Reviewerly/exHarmony huggingface dataset:
Classification of Machine and Deep learning Techniques for Financial Fraud Detection of Healthcare Industry

The healthcare industry is one of the most visible areas with vast potential for data collection, both in terms of patient health and economics. Large-scale credit card theft occurs in the healthcare industry because of the steady improvement of electronic payment methods, and credit card fraud monitoring has proven to be a difficult monetary burden for many service providers. Therefore, the method for identifying frauds needs constant improvement. Numerous fraud incidents occur on a regular basis, each with the potential to cause significant monetary loss. Credit card numbers and other private information may be stolen in a variety of ways, including through phishing and Trojan horse programmes that act like viruses. Consequently, there has to be effective technology for detecting various forms of financial fraud. In this paper, we apply a number of machine learning and deep learning techniques to the problem of financial fraud detection of healthcare. We use a number of different algorithms, including Naive Bayes (NB), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Convolutional Neural Network (CNN), and deep Artificial Neural Network (ANN), to skew the training of other normal and irregular transaction features. Available data are utilised to assess the quality of the model. Accuracy was represented graphically as 96.1%, 94.8%, 95.89%, 97.58%, 92.3%, and 98.53% for different algorithms, depending on whether they used NB, LR, KNN, RF, CNN, or deep ANN. The findings of the comparison clearly demonstrated that the deep ANN algorithm produces superior outcomes when compared to other methods..
DL-based Visual Interpretability over Medical Images using StyleGAN

Interpretability of the underlying predictive models is essential to increase the usage of AI-based medical devices in clinical settings as they become more prevalent in imaging disciplines like radiology and histology. GradCAM is one existing heatmap-based interpretability method that solely emphasises where the predictive characteristics are located. Do not elaborate on how they affect the prediction. In this work, we suggest a a fresh approach to interpretability that can be utilised to comprehend any by displaying the changes that would be made to the supplied image in a to arrive at various predictions. An expert in medical imagery, StyleGAN to offer a mapping between pictures and latent vectors. Our technique recognizes the best direction to update the model prediction in the latent space. This direction can be changed by moving the latent representation of an input image to create a number of fresh synthetic graphics with modified forecasts. We verify our radiological pictures, and show its capacity to provide a strategy on histology and GradCAM heatmaps are not as informative as meaningful explanations. Our technique displays the patterns that the model has discovered, enabling physicians to develop rely on the model’s forecasts, find new biomarkers, and ultimately reveal possible biases.
Aircraft Detection in Satellite Images

Object detection is one of the key areas for all the researchers in the field of computer science. The research is to find the types of objects in the image and provide their temporal and spatial characteristics. In the recent times there have been a lot of improvements in the field of satellite image detection with varying data sets being available and has left high impact on the performance of such analysis. A number of algorithms have evolved over a period of time in object detection and analysis namely different versions of YOLO, CNN, DETR etc. There is a need to deploy a study which enables the performance comparison of different versions of these algorithms on specific data set to understand their efficacy. The study in the paper contributes to understanding, evaluating and analyzing the performance characteristics of YOLO v7 algorithm with varying parameters.
Performance Analysis of Convolutional Neural Network for Image Classification

In earlier days of AI, the scientists was trying to build computers systems which will have visual data. In the last few decades, we have seen lots of research has been done and have come up with a Computer Vision Field where Convolution Neural Networks plays an important role, as is the backbone of computers vision systems. Convolution Neural Network is also called ConvoNets. This paper describes the learning approach of image classification using convolution neural network with appropriate architecture of CNN model.
Quantifying Relative Turbidity Levels using Image Processing Techniques

Since the past decade, underwater imaging has become an important area of research as it finds widespread applications in the fields of seafloor exploration, resource mapping etc., with the help of Manned and Unmanned underwater vehicles. But the quality of images captured underwater majorly suffer due to light attenuation and scattering, the latter of which is caused primarily due to turbidity. The main objective of this paper is to quantify the relative turbidity present in the acquired underwater image. In this work a three step image enhancement process, which are threshold, contrast limited adaptive histogram equalization and color balancing are carried out.The third stage output is taken as one of the reference images for estimation of turbidity level of the acquired image. The other extreme is created by extraction of an appropriate region of interest from input image whose pixel intensity is averaged and set as the most turbid image. This model is translated into a working user interface application which is tested using different images acquired in varying light conditions over a wide range of depths up to 3000m to validate the efficacy of the proposed methodology.The proposed technique can be used in underwater control systems for navigation and mapping in the given location, where it is impervious to use an appropriate model that is best suited for the water conditions.
IOT Smart Stand for Smart Phones

Focusing on a particular task has been a challenge for youth in this age of smart phones which has drastically reduced the productivity. In this paper, we propose a smart IOT stand with a mobile application for youth that will help to increase the productivity and concentrate on a particular task. This system detects when a phone is kept on the smart stand and starts counting the time required for a particular task. The activity of the user is stored in the application so that the user can create daily study patterns and segregate the time with the help of tags given to a certain task. So overall it is a IOT smart stand + mobile application combo that helps to boost productivity achieve more in academic or personal endeavors.
Performance Comparison of Artificial Intelligence- Based Recommendation Systems based on Healthcare Dataset

Recommendationn system assist to provide trustworthy results to users. Hence, Recommendation systems are becoming very popular in Healthcare also. This article analyses an intelligence-based recommender system that uses classification or prediction methods to offer recommendations to HD patients in a telehealth environment. The system offers patient choice support regarding the need for medical testing based on analytics of each patient’s medical test results in records. Artificial intelligence technology-based systems might positively impact the human lifestyle of patients grief from CD (chronic diseases) by giving helpful HRSs (health recommendation systems). This article has analyzed hybridCNN and SVM models that give heart patients HD prediction and medical advice. The fuzzy in CNN is used to detect disease levels and helps generate recommendations. The initial section’s primary objective at developing a classification method that can verify the HD of a patient and predict it into different output classes like; silent ischemia, angina, chest pain, etc. The main motive of this research is to construct an AIbased recommendation system for HD patients. The simulation analysis for the research system is shown on a clinical database gathered through discussion with medical specialists from an identified hospital. The evaluation of the researched recommender model is calculated by accuracy, RMSE, SP, and SN as calculation events. The HRSs are created based on data gathered from a knowledge-based generated with the help of doctors. The research outcomes of the detection and recommender models are calculated and given accuracy with a Hybrid-CNN of 94.3 per cent.
An Exploration on Big Data Analysis and Data Mining Methods

The exploration of BIG data is one of the biggest areas in researchers currently present. It is widely accepted that the advent of big data will have far-reaching effects on many fields, including science, commerce, industry, government, society, and so on. Large, complex datasets are being created at an everincreasing rate, making it imperative to develop novel methods for analysing this ‘‘Big Data.’’ Since dealing with Big Data presents significant obstacles for the applications, data mining techniques are proving to be of tremendous assistance in the field of Big Data analytics. Analysing such massive datasets using Big Data analytics allows for valuable insights to be gleaned. All things technological, including social media, financial technology, and scientific data, contribute significantly to the exponential expansion of data in the database in the modern digital era. Data mining is the process of sifting through large amounts of data in search of useful information. As a consequence of this, the R predictive algorithm is an improved measure of the composition of the surrounding air and our Experimental Analysis into the Predictive Capability of the Mathematics Models Algorithm. In this article, we’ll look at some of the most pressing problems brought on by big data, as well as some of the ways in which those problems could be overcome.
Security Intensification in Restricted Area By Confining Unauthorized Smart Phone Applications

With the advent of Technology, Android based applications have also taken a huge leap forward and are driving most of the Businesses around the world. However, the associated hazards of Information breach due to the user behind the machine are also growing several times. A very basic Android Phone if handled by an inimical element is in itself a threat to any organization. It becomes exceedingly difficult for any organisation to allow its employees to use android-based smartphones within Office premises considering the associated menace. The situation is considerably more problematic for organisations such as Defense, Nuclear, Medical, Research, etc as they deal with a huge amount of Classified Data. Notwithstanding, these demerits involved with the usage of Smartphones are sometimes ignored or these organisations attempt to impose various other physical limitations that involve significant expenses and cause discomfort to its employees. In this light, this paper aims to provide a novel method for imposing constraints on Android-based devices in order to mitigate the aforementioned risks. This approach contributes to restrict Android applications from accessing certain smartphone resources once the host device reaches a pre-defined designated area. This can be achieved by providing the requisite permissions to the Applications. This application will have a QR Code scanner that will scan QR Codes at Entry/Exit points to disable/enable undesired Smartphone applications.
The Human Assistant System

The requirements for human assistant systems and their popularity are growing rapidly every day. With the growing trend of voice assistant systems like Alexa, Siri, the dependence on voice assistants is also increasing. A speech recognition model developed to detect and analyze voice commands and perform the required actions as and when. An algorithm-based speech recognition engine processes speech and detects spoken speech using speech processing techniques. Object detection and identification of different types of object in different light conditions plays a major role in any system, an object detection model built based on a machine learning algorithm that is trained with enough data set to identify 80 different objects with good accuracy. Navigation is a fundamental part of all non-stationary systems, obstacles on the way increase the risk of system damage. A humanoid navigation system built to navigate a humanoid in unknown environments, with the system mainly performing the actions of obstacle detection, obstacle avoidance, and destination path planning. Finding the current location of any mobile system is of greater importance for the successful management of security as well as advanced humanoid robot applications.
Breast Cancer Classification Using Ensemble Approach, Machine Learning and Deep Learning

This research aims to use data classification, image classification, and the ensemble approach to classify and predict benign or malignant cancer cells, therefore lowering the time and expense involved with breast cancer screening. The dataset used in data classification was augmented using the Data Augmentation approach, and the augmented data is then separated into train, test, and validation. The model is then trained using CNN, SVM, KNN, and Random Forest algorithms, after which it is verified with the test data and validation parts of the dataset. The image classification models are trained using CNN and Random Forest algorithms then the models are validated by predicting images of different dataset image to check if a cell is benign or malignant. In data classification after 50 epochs of training, an accuracy of 94.73 percent was achieved for CNN in categorizing data, 92.98 percent for SVM, 90.87 percent for KNN, and 95.3 percent for Random Forest, in image classification 92.36 percent for CNN and 93.28 percent for Random Forest are achieved.
BVTracker: Multivehicle Tracking Based on Behavioral-Visual Features

In recent years, unmanned aerial vehicles (UAVs) is widely used in vehicle tracking. However, the objects in drone pictures always consist of fewer pixels due to their flying height. The lacking of visual information results in unreliable tracking results. Meanwhile, higher flying heights capture more targets, which makes it difficult to perform real-time inference with existing methods. To solve the above problems, we present a lightweight tracking model which employed behavioral information in the multivehicle tracking (MVT) problem. Besides, BVTracker utilizes visual information as well as behavioral information independently and consists of two branches. One is a trajectory prediction model based on long short-term memory (LSTM) network and self-attention network. The other one is an association model base on the Hungarian Algorithm. To enable MVT, the trajectory of each vehicle is predicted by the trajectory prediction model and the prediction results are associated with the detection findings. Moreover, a new dataset collected by UAVs for vehicles on freeways is applied to train and validate BVTracker. Compared with the state-of-art tracking algorithms, the experimental results show that the proposed method is superior to the existing algorithms at different frame rates and can achieve both robustness and real-time requirements, which profits the fast and effective traffic data analysis.
Towards More General Loss and Setting in Unsupervised Domain Adaptation

In this article, we present an analysis of unsupervised domain adaptation with a series of theoretical and algorithmic results. We derive a novel Rényi- <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> divergence-based generalization bound, which is tailored to domain adaptation algorithms with arbitrary loss functions in a stochastic setting. Moreover, our theoretical results provide new insights into the assumptions for successful domain adaptation: the closeness between the conditional distributions of the domains and the Lipschitzness on the source domain. With these assumptions, we reveal the following: if their conditional generation distributions are close, the Lipschitzness property of the target domain can be transferred from the Lipschitzness on the source domain, without knowing the exact target distribution. Motivated by our analysis and assumptions, we further derive practical principles for deep domain adaptation: 1) Rényi-2 adversarial training for marginal distributions matching and 2) Lipschitz regularization for the classifier. Our experimental results on both synthetic and real-world datasets support our theoretical findings and the practical efficiency of the proposed principles.
Joint Optimization of Security Strength and Resource Allocation for Computation Offloading in Vehicular Edge Computing

Vehicular Edge Computing (VEC) is a promising new paradigm that has attracted much attention in recent years, which can enhance the storage and computing capabilities of vehicular networks to provide users with low latency and high-quality services. Due to the open access and unreliable wireless channels, some appropriate security measures should be implemented in the VEC to ensure information security. However, the operation of the security mechanism dominates supererogatory computing resources, thus affecting the performance of VEC systems. The scarcity of computation and energy resources of the vehicles conflicts with the requirement of tasks for time delay and information security. In this paper, taking the driving velocity and position of the vehicles, the number of lanes, the model and density of the attackers, and security strength into consideration, we formulate a max-min optimization problem to jointly optimize offloading decision, transmit power, task computation frequency, encryption computation frequency, edge computation frequency, and block length to obtain optimal secure information capacity and local computation delay. The formulated optimization problem is a mixed integer nonlinear programming (MINLP), which is intractable. We apply the generalized benders decomposition (GBD)-based method to solve it. The simulation results show that our proposed algorithms have convergence and effectiveness and achieve fairness among vehicles on the road.
Optimal Distributed Filter for State and Unknown Input Estimations in Multi-Sensor Networks

This brief is aimed at estimating the state and unknown input of a linear system in the multi-sensor network. First, a distributed filter consisting of the unknown input estimator and the state estimator is proposed, in which the unknown input estimator only uses its own information while the state estimator uses its in-neighbors’ information. Second, the optimal gain matrices are obtained by employing the weighted least squares method and minimizing the performance function, which allow to realize the optimal unbiased estimation of the proposed filter. Third, a sufficient condition about the given filter’s convergence is derived. Finally, a simulation is given for validating the given methodologies.
Industrial Edge Intelligence: Federated-Meta Learning Framework for Few-Shot Fault Diagnosis

The scarcity of fault samples has been the bottleneck for the large-scale application of mechanical fault diagnosis (FD) methods in the industrial Internet of Things (IIoT). Traditional few-shot FD methods are fundamentally limited in that the models can only learn from the direct dataset, i.e., a limited number of local data samples. Federated learning (FL) has recently shown the capacity of collaborative artificial intelligence and privacy preservation. Based on these capabilities, we propose a novel approach to solve the few-shot FD problem, which includes a generic framework (i.e., FedMeta-FFD) and an easy-to-implement enhancement technique (i.e., AILR). The FedMeta-FFD framework allows clients to learn from indirect datasets owned by other collaborators while training a global meta-learner to solve the few-shot problem directly. More concretely, with only a few labeled examples and training iterations, the global meta-learner can quickly adapt to a new client (e.g., a machine under different operating conditions) or a newly encountered fault category. Adopting AILR can significantly improve the performance of the FedMeta-FFD framework while also increasing the stability of the learning process. Further, we conduct a theoretical analysis of the proposed framework's convergence in a non-convex setting. We thoroughly evaluate the proposed FedMeta-FFD on two fault diagnosis datasets and also perform the practical validation on real-world IIoT scenarios. They demonstrate that our proposed approach achieves significantly faster convergence and higher accuracy than the existing approaches.
Artificial Neural Network Based Audio Reinforcement for Computer Assisted Rote Learning

The dual-channel assumption of the cognitive theory of multimedia learning suggests that importing a large amount of information through a single (visual or audio) channel overloads that channel, causing partial loss of information, while importing it simultaneously through multiple channels relieves the burden on them and leads to the registration of a larger amount of information.In light of such knowledge, this study investigates the possibility of reinforcing visual stimuli with audio for supporting e-learners in memorization tasks.Specifically, we consider three kinds of learning material and two kinds of audio stimuli and partially reinforce each kind of material with each kind of stimuli in an arbitrary way.In a series of experiments, we determine the particular type of audio, which offers the highest improvement for each kind of material.Our work stands out as being the first study investigating the differences in memory performance in relation to different combinations of learning content and stimulus.Our key findings from the experiments are : (i) E-learning is more effective in refreshing memory rather than studying from scratch, (ii) Non-informative audio is more suited to verbal content, whereas informative audio is better for numerical content, (iii) Constant audio triggering degrades learning performance and thus audio triggering should be handled with care.Based on these findings, we develop an ANNbased estimator to determine the proper moment for triggering audio (i.e. when memory performance is estimated to be declining) and carry out follow-up experiments for testing the integrated framework.Our contributions involve (i) determination of the most effective audio for each content type, (ii) estimation of memory deterioration based on learners' interaction logs, and (iii) the proposal of improvement of memory registration through auditory reinforcement.We believe that such findings constitute encouraging evidence the memory registration of e-learners can be enhanced with content-aware audio incorporation.