Reviewerly/exHarmony huggingface dataset:
Navigation and Trajectory Planning Techniques for Unmanned Aerial Vehicles Swarm

Navigation and trajectory planning algorithms is one of the most important issues in unmanned aerial vehicle (UAV) and robotics. Recently, UAV swarm or flying ad-hoc network which have much interest and extensive attentions from aviation industry, academia and research community, as it becomes one of the great tools for smart cities, rescue/disaster managements and military applications. UAV swarm is a scenario makes the UAVs interacted with each other. The control and communication structure in UAVs swarm require a specific decision to improve the trajectory planning and navigation operations of UAVs swarm. In addition, it requires high processing time and power with resources scarcity to efficiently operates the flights plan. Artificial intelligence (AI) is a powerful tool for optimization and accurate solutions for decision and power management issues. However, it comes with high data communication and processing. Leveraging AI with navigation and path planning it gives much adding values and great results for the system robustness. UAV industry moves toward the AI approaches in developing UAVs swarm and promising more intelligence UAV swarm interaction, according to the importance of this topic, this chapter will provide a systematic review on AI approaches and most algorithms those enable to developing the navigation and trajectory planning strategies for UAV swarm.
Efficient Machine Learning of Mobile Robotic Systems Based on Convolutional Neural Networks

During the last decade, Convolutional Neural Networks (CNNs) have been recognized as one of the most promising machine learning methods that are being utilized for deep learning of autonomous robotic systems. Faced with everlasting uncertainties while working in unstructured and dynamical real-world environments, robotic systems need to be able to recognize different environmental scenarios and make adequate decisions based on machine learning of the current environment’s state representation. One of the main challenges in the development of machine learning models based on CNNs is in the selection of appropriate model structure and parameters that can achieve adequate accuracy of environment representation. In order to address this challenge, the book chapter provides a comprehensive analysis of the accuracy and efficiency of CNN models for autonomous robotic applications. Particularly, different CNN models (i.e., structures and parameters) are trained, validated, and tested on real-world image data gathered by a mobile robot’s stereo vision system. The best performing CNN models based on two criteria—the number of frames per second and mean intersection over union are implemented on the real-world wheeled mobile robot RAICO (Robot with Artificial Intelligence based COgnition), which is developed in the Laboratory for robotics and artificial intelligence (ROBOTICS&AI) and tested for obstacle avoidance tasks. The achieved experimental results show that the proposed machine learning strategy based on CNNs provides high accuracy of mobile robot’s current environment state estimation.
A Novel Model for the Identification and Classification of Thyroid Nodules Using Deep Neural Network

The accurate diagnosis of a thyroid nodule is essential due to an increase in the number of cases of malignant thyroid nodules. This research work presents a novel model for the identification and classification of thyroid nodules using deep neural network (DNN). The proposed model works in the following four phases (i) data collection (ii) pre-processing (iii) feature extraction and (iv) classification using DNN. In this work, eight features namely mean, variance, standard deviation, skewness, contrast, correlation, energy, and homogeneity are extracted using intensity and grey-level co-occurrence matrix (GLCM) methods. Experiments have been conducted on public and collected datasets with tenfold cross validation and 50–50% hold out method to validate the performance of the proposed model. It is inferred from the results that the proposed model performs better with an accuracy of 90.9%, sensitivity of 91.75% and specificity of 89.87% on public dataset and an accuracy of 92.85%, sensitivity of 93.68% and specificity of 91.78% on the collected dataset. It is evident that the proposed model is competitive to other state-of-the-art models for classifying thyroid nodules.
Analysis of Convolutional Neural Network Architectures for the Classification of Lung and Colon Cancer

Cancer is the most common disease, which leads to death worldwide. Colon, prostate, breast, bladder, and lung are the common types of cancer among men and women. This work helps researchers to compose an automated classification of lung and colon cancer using histopathology images. This study compares various Convolutional Neural Network (CNN) architectures for image classification using the feature extraction method for the LC25000 dataset. In this method, a pre-trained network extracts features from the dataset, and the multi-class classification approach classifies the test images. All models show excellent performance in classification. This study achieves accuracy between 97.32% to 99.79% for lung-colon cancer image classification.
Precise Stratification of Gastritis Associated Risk Factors by Handling Outliers with Feature Selection in Multilayer Perceptron Model

The aim of the study is to develop a machine learning model which can give the best risk stratification accuracy to classify the presence or absence of pathogen-associated gastritis in humans. Also, this study deals with a sequential-selection of methods and their implementations to handle outliers in the biological data. The dataset contains 21 lifestyle-dietary features that are possible risk factors for pathogen-associated gastritis disease. The Proposed Multilayer Perceptron Model (PMPM) showed highest accuracy of 92% on outliers replaced by median values + feature selection.
A Clustering and TOPSIS-Based Developer Ranking Model for Decision-Making in Software Bug Triaging

Multi-attribute decision making (MADM) is a state-of-the-art, popular technique for dealing with real-world problems. An effective decision can be made for various real-world problems involving multiple attributes to decide a proper solution. Software testing and bug fixing are essential steps in the field of software engineering. Bug triaging is a real challenge in large-scale software development. Bug triaging is the process of allocating newly reported bugs to the best developer who meets the requirements for addressing them. In this paper, a software developer ranking model based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed for an effective bug triaging process. Developers are ranked based on various criteria using the TOPSIS model for effective bug triaging. Assigning newly reported bugs to the appropriate and available software developer is a complex decision-making process. It involves consideration of multiple criteria for discovering the optimal solution. In software engineering, bug triaging refers to the process of allocating appropriate developers to a newly reported bug. This paper presents criteria for finding suitable developers using multi-criteria decision-making (MCDM) techniques. The Analytic Hierarchy Process (AHP) method is used to determine the weights of the criteria, and the TOPSIS MCDM technique is used to rank the most appropriate developer.
A QoE Framework for Video Services in 5G Networks with Supervised Machine Learning Approach

An ultra-densification suggestion has been made to meet the increased demand for the data needs in future. It is also used to slim the transmitting of the base station and refine the frequency reuse. The increase in size is anticipated but network traffic due to multimedia streaming and live streaming in 5G New Radio (NR) establishes various problems, such as Matching network operator supply, user anticipation, and difficulty in network planning and conservation. To ease the above-mentioned problems appropriate arrangement of Quality of Service (QoS)—Quality of Experience (QoE) correlation framework design is required. To measure the QoE functioning, Dynamic Adaptive Streaming over HTTP (DASH) for video and multimedia streaming usage in a real-time simulation scenario taken from 5G traces in static and mobility cases are used. Finally, a Supervised Machine Learning classification algorithm is used to forecast the user expectation which helps to enhance the QoE performance.
Nepali Voice-Based Gender Classification Using MFCC and GMM

Voice-based Gender recognition is a technique that is utilized to determine the gender category of the speaker by processing voice signals. With ongoing development in technology and Artificial Intelligence voice identification in a different language has become applicable in different sectors such as voice biometrics for security, voice search, voice to text, Voice commands to smart home devices, etc. It is one of the types of biometrics which is used to detect and authenticate the user based on their speech. The various characteristics of different speakers help to distinguish one speaker from another. This paper focuses on the implementation of Nepali Voice-based gender classification using MFCC with GMM with covariance type Tied and Diagonal. With covariance type as tied, we got an accuracy of 93.8% and with covariance type as diagonal, we got an accuracy of 94.16%.
Accurate Estimation of 3D-Repetitive-Trajectories using Kalman Filter, Machine Learning and Curve-Fitting Method for High-speed Target Interception

Accurate estimation of trajectory is essential for the capture of any high-speed target. This chapter estimates and formulates an interception strategy for the trajectory of a target moving in a repetitive loop using a combination of estimation and learning techniques. An extended Kalman filter estimates the current location of the target using the visual information in the first loop of the trajectory to collect data points. Then, a combination of Recurrent Neural Network (RNN) with least-square curve-fitting is used to accurately estimate the future positions for the subsequent loops. We formulate an interception strategy for the interception of a high-speed target moving in a three-dimensional curve using noisy visual information from a camera. The proposed framework is validated in the ROS-Gazebo environment for interception of a target moving in a repetitive figure-of-eight trajectory. Astroid, Deltoid, Limacon, Squircle, and Lemniscates of Bernoulli are some of the high-order curves used for algorithm validation.
UAV Path Planning Based on Deep Reinforcement Learning

Currently, UAV has been used for the military and civil purposes, especially the rotor UAV, which has the ability of vertical take-off and landing, has six degrees of freedom and can hover in the air. Because of its high mobility, it has become a working platform for various environments with different purposes. When UAV performs autonomous flight mission, the static and dynamic obstacle environment occurs, therefore, research on effective obstacle avoidance and path planning technology for unknown environment is very important. Traditional path planning technology needs to rely on map information and high real-time algorithm, which requires huge storage space and computing resources. In this chapter, the author studies the deep reinforcement learning algorithm for UAV path planning. In view of the current challenges faced by UAVs in autonomous flight in obstacle environments, this chapter proposes an improved DQN algorithm combined with artificial potential fields, establishing a reward function to evaluate the behavior of UAV, which could guide the UAV to reach the target point as soon as possible under the premise of avoiding obstacles. The network structure, state space, action space and reward function of the DQN algorithm is designed and a UAV reinforcement learning path planning system is established. In order to better verify the advantages of the algorithm proposed in this chapter, a comparative experiment between the improved DQN algorithm and the DQN algorithm is carried out. The path planning performance of the DQN algorithm and the improved DQN algorithm in the same environment is compared. The loss function and success rate are selected as the comparison criteria. The experimental results show that the improved DQN algorithm is faster and more stable than the DQN algorithm for UAV path planning, which verifies the superiority of the improved DQN algorithm for path planning.
A Survey on Attention-Based Image Captioning: Taxonomy, Challenges, and Future Perspectives

Generating textual descriptions for images is called image captioning (IC). IC is an innovative concept of Computer Vision (CV) with several applications such as visual assistance, multi-modal search, machine vision, and many more. Enabling a system to generate image captions has witnessed an excess of progress aided by various state-of-the-art deep learning techniques. Recently, the attention-based image captioning (AIC) approaches have amplified the core field of IC with their promising results. The exponential growth of AIC approaches demands a thorough examination of its literature. There have been relatively lesser contributions in extensively surveying AIC systems up to this point. We review a comprehensive and analytical survey of AIC approaches reported in the literature and propose an alternative taxonomy for diverse AIC techniques. We also highlight a list of open challenges and directions for future work.
Adaptive Total Variation Based Image Regularization Using Structure Tensor for Rician Noise Removal in Brain Magnetic Resonance Images

Image denoising is an essential pre-processing step in medical imaging systems. In this paper, a new adaptive total variation based image regularization algorithm using structure tensor matrices is proposed to eliminate the Rician noise present in the magnetic resonance images. In the proposed algorithm a regularization term based on a variable exponent is used. The variable exponent as well as the regularization parameter in the energy functional both depend on the edge stopping function value. The edge stopping function value is based on the trace of structure tensor matrix. In the noisy inner region the value of the variable exponent becomes two that is in the inner region square of the gradient that is used as a regularization term. So the adaptive model behaves like a Tikhonov model and strong smoothing takes place in the flat inner region. At the object boundaries the variable exponent’s value becomes one that is simply a gradient of an image that is used as a regularizing term. So the model behaves like a Rudin-Osher-Fatemi model and the edges are preserved well. The weight of the fidelity term in the functional is also adaptively selected depending on whether a pixel belongs to an edge or the inner region. The weight of the fidelity term is set to a large value at the edges and the small value is in the inner region. The algorithm is found to be very efficient in removing Rician noise present in the brain’s magnetic resonance images compared to the other variable exponent based adaptive total variation models such as Chen model, Erik model, Chambolle model and Qiang Chen total variation model using difference curvature both qualitatively as well as quantitatively.
QKPICA: A Socio-Inspired Algorithm for Solution of Large-Scale Quadratic Knapsack Problems

In the past few decades, diverse but generic ideas from the natural evolutionary phenomenon, swarms, animals, and birds have inspired a wide range of search and optimization meta-heuristics. These meta-heuristics have been utilized to solve many real-world complex engineering optimization problems. Recently, inspiration from human behaviour has emerged as a new paradigm to explore. Numerous algorithms mimicking human behaviour and knowledge sharing have been proposed in the literature. In this paper, a new meta-heuristic inspired by social algorithms is designed and investigated for the large-scale Quadratic Knapsack Problems (QKPs). This is dubbed as QKPICA. QKPICA utilizes new socio-politically inspired operators along with binary encoding. The results show that QKPICA provides better performance than existing binary Imperialist Competitive Algorithms (ICAs). The proposed QKPICA is quite generic, and its applicability to other combinatorial optimization problems can be attempted with advantage.
Performance Analysis of ResNet in Facial Emotion Recognition

Facial emotion recognition is a type of image classification where each face image is classified in accordance with its emotion class like happy, surprise, disgust, sad, etc. Convolutional neural network (CNN) works quite well in image classification. But CNN suffers from the vanishing gradient problem while training the model using the backpropagation algorithm. Due to the vanishing gradient problem the model stops further training as the gradient becomes vanishingly small. To solve this problem a ResNet model has been implemented and analyzed in this paper. ResNet uses an additional skip connection which solves the vanishing gradient problem and therefore ResNet can be used to create a deep neural network. The proposed model has been implemented using tensorflow and python and the performance of the model has been analyzed. The ResNet model showed an accuracy of 85.96% after being trained for 50 epochs.
Choosing Data Splitting Strategy for Evaluation of Latent Factor Models

Recommender systems predict how much a user is interested in some items. When interest’s prediction task is stated as a rating prediction task, matrix factorization techniques show state-of-the-art performance in many cases. As well as others, latent factor models are trained using only some part of a dataset, and thus may be biased depending on a chosen split. There are a plethora of data splitting strategies. But when the question is how to split data to train the most accurate latent factor model in less time, there are no existing experimental results that can help choose the most suitable strategy for a particular case. In this research, we use 15 datasets from different domains, their different states in time, and 4 different splitting strategies to compare how much the choice of a splitting strategy influences the latent factor model’s accuracy and training time. Rating prediction errors, item ranking metrics, and evaluation times are measured. According to the measurements, there is almost no significant difference between the accuracies of latent factor models trained using different data splitting strategies. There are also no findings that allow choosing a splitting strategy apriori, based only on dataset size or state in time. The usage of cross-validation drastically increases training times but gives almost no profit in comparison to other splitting strategies. As a result, we recommend using temporal splitting strategies if they fit domain-specific logic and goals.
Adopting Metaverse as a Pedagogy in Problem-Based Learning

Pedagogical practices vary from time to time based on the requirement of various academic disciplines. Course instructors are constantly searching for inclusive and innovative pedagogies to enhance learning experiences. The introduction of Metaverse can be observed as an opportunity to enable the course instructors to combine virtual reality with augmented reality to enable immersive learning. The scope of immersive learning experience with Metaverse attracted many major universities in the world to try Metaverse as a pedagogy in fields such as management studies, medical education, and architecture. Adopting Metaverse as a pedagogy for problem-based learning enables the course instructors to create an active learning space that tackles the physical barriers of traditional pedagogical practices of case-based learning facilitating collaborative learning. Metaverse, as an established virtual learning platform, is provided by Meta Inc., providing the company a monopoly over the VR-based pedagogy. Entry of other tech firms into similar or collaborative ventures would open up a wide array of virtual reality-based platforms, eliminating the monopoly and subsequent dependency on a singular platform. The findings of the study indicate that, currently, the engagements on Metaverse are limited to tier 1 educational institutions worldwide due to the initial investment requirements. The wide adoption of the Metaverse platform in future depends on the ability of the platform providers to bridge the digital gap and facilitate curricula development.
Introduction to Blockchain

Blockchain is an exciting new digital technology that has been controversial over its short existence. In this chapter, we aim to avoid all the hype and controversies around this technology and learn about its historical origin, underlying technology, and how it works. We start this chapter with a brief history of money and ledger, followed by an overview of the socio-technological movement that led to the creation of blockchain. Next, the enabling technologies that made blockchain possible are briefly discussed. Finally, a simple technical explanation is given of how this technology works. An attempt was made to keep the technical concepts as simple as possible and use schematic figures to help explain complex computer science concepts for non-technical readers.
Reducing Methane Emissions

Methane emissions represent the largest sources of direct greenhouse gas (GHG) emissions during the production of oil and gas. A variety of technically and financially viable solutions exist to reduce emissions from the flaring, venting and leakage of methane. These are direct actions the industry can take to decarbonize its supply chain in the short term, while generating revenue from a wasted natural resource worth billions of US dollars annually. However, structural shifts are needed in both the investment and operational culture for the industry to achieve the industry’s emission reduction commitments, such as under the Global Gas Flaring Reduction Partnership or the Global Methane Pledge. We discuss key areas where blockchain can act as a disruptive innovation to create a more efficient marketplace for environmental data from oil and gas operations. From organizing verifiable credentials used by data providers and verification professionals to the digitalization of verification and monitoring systems and the tracking and trade of secondary environmental attributes (i.e., carbon footprint of oil and gas products). A central theme is improving the flow of and access to information across producers, consumers, investors, and regulators, using the measurement economy to incentive the reduction of methane emissions.
Efficient Approach for Virtual Machine and Resource Allocation in Cloud Computing

Globalization has brought many changes in modern business world. Problems of enterprise oriented software applications like distribution and configuration of resources presents challenge to the traditional software sales model. Cloud computing presents the solution for such problems. With the help of cloud-based model one can earn good return by making the QoS demands of their users (customers) satisfied. In order to fulfill the infrastructure demands (like network, storage, etc.) to their users, service providers have to maintain their own hardware or they lease it from the IaaS providers. For maintaining their own hardware SaaS providers will have to incur an extra cost, but if taken on lease, zero maintenance cost involved. Moreover, if provider wants to optimize cost and gain customer satisfaction, they will have to satisfy their customers/users by maintaining service level objectives. This paper presents a model for resource allocation in such a way as to minimize cost and maximize profit by satisfying the service level needs of the customers. The model is designed to enable the service providers with an ability to cope with the changing needs of customers, mapping customer requests to infrastructure level parameters and better handling of heterogeneous VMs.