bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Technology in Healthcare
Authors: Lopes Andreia Robert , Dias Ana Sofia , Sá-Moura Bebiana
Published: September 2022

The COVID-19 pandemic has disrupted healthcare worldwide and laid several fundamental problems that will have to be tackled to ensure high-quality healthcare services. This pandemic has represented an unparalleled challenge for healthcare systems and poses an opportunity to innovate and implement new solutions. Digital transformation within healthcare organizations has started and is reshaping healthcare. Technologies such as blockchain and IoT can bring about a revolution in healthcare and help solve many of the problems associated with healthcare systems that the COVID-19 crisis has exacerbated. In this chapter, IoT and blockchain technologies were discussed, focusing on their main characteristics, integration benefits, and limitations, identifying the challenges to be addressed soon. The authors further explored its potential in describing concrete cases and possible applications for healthcare in general and specifically for COVID-19.
Blockchain-Enabled Secure Internet of Things
Authors: Kumar Vinod , Lalotra Gotam Singh
Published: September 2022

This century is the time of ubiquitous, smart, and intelligent devices. These devices have a wide variety of applications in different fields like business, manufacturing, healthcare, retail, education, security, transportation, etc. Internet of things is now becoming the inexorable part of the all these fields. But security has always been a major concern in embracing these technologies. The blockchain technology is the next frontier for securing the internet of things. It will play a pivotal role to secure the communication in internet of things ecosystem. This chapter discusses the blockchain-enabled secure internet of things (IoT).
Consequent Formation in Security With Blockchain in Digital Transformation
Authors: Makka Shanthi , Arora Gagandeep , Sagar B. B.
Published: September 2022

Blockchain technology makes use of a centralized, peer-to-peer (P2P) network of databases, also called nodes, to validate and record digital transactions between individual users located anywhere across the globe. These transactions often take place through the exchange of cryptocurrencies such as bitcoins, Ethereum, and Ripple, etc. The security and transparency that is inherently present in digital transactions place blockchain technology in high demand across various industrial applications. Each node updates its database in real-time as and when transactions occur. The transaction gets authorized only when a majority of the nodes in the network validate the transaction. Once the verification is complete, a block, consisting of hash and keys, is generated for each new transaction and is linked to previous transactions in every database. Every node updates its database with the new block. A hacker would have to break down every node in the system to commit fraud. Blockchain could play a major role in maintaining the cyber security of digital transactions in the future.
Cloud-Centric Blockchain Public Key Infrastructure for Big Data Applications
Authors: Khieu Brian Tuan , Moh Melody
Published: September 2022

A cloud-based public key infrastructure (PKI) utilizing blockchain technology is proposed. Big data ecosystems have scalable and resilient needs that current PKI cannot satisfy. Enhancements include using blockchains to establish persistent access to certificate data and certificate revocation lists, decoupling of data from certificate authority, and hosting it on a cloud provider to tap into its traffic security measures. Instead of holding data within the transaction data fields, certificate data and status were embedded into smart contracts. The tests revealed a significant performance increase over that of both traditional and the version that stored data within blocks. The proposed method reduced the mining data size, and lowered the mining time to 6.6% of the time used for the block data storage method. Also, the mining gas cost per certificate was consequently cut by 87%. In summary, completely decoupling the certificate authority portion of a PKI and storing certificate data inside smart contracts yields a sizable performance boost while decreasing the attack surface.
Composite Identity of Things (CIDoT) on Permissioned Blockchain Network for Identity Management of IoT Devices
Authors: Amin Anang Hudaya Muhamad , Kiwanuka Fred N. , Abdelmajid Nabih T. J. , AlKaabi Saif Hamad , Khalid Abdulqader Rashed Ahli Sultan
Published: September 2022

Internet of things (IoT) is in the forefront of many existing smart applications, including autonomous systems and green technology. IoT devices have been commonly used in the monitoring of energy efficiency and process automation. As the application spreads across different kinds of applications and technology, a large number of IoT devices need to be managed and configured, as they are capable of generating massive amount of sensory data. Looking from this perspective, there is a need for a proper mechanism to identify each IoT devices within the system and their respective applications. Participation of these IoT devices in complex systems requires a tamper-proof identity to be generated and stored for the purpose of device identification and verification. This chapter presents a comprehensive approach on identity management of IoT devices using a composite identity of things (CIDoT) with permissioned blockchain implementation. The proposed approach described in this chapter takes into account both physical and logical domains in generating the composite identity.
SEF4CPSIoT Software Engineering Framework for Cyber-Physical and IoT Systems
Authors: Ramachandran Muthu
Published: September 2022

Cyber-physical systems (CPS) have emerged to address the need for more efficient integration of modern advancement in cyber and wireless communications technologies such as 5G with physical objects. In addition, CPSs systems also needed to efficient control of security and privacy when we compare them with internet of things (IoT). In recent years, we experienced lack of security concerns with smart home IoT applications such as home security camera, etc. Therefore, this paper proposes a systematic software engineering framework for CPS and IoT systems. This paper also proposed a comprehensive requirements engineering framework for CPS-IoT applications which can also be specified using BPMN modelling and simulation to verify and validate CPS-IoT requirements with smart contracts. In this context, one of the key contribution of this paper is the innovative and generic requirements classification model for CPS-IoT application services, and this can also be applied to other emerging technologies such as fog, edge, cloud, and blockchain computing.
Blockchain for Industrial Internet of Things (IIoT)
Authors: Sharma Rinki
Published: September 2022

Over the years, the industrial and manufacturing applications have become highly connected and automated. The incorporation of interconnected smart sensors, actuators, instruments, and other devices helps in establishing higher reliability and efficiency in the industrial and manufacturing process. This has given rise to the industrial internet of things (IIoT). Since IIoT components are scattered all over the network, real-time authenticity of the IIoT activities becomes essential. Blockchain technology is being considered by the researchers as the decentralized architecture to securely process the IIoT transactions. However, there are challenges involved in effective implementation of blockchain in IIoT. This chapter presents the importance of blockchain in IIoT paradigm, its role in different IIoT applications, challenges involved, possible solutions to overcome the challenges and open research issues.
Blockchain Technology-Security Booster
Authors: Patil Harsha Kundan
Published: September 2022

“Blockchain” as the name suggests is the chain of blocks. It is the chunk of digital information (blocks) that are connected through the public databases (Chain). It is nothing but the newer version of file organisation. Blocks store digital information like actual record of any transaction, details of involve entities in the transaction, time stamps, and other metadata of the transactions. Blocks also have unique ids, which are known as hash. Blockchain technology is built using peer-to-peer networking. Anyone who is on network can access the blocks. There is no centralised community to control the blockchain. It is operated by miners, the peoples who lend their computing power to the network to solve the complex computation algorithm problems. These blocks are stored in multiple computers. Due to its distribution and decentralisation, the validation process is broadcast in nature, which provides it “the trusted approach”. Blockchain enables security and tamperproof capabilities for storing data and smart contracts. Any tampering of data attempted by a node or user in a block changes the hash of the block. The blockchain technology has the capability to face and provides the solution to fight with the problem of risk and security concern online. In 2008, a mysterious white paper titled “Bitcoin: A Peer to Peer Electronic Cash System”, by visionary Satoshi Nakamoto gave birth to the concept of blockchain. The chapter explains the structure of blockchain technology in detail and enlighten the aspects that make blockchain technology the secure concept of today's world.
Data Security in Clinical Trials Using Blockchain Technology
Authors: de-Melo-Diogo Marta , Tavares Jorge , Nunes Luís Ângelo
Published: September 2022

Blockchain technology in a clinical trial setting is a valuable asset due to decentralization, immutability, transparency, and traceability features. For this chapter, a literature review was conducted to map the current utilization of blockchain systems in clinical trials, particularly data security managing systems and their characteristics, such as applicability, interests of use, limitations, and issues. The advantages of data security are producing a more transparent and tamper-proof clinical trial by providing accurate, validated data, therefore producing a more reliable and credible clinical trial. On the other hand, data integrity is a critical issue since data obtained from trials are not instantly made public to all participants. Work needs to be done to establish the significant implications in security data when applying blockchain technology in a real-world clinical trial setting and generalized conditions of use to establish its security.
Security and Privacy for Electronic Healthcare Records Using AI in Blockchain
Authors: Selvanambi Ramani , Bhutani Samarth , Veauli Komal
Published: September 2022

In yesteryears, the healthcare data related to each patient was limited. It was stored and controlled by the hospital authorities and was seldom regulated. With the increase in awareness and technology, the amount of medical data per person has increased exponentially. All this data is essential for the correct diagnosis of the patient. The patients also want access to their data to seek medical advice from different doctors. This raises several challenges like security, privacy, data regulation, etc. As health-related data are privacy-sensitive, the increase in data stored increases the risk of data exposure. Data availability and privacy are essential in healthcare. The availability of correct information is critical for the treatment of the patient. Information not easily accessed by the patients also complicates seeking medical advice from different hospitals. However, if data is easily accessible to everyone, it makes privacy and security difficult. Blockchains to store and secure data will not only ensure data privacy but will also provide a common method of data regulation.
Security for IoT
Authors: Sheikh Anjum Nazir Qureshi , Ambhaikar Asha , Kumar Sunil
Published: September 2022

The internet of things is a versatile technology that helps to connect devices with other devices or humans in any part of the world at any time. Some of the researchers claim that the number of IoT devices around the world will surpass the total population on the earth after a few years. The technology has made life easier, but these comforts are backed up with a lot of security threats. Wireless medium for communication, large amount of data, and device constraints of the IoT devices are some of the factors that increase their vulnerability to security threats. This chapter provides information about the attacks at different layers of IoT architecture. It also mentions the benefits of technologies like blockchain and machine learning that can help to solve the security issues of IoT.
Blockchain Technology Integration in IoT and Applications
Authors: Chander Bhanu
Published: September 2022

The Internet of Things (IoT) pictures an entire connected world, where things or devices are proficient to exchange a few measured data words and interrelate with additional things. This turns for a feasible digital demonstration of the existent world. Nonetheless, nearly all IoT things are simple to mistreat or compromise. Moreover, IoT devices are restricted in computation, power, and storage, so they are more vulnerable to bugs and attacks than endpoint devices like smartphones, tablets, and computers. Blockchain has remarkable interest from academics and industry because of its salient features including reduced dependencies on third parties, cryptographic security, immutability, decentralized nature, distributed nature, and anonymity. In the current scenario, blockchain with its features provides an anonymous framework for IoT. This chapter produces comprehensive knowledge of IoTs, Blockchain knowledge, security issues, Blockchain integration with IoT (BIoT), consensus, mining, message validation mechanisms, challenges, a solution, and future directions.
Blockchain Technology for IoT
Authors: R. Sasikumar , P. Karthikeyan , M. Thangavel
Published: September 2022

In the internet era, data is considered to be the primary asset, and the host or applications in a network are vulnerable to various attacks. Traditional network architectures have centralized authority to provide authentication, authorization, and access control services. In this case, there is a possibility of data mishandling activities from the valuable information available in the given network application. To avoid this type of mishandling, a new technology came into existence known as blockchain. Implementing blockchain technology in the internet of things (IoT) will ensure data integrity, stability, and durability. The authors present a detailed investigation of various IoT applications with blockchain implementation. Blockchain-based mechanisms will improve the security aspects in the traditional network applications related to IoT like insurance policies claiming, personal identification, and electronic health records.
Decentralizing Privacy Using Blockchain to Protect Private Data and Challanges With IPFS
Authors: Manoj M. K. , Krishnan Somayaji Siva Rama
Published: September 2022

Blockchain technology is a distributed framework for sharing data that is validated through cryptographic functions. The nodes of the network come to a consensus regarding addition of data to the blockchain. Every blockchain operation requires a processing fee. This fee makes storing of large data on the blockchain infeasible. An indirect alternative for this challenge could be use of IPFS, which is a decentralized peer-peer network that facilitates storage of file. This is accomplished by storing the hash of the IPFS as data on the blockchain.
Blockchain and IoT-Based Diary Supply Chain Management System for Sri Lanka
Authors: Jayasena K. Pubudu Nuwnthika , Madhunamali Poddivila Marage Nimasha Ruwandi
Published: September 2022

The central problem to be addressed in this research is to investigate how blockchain technology can be used in today's food supply chains to deliver greater traceability of assets. The aim is to create a blockchain model in the dairy supply chain that can be implemented across any food supply chains and present the advantages and limitations in its implementation. Blockchain allows monitoring all types of transactions in a supply chain more safely and transparently. Acceptance of blockchain in the supply chain and logistics is slow right now because of related risks and the lack of demonstrable models. The proposed solution removes the need for a trusted centralized authority, intermediaries and provides records of transactions, improving high integrity, reliability, and security efficiency and protection. All transactions are registered and maintained in the unchangeable database of the blockchain with access to a shared file network.
Artificial Intelligence and Image Analysis for the Identification of Endometrial Malignancies
Authors: Pouliakis Abraham , Damaskou Vasileia , Margari Niki , Karakitsou Efrossyni , Pergialiotis Vasilios , Valasoulis George , Michail George , Chrelias Charalampos , Chrelias George , Sioulas Vasileios , Gouloumi Alina-Roxani , Koufopoulos Nektarios , Nifora Martha , Zacharatou Andriani , Kalantaridou Sophia , Panayiotides Ioannis G.
Published: September 2022

Abraham Pouliakis et al. compare Machine Learning Algorithms (MLAs) in the discrimination between benign and malignant endometrial nuclei and lesions. Nuclei characteristics are obtained via image analysis and were measured from liquid-based cytology slides. 416 histologically confirmed patients were involved, 168 healthy, and the remaining with pathological endometrium. 50% of the cases were used to three MLAs: a feedforward artificial neural network (ANN) trained by the backpropagation algorithm, a learning vector quantization (LVQ) and a competitive learning ANN. The outcome of this process was the classification of cell nuclei as benign or malignant. Based on the nuclei classification, an algorithm to classify individual patients was constructed. The sensitivity of the MLAs in training set for nuclei classification was in the range of 77%-84%. Patients' classification had sensitivity in the range of 90%-98%. These findings indicate that MLAs have good performance for the classification of endometrial nuclei and lesions.
Classification of Breast Lesions in Frontal Thermographic Images Using a Diagnosis Aid Intelligent System
Authors: Araújo de Santana Maíra , Pereira Jessiane Mônica Silva , Lins de Lima Clarisse , Jacinto de Almeida Maria Beatriz , Silva de Andrade José Filipe , Silva de Souza Thifany Ketuli , Fernandes de Lima Rita de Cássia , Pinheiro dos Santos Wellington
Published: September 2022

This study aims to assess the breast lesions classification in thermographic images using different configuration of an Extreme Learning Machine network as classifier. In this approach, the authors changed the number of neurons in the hidden layer and the type of kernel function to further explore the network in order to find a better solution for the classification problem. Authors also used different tools to perform features extraction to assess both texture and geometry information from the breast lesions. During the study, the authors found that the results changed not only due to the network parameters but also due to the features chosen to represent the thermographic images. A maximum accuracy of 95% was found for the differentiation of breast lesions.
Feature Selection Based on Dialectical Optimization Algorithm for Breast Lesion Classification in Thermographic Images
Authors: Pereira Jessiane Mônica Silva , Araújo de Santana Maíra , Lins de Lima Clarisse , Fernandes de Lima Rita de Cássia , Lopes de Lima Sidney Marlon , Pinheiro dos Santos Wellington
Published: September 2022

Breast cancer is the leading cause of death among women worldwide. Early detection and early treatment are critical to minimize the effects of this disease. In this sense, breast thermography has been explored in the process of diagnosing this type of cancer. Furthermore, in an attempt to optimize the diagnosis, intelligent pattern recognition techniques are being used. Features selection performs an essential task in this process to optimize these intelligent techniques. This chapter proposes a features selection method using Dialectical Optimization Method (ODM) associated to a KNN classifier. The authors found that this combination proved to be a good approach showing a low impact on breast lesion classification performance. They obtained around 5% decrease in accuracy, with a reduction of about 46.80% of the features vector. The specificity and sensitivity values they found were competitive to other widely used methods.
Mammogram Classification Using Nonsubsampled Contourlet Transform and Gray-Level Co-Occurrence Matrix
Authors: Taifi Khaddouj , Taifi Naima , Fakir Mohamed , Safi Said , Sarfraz Muhammad
Published: September 2022

This chapter explores diagnosis of the breast tissues as normal, benign, or malignant in digital mammography, using computer-aided diagnosis (CAD). System for the early diagnosis of breast cancer can be used to assist radiologists in mammographic mass detection and classification. This chapter presents an evaluation about performance of extracted features, using gray-level co-occurrence matrix applied to all detailed coefficients. The nonsubsampled contourlet transform (NSCT) of the region of interest (ROI) of a mammogram were used to be decomposed in several levels. Detecting masses is more difficult than detecting microcalcifications due to the similarity between masses and background tissue such as F) fatty, G) fatty-glandular, and D) dense-glandular. To evaluate the system of classification in which k-nearest neighbors (KNN) and support vector machine (SVM) used the accuracy for classifying the mammograms of MIAS database between normal and abnormal. The accuracy measures through the classifier were 94.12% and 88.89% sequentially by SVM and KNN with NSCT.
Split and Merge-Based Breast Cancer Segmentation and Classification
Authors: Khoulqi Ichrak , Idrissi Najlae
Published: September 2022

Breast cancer is the most frequent cancer in morocco with 36.1%. It is the second leading cause of death for women all over the world. The effective way to diagnose and treat breast cancer is the early detection because it increases the success of treatment and the chances of survival. Digitized mammographic images are one of the frequently used diagnosis tools to detect and classify the breast cancer at the early stage. To improve the diagnosis accuracy, computer-aided diagnosis (CAD) systems are beneficial for detection. Generally, a CAD system consists of four stages: pretreatment, segmentation, features extraction, and classification. In this chapter, the authors present some work in the development of a CAD system in order to segment a breast tumor (microcalcifications) on mammographic images and classify it by choosing the algorithm that gives a good rate using a technique of a vote.
An Ensemble Feature Subset Selection for Women Breast Cancer Classification
Authors: Kalaivani A.
Published: September 2022

Breast cancer leads to fatal diseases both in India and America and takes the lives of thousands of women in the world every year. The patients can be easily treated if the signs and symptoms are identified at the early stages. But the symptoms identified at the final stage spreads in the human body, and most of the time, the cancer is identified at the final stage. Breast cancer detected at the early stage is treated easily rather than at the advanced stage. Computer-aided diagnosis came into existence from 2000 with high expectations to improve true positive diagnosis and reduce false positive marks. Artificial intelligence revolved in computing drives the attention of deep learning for an automated breast cancer detection and diagnosis in digital mammography. The chapter focuses on automatic feature selection algorithm for diagnosis of women breast cancer from digital mammographic images achieved through multi-layer perceptron techniques.
Predictive Modeling for Classification of Breast Cancer Dataset Using Feature Selection Techniques
Authors: S. Leena Nesamani , Rajini S. Nirmala Sigirtha
Published: September 2022

Predictive modeling or predict analysis is the process of trying to predict the outcome from data using machine learning models. The quality of the output predominantly depends on the quality of the data that is provided to the model. The process of selecting the best choice of input to a machine learning model depends on a variety of criteria and is referred to as feature engineering. The work is conducted to classify the breast cancer patients into either the recurrence or non-recurrence category. A categorical breast cancer dataset is used in this work from which the best set of features is selected to make accurate predictions. Two feature selection techniques, namely the chi-squared technique and the mutual information technique, have been used. The selected features were then used by the logistic regression model to make the final prediction. It was identified that the mutual information technique proved to be more efficient and produced higher accuracy in the predictions.
Healthcare
Authors: Selvanambi Ramani , Jaisankar N.
Published: September 2022

Quality analysis of the treatment of cancer has been an objective of e-health services for quite some time. The objective is to predict the stage of breast cancer by using diverse input parameters. Breast cancer is one of the main causes of death in women when compared to other tumors. The classification of breast cancer information can be profitable to anticipate diseases or track the hereditary of tumors. For classification, an artificial neural network (ANN) structure was carried out. In the structure, nine training algorithms are used and the proposed is the Levenberg-Marquardt algorithm. For optimizing the hidden layer and neuron, three optimization techniques are used. In the result, the best approval execution is anticipated and the diverse execution evaluation estimation for three optimization algorithms is researched. The correlation execution diagram for an accuracy of 95%, a sensitivity of 98%, and a specificity of 89% of a social spider optimization (SSO) algorithm are shown.
Grey Wolf Optimization Trained Feed Foreword Neural Network for Breast Cancer Classification
Authors: Pal Shankho Subhra
Published: September 2022

Breast cancer is the most common invasive cancer in females worldwide and is major cause of deaths. The diagnoses of breast cancer include mammograms, breast ultrasound, magnetic resonance imaging (MRI), ductogram and biopsy. Biopsy is best and only way to know if the breast tumor is cancerous. Report says that positive detection of breast cancer through biopsy can reach as low as 10%. So many statistical techniques and cognitive science approaches like artificial intelligence are used to detect the type of breast cancer in a patient for getting more accuracy. This article presents the breast cancer classification using feed foreword neural network trained by grey wolf optimization algorithm. The superiority of the GWO-FFNN is shown by experimenting Wisconsin Hospital data set (Breast Cancer Wisconsin) and comparing recently reported results. The evaluations show that the proposed approach is very robust, effective and gives better correct classification as compared to other classifiers.
Proximate Breast Cancer Factors Using Data Mining Classification Techniques
Authors: Mensah Alice Constance , Asare Isaac Ofori
Published: September 2022

Breast cancer is the most common of all cancers and is the leading cause of cancer deaths in women worldwide. The classification of breast cancer data can be useful to predict the outcome of some diseases or discover the genetic behavior of tumors. Data mining technology helps in classifying cancer patients and this technique helps to identify potential cancer patients by simply analyzing the data. This study examines the determinant factors of breast cancer and measures the breast cancer patient data to build a useful classification model using a data mining approach. In this study of 2397 women, 1022 (42.64%) were diagnosed with breast cancer. Among the four main learning techniques such as: Random Forest, Naive Bayes, Classification and Regression Model (CART), and Boosted Tree model were used for the study. The Random Forest technique had the better accuracy value of 0.9892(95%CI,0.9832 -0.9935) and a sensitivity value of about 92%. This means that the Random Forest learning model is the best model to classify and predict breast cancer based on associated factors.
Analysis of Machine Learning Algorithms for Breast Cancer Detection
Authors: Aswathy M. A.  , Mohan Jagannath
Published: September 2022

As per the latest health ministry registries of 2017-2018, breast cancer among women has ranked number one in India and number two in United States. Despite the fact that breast cancer affects men also, pervasiveness is lower in men than women. This is the reason breast cancer is such a vital concern among ladies. Roughly 80% of cancer malignancies emerge from epithelial cells inside breast tissues. In breast cancer spectrum, ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) are considered malignant cancers that need treatment and care. This chapter mainly deals with breast cancer and machine learning (ML) applications. All through this chapter, different issues related to breast cancer prognosis and early detection and diagnostic techniques using various ML algorithms are addressed.
Performance Analysis of Machine Learning Algorithms for Cervical Cancer Detection
Authors: Singh Sanjay Kumar , Goyal Anjali
Published: September 2022

Cervical cancer is second most prevailing cancer in women all over the world and the Pap smear is one of the most popular techniques used to diagnosis cervical cancer at an early stage. Developing countries like India has to face the challenges in order to handle more cases day by day. In this article, various online and offline machine learning algorithms has been applied on benchmarked data sets to detect cervical cancer. This article also addresses the problem of segmentation with hybrid techniques and optimizes the number of features using extra tree classifiers. Accuracy, precision score, recall score, and F1 score are increasing in the proportion of data for training and attained up to 100% by some algorithms. Algorithm like logistic regression with L1 regularization has an accuracy of 100%, but it is too much costly in terms of CPU time in comparison to some of the algorithms which obtain 99% accuracy with less CPU time. The key finding in this article is the selection of the best machine learning algorithm with the highest accuracy. Cost effectiveness in terms of CPU time is also analysed.
Survey of Breast Cancer Detection Using Machine Learning Techniques in Big Data
Authors: Gupta Madhuri , Gupta Bharat
Published: September 2022

Cancer is a disease in which cells in body grow and divide beyond the control. Breast cancer is the second most common disease after lung cancer in women. Incredible advances in health sciences and biotechnology have prompted a huge amount of gene expression and clinical data. Machine learning techniques are improving the prior detection of breast cancer from this data. The research work carried out focuses on the application of machine learning methods, data analytic techniques, tools, and frameworks in the field of breast cancer research with respect to cancer survivability, cancer recurrence, cancer prediction and detection. Some of the widely used machine learning techniques used for detection of breast cancer are support vector machine and artificial neural network. Apache Spark data processing engine is found to be compatible with most of the machine learning frameworks.
Machine Learning Techniques for Healthcare Applications
Authors: Rajamohana S. P.  , Dharani A.  , Anushree P.  , Santhiya B.  , Umamaheswari K.
Published: September 2022

Autism spectrum disorder (ASD) is one of the common disorders in brain. Early detection of ASD improves the overall mental health, which is very important for the future of the child. ASD affects social coordination, emotions, and motor activity of an individual. This is due to the difficulties in getting self-evaluation results and expressive experiences. In the first case study in this chapter, an efficient method to automatically detect the expressive states of individuals with the help of physiological signals is explored. In the second case study of the chapter, the authors explore breast cancer prediction using SMO and IBK. Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight. In this proposed system, the tumor is the feature that is used to identify the breast cancer presence in women. Tumors are basically of two types (i.e., benign or malignant). In order to provide appropriate treatment to the patients, symptoms must be studied properly, and an automatic prediction system is required that will classify the tumor into benign or malignant using SMO and IBK.
Breast Cancer Detection Using Hybrid Computational Intelligence Techniques
Authors: Acharjya Debi Prasanna , Chowdhary Chiranji Lal
Published: September 2022

Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This chapter highlights two hybridizations pertaining to breast cancer. In one hybridization technique, it hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction methods. In the second case, intuitionistic fuzzy histogram hyperbolization is hybridized with possibilistic fuzzy c-mean clustering algorithm. Both hybridizations are studied to extract the region of interest and then to enhance the edges surrounding it. Experimental analysis is carried out for both models and an exhaustive study on these models is presented in this chapter.
Computational Studies in Breast Cancer
Authors: Lamba Monika , Munjal Geetika , Gigras Yogita
Published: September 2022

Early detection of breast cancer is a worldwide need as many hospitals have appeared in commitment of research pathway. As per WHO (World Health Organisation), early detection of breast cancer boosts the choice of making corrective judgement on medication plan. This corrective choice helps women to save themselves from expensive and unwanted medical test and treatment. Physical observation and medical history play an important role in diagnosing this disease; however, for detailed understanding, some reliable and accurate methods are still required. This chapter reviews existing computational methods and need of novel algorithms that can help in accurately diagnosing this disease. Correct diagnosis and yield results devising treatment strategy. For correct diagnosis micro-array gene expression data is widely used, this chapter highlights various computational studies done on breast cancer microarray data. This review highlights the benefit of computational model being an impressive tool for discovery of cancer along with devising its therapies.
Breast Ultrasound Image Processing
Authors: Ramamoorthy Strivathsav Ashwin , Gopi Varun P.
Published: September 2022

Breast cancer is a serious disease among women, and its early detection is very crucial for the treatment of cancer. To assist radiologists who manually delineate the tumour from the ultrasound image an automatic computerized method of detection called CAD (computer-aided diagnosis) is developed to provide valuable inputs for radiologists. The CAD systems is divided into many branches like pre-processing, segmentation, feature extraction, and classification. This chapter solely focuses on the first two branches of the CAD system the pre-processing and segmentation. Ultrasound images acquired depends on the operator expertise and is found to be of low contrast and fuzzy in nature. For the pre-processing branch, a contrast enhancement algorithm based on fuzzy logic is implemented which could help in the efficient delineation of the tumour from ultrasound image.
Breast Cancer Lesion Detection From Cranial-Caudal View of Mammogram Images Using Statistical and Texture Features Extraction
Authors: Kavya N  , Sriraam N  , Usha N  , Hiremath Bharathi , Suresh Anusha , Sharath D  , Venkatraman B  , Menaka M
Published: September 2022

Breast cancer is the most common cancer among women in the world today. Mammography screening gives vital information about normal and abnormal regions. The task is to detect the lesion in mammograms using computer-aided diagnosis techniques. The automated detection of cancer decreases the mortality rate and manual error. In this work, the statistical (mean, variance, skewness, kurtosis, energy and entropy) and tamura features (coarseness, contrast and directionality) were extracted from the Cranial-Caudal (CC) view of mammogram images collected from the M.S. Ramaiah Memorial Hospital, Bangalore. The support vector machine was used for classification. Different support vector machine kernels were used and results were tabulated. The highest accuracy was obtained for linear and quadratic kernels with 95.7% with sensitivity of 100% and specificity of 91%.
Effect of GLCM Texture Features on the Medio-Lateral Oblique (MLO) View of Digital Mammograms for Breast Cancer Detection
Authors: Usha N.  , Sriraam N.  , Kavya N.  , Hiremath Bharathi , Pujar Anupama K , Ravi Prabha , Jain Aditi , Venkatraman B.  , Menaka M.
Published: September 2022

Breast cancer is one among the most common cancers in women. The early detection of breast cancer reduces the risk of death. Mammograms are an efficient breast imaging technique for breast cancer screening. Computer aided diagnosis (CAD) systems reduce manual errors and helps radiologists to analyze the mammogram images. The mammogram images are typically in two views, cranial-caudal (CC) and medio lateral oblique (MLO) views. MLO contains pectoral muscles (chest muscles) at the upper right or left corner of the image. In this study, it was removed by using a semi-automated method. All the normal and abnormal images were filtered and enhanced to improve the quality. GLCM (Gray Level Co-occurrence Matrix) texture features were extracted and analyzed by changing the number of features in a feature set. Linear Support Vector Machine (LSVM) was used as classifier. The classification accuracy was improved as the number of features in GLCM feature set increases. Simulation results show an overall classification accuracy of 96.7% with 19 GLCM features using SVM classifiers.
Segmentation of Pectoral Muscle in Mammogram Images Using Gaussian Mixture Model-Expectation Maximization
Authors: Khoulqi Ichrak , Idrissi Najlae , Sarfraz Muhammad
Published: September 2022

Breast cancer is one of the significant issues in medical sciences today. Specifically, women are suffering most worldwide. Early diagnosis can result to control the growth of the tumor. However, there is a need of high precision of diagnosis for right treatment. This chapter contributes toward an achievement of a computer-aided diagnosis (CAD) system. It deals with mammographic images and enhances their quality. Then, the enhanced images are segmented for pectoral muscle (PM) in the Medio-Lateral-Oblique (MLO) view of the mammographic images. The segmentation approach uses the tool of Gaussian Mixture Model-Expectation Maximization (GMM-EM). A standard database of Mini-MIAS with 322 images has been utilized for the implementation and experimentation of the proposed technique. The metrics of structural similarity measure and DICE coefficient have been utilized to verify the quality of segmentation based on the ground truth. The proposed technique is quite robust and accurate, it supersedes various existing techniques when compared in the same context.
Breast Cancer Diagnosis in Mammograms Using Wavelet Analysis, Haralick Descriptors, and Autoencoder
Authors: Araujo de Santana Maira , Silva Pereira Jessiane Mônica , Wagner Azevedo da Silva Washington , Pinheiro dos Santos Wellington
Published: September 2022

In this chapter, the authors used autoencoder in data preprocessing step in an attempt to improve image representation, consequently increasing classification performance. The authors applied autoencoder to the task of breast lesion classification in mammographic images. Image Retrieval in Medical Applications (IRMA) database was used. This database has a total of 2,796 ROI (regions of interest) images from mammograms. The images are from patients in one of the three conditions: with a benign lesion, a malignant lesion, or presenting healthy breast. In this study, images were from mostly fatty breasts and authors assessed different intelligent algorithms performance in grouping the images in their respective diagnosis.
Data Mining and Machine Learning Approaches in Breast Cancer Biomedical Research
Authors: Chellamuthu Gunavathi , Kannimuthu S.  , Premalatha K.
Published: September 2022

Breast cancer is the most common invasive cancer in females worldwide. Breast cancer diagnosis and breast cancer prognosis are the two important challenges for the researchers in the medical field and also for the practitioners. If the cells in the breast start to grow without any control, it leads to cancer. Normally, the growth of the lump can be seen using x-ray. The benign and malignant breast lumps are distinguished during breast cancer diagnosis. The prognosis process predicts the period at which the breast cancer is likely to reappear in patients who have had their cancers removed. Data mining techniques and machine learning algorithms are mostly used in the whole process of breast cancer diagnosis and treatment. They utilize the large volume of breast cancer data for extracting knowledge. The application of data mining and machine learning methods in biomedical research is presently vital and crucial in efforts to transform intelligently all available data into valuable knowledge.
Mobile-Aided Breast Cancer Diagnosis by Deep Convolutional Neural Networks
Authors: Wu Jiann-Ming , Tien Chao-Yuan
Published: September 2022

After verifying the capability of deep learning for basic image recognition, this chapter further extends image recognition to App-aided breast cancer diagnosis. Human cancer has been considered as the most important health problem. For medical image recognition of breast cancer, the presented approach is no longer the same as the traditional. It needs no axioms for distinguishing malignant and benign tumors, and no hand-crafted textural descriptors for feature extraction. The goal is to develop a mobile-aided diagnosis system of directly processing raw medical images. It automatically extracts features and learn filters of a deep CNN subject to labelled medical images in advance. This chapter presents a CNN architecture for diagnosing breast cancer images, illustrating effectiveness of problem solving by designing classifiers, respectively diagnosing lobular carcinoma breast cancer against phyllodes tumor and papillary carcinoma against adenosis. The performances of two classifiers for breast cancers diagnosis are separately summarized by the testing accuracy rates of 94.9% and 87.3%.
The Economies of East Asia
Authors: Hooke Angus , Kaur Harpreet
Published: September 2022

In 2018, East Asia accounted for accounted for 31% of gross world product (GWP). A global forecasting model developed by Hooke and updated by Hooke, Alati, and Kaur for the first three chapters of this book suggests that the region's share will remain at about this level until 2050, with the effects of global labour productivity convergence more than offsetting East Asia's relatively slow work force growth. Thereafter, the share will decline to 18% by 2100 due mainly to more rapid productivity and work force growth in West Asia and Africa. China is expected to retain its position as the world's largest economy until about the middle of this century, at which time its GDP will be more than 2.5 times that of the United States. An important driver of growth in East Asia during the coming decades will be Indonesia, whose share of GWP is forecast to rise from 2.6% in 2018 to 3.8% by 2050.
Stock Market Responses to Monetary and Fiscal Policies
Authors: Erer Elif , Erer Deniz
Published: September 2022

This study analyzes the short-run and long-run effects of interaction between fiscal and monetary policies on stock market performance in four emerging Asian economies, which are China, India, Indonesia, and Malaysia, by using ARDL model. The study covers the period of 2003:Q1-2020:Q1. The findings from this study show monetary and fiscal policies play an important role in determining stock market returns. Also, the results theoretically support Richardian neutrality hypothesis for China and Indonesia, Keynesian positive effect hypothesis for India, and classical crowding out effect hypothesis for Malaysia, and interest channel of monetary transmission mechanism only for China.
The Impact of Macroeconomic Indicators on Unemployment Rate
Authors: Vladi Eneda , Hysa Eglantina
Published: September 2022

The aim of this chapter is to study the impact of the selected macroeconomic indicators on unemployment rate in the region of Western Balkan countries and, more specifically, Albania, Serbia, Macedonia, Montenegro, Bosnia-Herzegovina, and Kosovo. This research is based on the time period 2000 to 2017 and includes five countries and the econometric model used in here is panel data. Data are retrieved from official and trustable sources such as World Bank and International Monetary Fund (IMF). The methodology used is the vector autoregressive model (VAR), unit root test, Hausman test, Granger causality test. All the macroeconomic variables, inflation, interest rates, GDP, and FDI are found to have a significant impact on unemployment rate of this group of countries. The novelty of this study remains the fact that this analysis is performed for the Western Balkan countries as a group. The results can serve and can be taken into consideration when applying similar econometric analysis in the future researches or implementing new policies that influences the macroeconomic factors.
International Trade in the Realm of the Circular Economy
Authors: Jaswal Isha
Published: September 2022

A circular economy advocates the use of resources for the longest time possible and to further regenerate materials when such resources are at the end of their service life. The movement towards a circular economy brings structural changes in an economy, and this, in turn, can potentially impact the international trade regime. Consequently, the aim should be to achieve material circularity among various countries of the world. While transiting towards a circular economy, nations introduce superfluous trade restrictions and at times enter into trade disputes with trade partners. It is imperative that circular economy policies and trade policies are reciprocally supportive. International cooperation on circular economy value chains should thus be explored for coordination of quality standards of materials, promoting demand for second-hand goods and secondary raw materials, removing unnecessary regulatory barriers, and to avoid environmentally harmful activities.
Population Aging and Health Expenditures in EU Member States
Authors: Ozturk Omer Faruk
Published: September 2022

All the countries are faced with the population aging resulting from the rising life expectancy and decreasing fertility rates at the present time and in turn have experienced many social and economic implications. In this research, the authors explore the causal interaction between population aging and health expenditures in a sample of EU member economies during the 2000-2018 period through Dumitrescu and Hurlin causality analysis. The causality analysis revealed a unilateral causality from health expenditures to population aging.