Optimization Problems -- Application of Nature-Inspired Optimization Techniques in Vessel Traffic Control -- Enhanced throughput and accelerated detection of network attacks using a membrane computing model implemented on a GPU -- Physics-based Algorithms for Boolean Target Coverage -- A Hybrid Bio- Inspired Algorithm for Protein Domain Problems -- Modeling Service Discovery over Wireless Mesh Networks.

Advances in Nature-Inspired Computing and Applications

Handbook of Research on Soft Computing and Nature-Inspired Algorithms

Phishing is a critical issue that faces the digital security. The straightforwardness of the web and Internet uncovered open doors for offenders to transfer malevolent substance at the same time with the upgrade of online business trades, for example, phishing – the demonstration of taking individual data which ascends in number. Internet clients’ costs have been increased to billions of dollars for each year due to phishing. Phishers use parodied email, Uniform Resource Locator (URL) locations of phony sites, and phishing programming to take individual data and monetary record subtleties, for example, usernames and passwords. The boycott system is definitely not a sufficient method to remain safe from the cybercriminals. Hence, phishing site pointers must be considered for this reason, with the presence and utilization of machine learning calculations. The current techniques make utilization of all separated attributes in the phishing URL location, prompting high false positive rate.

Dynamic Recognition of Phishing URLs Using Deep Learning Techniques

AI-assisted Computer Network Operations Testbed for Nature-Inspired Cyber Security based Adaptive Defense Simulation and Analysis

In service to the state of the art, advances are required toward redesigning the framework over which web applications are built. The semantic web lies at the intersection of web and machine understandable meaningful data, turning it into intelligent ‘web of data’. The key requirement with any intelligent system has been to find a concrete knowledge representation that can make the inferences within time and space constraints; that is, reasoning effectively and efficiently within the resource constraints posed to the problem at one hand and with insufficient data as well as incomplete knowledge on the other hand. Various Knowledge representation schemes have been proposed in the literature, each having its limitation over the others. Ontology is the key component for semantic web engineering. Ontologies are conceptual knowledge bases providing a systematic and taxonomical description of the concepts and instances under consideration. Conceptual clarity in the computational representation of a concept is vital for holistic thinking and knowledge engineering. In order to meet the needs of an application/enterprise, knowledge should be presented taking care of all possible perspectives; and represented in a hierarchical structure with differing levels of granularity. This paper discusses about bringing all the manifestations of an ontological concept/decision under one umbrella; hence describing the resultant scheme as a unit of knowledge. By representing a concept as a knowledge unit, classical ontology is claimed to be sufficient in dealing with imprecise, vague and heterogeneous knowledge for real-world web applications; and portrays the capability to acquire fresh knowledge through its thorough interaction with the external world in a given working environment.

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Shishir Kumar Shandilya

Papers

Advances in Nature-Inspired Computing and Applications

Handbook of Research on Soft Computing and Nature-Inspired Algorithms

Dynamic Recognition of Phishing URLs Using Deep Learning Techniques

AI-assisted Computer Network Operations Testbed for Nature-Inspired Cyber Security based Adaptive Defense Simulation and Analysis

Data of SemanticWeb as Unit of Knowledge