Reviewerly/exHarmony huggingface dataset:
NIANN: Integration of ANN with Nature-Inspired Optimization Algorithms

Artificial neural networks (ANNs) are stimulated according to the biological brain's connection of axons and dendrons. These neural networks perform a major part in the advancement of artificial intelligence and learning algorithms. Though initially used for image classification, in modern times applications of ANNs have been useful over numerous fields such as medical data mining, bioinformatics, natural language processing, time series forecasting, and in various optimization problems as well. Nature-inspired algorithms are a set of novel problem-solving approaches that are derived from various incidents occurring in nature around us. Each of the methods such as the BAT, genetic algorithm, or colony optimization methods were created by keeping a specific hard problem in mind. In recent times general purpose use of these nature-inspired algorithms has become widely popular in solving mainly optimization problems derived from the fields of NLP, machine learning, deep learning, classification, and feature selection as well. Nature-inspired algorithms mainly work by mimicking phenomena occurring in nature among various species on a macro scale. A set of nature-inspired algorithms such as the genetic algorithm family mimics the processes that occur in a microorganism such as a cell division, mutation, etc. Since these algorithms are inspired by nature and were developed keeping in mind achieving an optimal solution of a given hard problem, their application in general-purpose problems also yields satisfactory results. If an algorithm fails to achieve a satisfactory solution to a problem, it is easy to modify them according to the need of the given problem to overcome any obstacle. In this chapter, an approach is introduced that aims to combine the nature-inspired optimization algorithm with the learning model of artificial neural networks to provide a more accurate and streamlined output generation of the neural network. Nature-inspired algorithms can be used as a learning method in the ANN model. In contrast to that, an ANN can also be used as an objective function to a nature-inspired algorithm to improve its capability to generate an optimal solution. This chapter aims to explore both approaches in detail.
Hybridization of Fuzzy Theory and Nature-Inspired Optimization for Medical Report Summarization

It is becoming increasingly challenging to construct a smart medical system because of the large amount of accumulating information in the scientific literature of the biomedical sector. The current scenario reflects progress in a variety of less known regions as a means of extraction for the understanding of prevention and treatment for significant medical diseases such as COVID-19. Recently, many good scientific research publications in the biomedical arena was released using the MEDLINE/PubMed dataset. In the fields of biomedical research and healthcare, assessing these enormous data sets and extracting valuable information is a critical but difficult endeavour. Here, we attempt to retrieve relevant data from openly accessible text materials, like medical reports, journals, articles, papers, and some other research works, in the medical area. hese types of text data undergo first preprocessing in this chapter using sentence tokenization, then stopword removal, stemming operations, and ultimately vectorization using the BioBERT model. Consequently, a structured data is generated to process each report in feature extraction process and then clustered the similar sentences by Fuzzy C-means clustering. Then, using multiple similarity clustering measures and a bi-objective strength measure, defuzzify the clusters and construct the base summaries. To construct the report summary, en ensemble summarising approach has been employed by using Pareto evolutionary algorithm. The method contains two optimization methods(or functions): one dependent on the produced summary size, which is constant, and the other dependent on the IG (i.e., information gain) of the considering base summaries, which is variable. When the process of evolution converges, the strongest chromosomal solution of the ultimate population offers a desired summary report. This approach is used to generate an efficient summary from biomedical reports publically available in the MEDLINE/PubMed dataset, and finally, its performance comparing with a few similar cutting-edge techniques.
Augmented Bridges: Investigating the potential of augmented reality for the design of configurable bridges

The paper expands on the potential of using Augmented Reality (AR) for the design and customization of bridges. Following a literature review on the use of AR in architectural design, and in particular in design education, the authors discuss educational experiences gained in two digital design studios. These two courses were taught remotely at the University of Thessaly and the Frankfurt University of Applied Sciences and cross-informed each other. The teaching experiences of the authors were evaluated and subsequently used to develop the curriculum of “Augmented Bridges”, an experimental AR design studio. The aim of the paper is to present current research in AR and the interactive design process of site-specific bridges, using digital and augmented media. The studio results offered valuable insight into the educational and creative value of AR technologies in architecture.
An Optimistic Bayesian Optimization Based Extreme Learning Machine for Polycystic Ovary Syndrome Diagnosis

The most prevalent endocrine disorder that exist in 5 to 10% of the women during their fertility period is the polycystic ovarian syndrome (PCOS). Women having PCOS problem are associated with serious consequences of health issues such as heart disease, obesity, infertility, ovarian cancer, type 2 diabetes and so on. To prevent the risks associated with PCOS, there is a need to develop prediction models for the accurate identification of PCOS at early stage. This chapter aims in developing prediction model based on ELM (Extreme learning machine) and Bayesian optimization algorithm for the detection of PCOS at early stage. The class imbalance problem has been overcome using a strategy known as random oversampling. In this approach, Bayesian optimization is employed to choose the best hyperparameters of the models. The proposed architecture has been evaluated using PCOS dataset obtained from Kaggle. Further, the efficacy of the proposed ELM and Bayesian optimization algorithm has been compared with SVM, MLP, ELM and ELM and Genetic algorithm. The experimental results reveal that ELM and Bayesian optimization attained better performance of 99.31% accuracy when compared with other machine learning approaches.
Automatic orientation of historical terrestrial images in mountainous terrain using the visible horizon

Historical terrestrial images are the only visual sources documenting alpine environments shortly after the end of the Little Ice Age. Despite their unique value, they are largely unused for quantifying environmental changes because of the difficult and time-consuming estimation of the unknown camera parameters. For most images large parts of the captured scenery have vastly changed over time, making automatic feature point matching infeasible. In contrast, the visible image horizon seems to remain stable over time and hence, appears to be a suitable feature for image orientation. Since the focal length is unknown for historical terrestrial images, existing methods, focusing solely on estimating the exterior orientation of recent imagery, can not be applied. Accordingly, it was investigated if the horizon is suitable to estimate both the interior and exterior orientation of historical terrestrial images, with an accuracy comparable to manually oriented images. In a first step, the whole horizon was used to approximate the unknown camera parameters, reducing the potential search space. In the subsequent spatial resection these approximations were further refined using salient points along the horizon. We evaluated our approach using 204 manually oriented reference images. With the proposed method the accuracy of the estimated exterior orientation could be significantly improved compared to previous works. Additionally, the unknown focal length was estimated within 5% of the true focal length for 75% of the images. As historical terrestrial images are commonly used for monoplotting, the accuracy for 2400 manually selected checkpoints was evaluated. This analysis showed that for 63% of the images the same accuracy as with manually oriented images was achieved. For additional 22% the estimated camera parameters were still accurate enough to serve as initial estimates for a subsequent manual orientation. In 15% of the images our method completely failed. Due to the vastly changing scenery and oblique viewing geometry, finding the initial camera parameters, in our experience, is often the most challenging and time consuming step during manual orientation of historical images. Hence, in 85% of the images this initial step can be replaced with our method, leading to a significantly reduced effort for orienting whole collections of historical terrestrial images.
ICC++: Explainable feature learning for art history using image compositions

Image compositions are helpful in the study of image structures and assist in discovering the semantics of the underlying scene portrayed across art forms and styles. With the digitization of artworks in recent years, thousands of images of a particular scene or narrative could potentially be linked together. However, manually linking this data with consistent objectiveness can be a highly challenging and time-consuming task. In this work, we present a novel approach called Image Composition Canvas (ICC++) to compare and retrieve images having similar compositional elements. ICC++ is an improvement over ICC, specializing in generating low and high-level features (compositional elements) motivated by Max Imdahl's work. To this end, we present a rigorous quantitative and qualitative comparison of our approach with traditional and state-of-the-art (SOTA) methods showing that our proposed method outperforms all of them. In combination with deep features, our method outperforms the best deep learning-based method, opening the research direction for explainable machine learning for digital humanities. We will release the code and the data post-publication.
Treatment of Health Data

The pandemic, from the very beginning, has overwhelmed the everyday life of all people and has strongly crystallized the need to correctly manage personal data relating to the health of patients if they can be used for scientific research and the collection of statistical data. Patient consent, public interest, and public health run counter to the right to the protection of personal data of Covid-19 patients. A balance must be struck between fundamental rights such as the right to privacy, the protection of personal data, and the public interest which translates into the protection of public health.
Prison and Health Protection

Penitentiaries, since they are closed places and also extremely crowded, suffered a high risk of coronavirus infections. This chapter per the authors will analyze the policies adopted during the pandemic to face this health emergency. The right to health and the need for security involves a subtle balance between the right to health of the prisoner and the right-duty of the State to enforce the sentence. When a hard line prevailed, aimed at suspending any benefit ad libitum, determining, as it happened at the end of the first wave of the pandemic, it can cause strong repercussions on the rights of prisoners. The legislation on measures to cope with the epidemiological emergency from Covid-19 in prison seemed at times contradictory and insufficient to address the emergency situation of prisoners.
Multiple Face Recognition System Using OpenFace

The digitalization of human work has been an ever-evolving process. Student’s and employee’s attendance systems are automated by using fingerprint biometrics. Specifically covid situation has created the need for touchless attendance system. Many institutions have already implemented a face detection-based attendance system. However, the major problem in designing face-recognising biometric applications is the scalability and accuracy in time to differentiate between multiple faces from a single clip/image. This paper used the OpenFace model for face recognition and developed a multi-face recognition model. The Torch and Python deployment module of deep neural network-based face recognition was used, and it was predicated accurately in time.
Code-Level Self-adaptive Approach for Building Reusable Software Components

The code-level framework is based on the notion that separate process units it can be well defined along with the broader process of codes, which are compatible with the life-cycle model. Centric code framework is chosen to meet the needs of domain-specific organizations and special projects. The framework is also independent of any life-cycle model such as a waterfall model or spiral model. We are able to create adaptive components to show the form and the content of each type of artifact related to the software, including documentation, code, and test plan specifications of the component. This article presents the building of adaptive code-level reusable software components. A set of related components can be adaptive to create or modify a sample work product. For the work product, each product components are adaptive in their nature.
Computer Vision Technique to Detect Accidents

Detecting accident in smart cities is hypothetical task in day-to-day life. It is hard to control by traffic police; the police cannot be available for all the 24 * 7 (all the month). Due to this, many accidents are passing by in everyone life. Many humans are losing their life due to lack of first aid support from the hospital. It takes at least 5 min to pass the accident information to the hospital; hence to overcome this problem, we have used computer vision technique to identify the accident in specific location, and the messages will be passed automatically to the nearby hospital. When the accident is detected the local hospital, and patrol is intimated by the Gmail else by SMS through SMTP to take necessary action. Using deep learning techniques, we are able to achieve a promising solution to this problem.
Analysis of Cuckoo Search Technique for Solving Problems of Optimization

“Cuckoo Search” was developed by Xin-She Yang and S. Deb in 2009. It is nature inspired approach relies on some species brood parasitism. Cuckoo Search in combined with Levy flights is firstly applied on several nonlinear constrained benchmark functions, and its results are outstanding comparing to other existing algorithms. Cuckoo Search parameters are initially maintained fixed for a specific amount of time, which reduces the algorithm’s efficiency. To address this problem, a mechanism for fine-tuning the parameters of Cuckoo Search has been developed. For tackling engineering optimization issues, CS is a very effective and reliable method.
Analysis of Disaster Tweets Using Natural Language Processing

Now-a-days social media has become a crucial part of life. Twitter is a social networking site on which people post and interact with messages renowned as tweets. Users registered officially can tweet, like and re-tweet messages. During the emergence of a disaster or crisis social media has become a significant means of communication. The widespread use of mobile phones and other forms of communication allows individuals to express and alert others about real-life disasters. Such knowledge relating to disasters spread over the media could save thousands of individuals by warning others and allowing them to take the required actions. It is being worked on by many firms to analyze tweets and observe tweets relating to disasters and emergencies using programming. Such efforts may be useful to loads of people using the internet. However, this effort has other problems, such as detecting and distinguishing catastrophe tweets from non-disaster tweets. Often the data available in twitter is not structured so processing is to be done on the data to classify data as ‘disaster’ and ‘non-disaster’. This paper deals with developing a model that can tell if a user is sharing data about a disaster. The data set used includes 10,000 tweets along with classifiers. This Optimized SVM model pre-processes the data using Natural Language Processing (NLP) and then builds the classifier model that gives maximum accuracy.
Mobile-Based Selfie Sign Language Recognition System (SSLRS) Using Statistical Features and ANN Classifier

This work is to bring mobile-based sign language recognition system into real time. Selfie sign videos are captured with smartphone front camera. Morphological gradients along with Sobel edge operators are used to extract hand contour from each sign video frame. Discrete cosine transform (DCT) of hand contour is optimized by principle component analysis (PCA) to reduce the execution time. The four statistical features such as mean, skewness, standard deviation, and kurtosis are calculated for the optimized hand contour DCT. The feature vector formed with these four statistical features is used for sign classification using artificial neural networks (ANN) classifier. The performance of SSLRS is evaluated with the word matching score (WMS).
Smart Voting System with GSM Module

India is the one among the largest democratic country where in the voting process is considered very crucial and essential. India is the second most populated country which brings in the requirement of an effective voting system to avoid any kind of impersonation and negligence during this process. The researchers conducted in the year 2014 show that not more than 66.4% of the citizens cast their votes. In certain regions, there is still the practice of using the traditional electronic voting machine (EVM) which is considered not so efficient and can lead to various types of fraud. Biometrics verification is the process that validates each of the voter to proceed with the voting process. Further, a methodology is explained which verifies every voter with their respective IDs and the existing database using biometrics and provide the voters with their voting status through an SMS to the registered number.
Multi-network for Joint Detection of Dynamic and Static Objects in a Road Scene Captured by an RGB Camera

This study presents a unified network to localize dynamic, static traffic objects and pedestrians, classify traffic light colors, detect drivable area and lane line simultaneously. In the network architecture, traffic object branch is created to classify dynamic objects such as cars, trucks, buses, motorcycle, and bicycle. Static objects are categorized by traffic sign and traffic light objects. Pedestrians are also localized as a separate traffic object group. Traffic light is classified correctly when it is visible. The network design has a unified architecture, one shared encoder for feature extraction and three decoders for three tasks. For benchmarking purposes, the BDD100K dataset is used. The presented model is ranked in the second place for drivable area segmentation, lane line detection, and inference speed while benchmarking with publicly available multi-networks. In comparison with respect to state-of-the art segmentation models re-trained with BDD100K dataset, the task of dynamic object localization’s MIoU metric is reached to the level of 73.54%, which is 40% higher than the results of re-trained segmentation methods.
Deep Learning for Breast Cancer Diagnosis Using Histopathological Images

Deep learning capabilities with convolution neural networks are unlimited in achieving absolute learning results in all kinds of medical imaging methods. The algorithmic mechanism on datasets is prudent enough to qualify their efficiency. Many random textures and structures are found in the histopathological images of breast cancer that deal with multi-color and multi-structure components. Most of the experiments performed in the wet labs derive results conventionally, but when assisted with the computation models of learning, the accuracy, reliability, and specificity of the results are boosted empirically. The process of employing the computational methods using convolution neural networks in parallel to the conventional experimentation of classification for diagnosing malign breast cancer images attains satisfactory results for effective decision-support.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing Publishing. • Type of peer review: Single Anonymous • Conference submission management system: Morressier • Number of submissions received: 24 • Number of submissions sent for review: 4 • Number of submissions accepted: 23 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 95.8 • Average number of reviews per paper: 1 • Total number of reviewers involved: 1 • Contact person for queries: Name: Roger Paul Webb Email: r.webb@surrey.ac.uk Affiliation: University of Surrey - Surrey Ion Beam Centre
PIRAP: Security Assessment Framework for Heterogeneous Web Service Composition

In recent times, enterprise environments such as E-banking, E-commerce, etc., are greatly affected by the evolution of web services and their associated attacks. The process of web service composition gives rise to security issues due to the incompatibility standards of the Simple Object Access Protocol (SOAP) and representational state transfer (REST) features and the compromising service-level agreement (SLA) between the end parties which has not been considered yet. Also, the security assessment process needs to cooperate with good security standards and web services. In case of heterogeneous web services, assessment done using machine learning technologies makes use of pattern analysis where this does not work with the evolving trend behind the web service environment. Hence, there is a need for a security assessment model which will incorporate finite state machine (FSM) in its assessment process to keep track of the various WS-attacks that arise in the web application via web services in heterogeneous environments. The proposed model does the web service composition by compromising all the security conflicts that arise due to the usage of dissimilar web services. We experimented the concept by testing several web services and observed that the performance of the proposed framework in attack detection is accurate and automatic thereby achieving traceability and computability metrics.