Reviewerly/exHarmony huggingface dataset:
Spatial Intuitionistic Fuzzy C-means with Calcifications enhancement based on Nonsubsampled Shearlet Transform to detect Masses and Microcalcifications from MLO Mammograms

Breast cancer is the most common form of cancer in women and the second leading cause of cancer death in this group. It is difficult, however, to diagnose cancer. Clusters of Microcalcifications and lumps are the two most prevalent indicators of breast cancer. MLO Mammograms can be processed digitally to find Masses and Microcalcifications. Most of the existing computer-aided techniques can detect either Masses or Microcalcifications, seldom both and no further measures are taken to mask the Pectoral muscle, even though its presence increases the probability of false positives. In this regard, a new work is presented to identify Masses and Microcalcifications in MLO Mammogram and specific steps applied to mask the Pectoral muscle. MLO Mammograms are analysed in this study and a technique is presented that will separate the process of identification of Masses and Microcalcifications. We separated the processing of Masses and Microcalcifications due to their size and characteristics. Microcalcifications are located by first enhancing the image with the nonsubsampled shearlet transform (NSST) and then using morphological operations to find bright spots. We also developed a unique image enhancement process and a modified spatial intuitionistic fuzzy C-means technique (SIFCM) to locate Masses. We applied this technique to the mini-MIAS database. The proposed approach has shown promising results.
Predicting Anomalous and Consigning Apprise During Heists

Nowadays, there are many problems due to robberies, and there are many solutions for this but this can't be stopped at that moment. The crime charge has increased because there aren't many security guards outside the cell. Consequently, there is a critical need for a sophisticated device that can detect an anomaly i.e., face occlusion and weapon detection are considered anomalous behavior. With the current system, the ongoing illegal behavior cannot be reported to the nearby protective authorities before the criminal may get away. With the aid of Convolutional neural network (CNN), YOLO algorithms, and SMTP for sending emails.this machine has provided a technique to identify and categories whether or not it's unusual behavior or not, and to send alerts to the security government. In which the MobileNetV2 model of CNN algorithm is used to detect the face mask of the burglar and YOLOv4 for detecting objects like guns, rifles, and knives. When compared to the ResNet50 model, MobileNetV2 gives high accuracy with less time consumption. With the number of crimes increasing as well as the lack of safety, there is a growing necessity to produce this kind of security gadget. This method helps in reporting the ongoing unlawful activity to the local government's concerned protection agency in order to prevent another robbery from occurring.
Price Prediction of Bitcoin and Ethereum - A Machine Learning Approach

India is rapidly moving towards the digitization of money in all aspects. Cryptocurrency has grown widely in India and around the world among investors for financial activities like buying, selling, and trading. According to the report submitted in 2021 by the United Nations Conference on Trade and Development, 7.3% of Indians owned cryptocurrency in 2021. In the past two years, i.e., 2020 and 2021, the value of global currencies have been falling due to the poor run of stock markets. So the investors found it very hard to cope with the economical issues. This in turn has led to a renewal of interest in digital currency. Our main target is to implement the efficient machine learning and deep learning- based models specifically Convolutional Neural Network (CNN), long short term memory(LSTM) and Gated Recurrent Units (GRU) to handle the price volatility of bitcoin and ethereum and to produce high accuracy.
Monochromatic Image Dehazing Using Enhanced Feature Extraction Techniques in Deep Learning

Images photographed in foggy weather usually have poor visibility. To mitigate this problem researchers have come up with various image dehazing techniques. Now, more than ever, high-quality images that can be used to glean maximum information from autonomous systems are in high demand. This research work uses different Deep Learning (DL) architectures to draw out essential details from the picture and localize the information recovered to reduce the haze from the picture. The paper investigates to remove the hazes from the dehazed images using DL techniques. The first task of this proposed work attempts three pre-processing techniques namely, air light estimation, contextual regularization and boundary constraint. The second task of this work is to identify the suitable DL model to extract clear images from dehazed images. Evaluation metrics are PSNR value and SSIM value are used to estimate the values of dehazed images compared with clear images. Experimental results proves that AOD-Net outperforms good result with respect to PSNR value.
Application of Artificial Intelligence in E-Marketing

Digital Markets, the market is growing and developing, and customers now have an almost infinite number of new shopping possibilities. Customers use a multitude of channels and platforms to communicate their needs, attitudes, and opinions as the need for exceptional customer experience increases throughout the entire digital journey. Artificial intelligence makes it possible to deliver personalized content while enhancing the online experience (simulated intelligence). This seemingly infinite pool of information that buyers have chosen is expanding. To obtain the data and exploit it, many marketers deploy simulated intelligence. Businesses may obtain and track precise real-time customer data with the help of artificial intelligence, and they can use this data to develop distinctive digital marketing strategies. the current and future relationships between computer science and marketing science, two different yet related fields of study. There are numerous scientific distributions on artificial intelligence (intelligence based on computers) in various fields. This amount is still insignificant in terms of digital marketing difficulties. There are several possible uses for artificial intelligence research in the field of marketing science. It stays away from being more closely tied to the particular marketing concerns that the corporate world is more aware of, such as customer behaviour through virtual entertainment, targeted commercials, virtual entertainment marketing, conversion streamlining, and predictive models that specifically handle digital marketing and artificial intelligence (man-made intelligence)
Sentiment Analysis of Twitter Data Using Big Data Analytics and Deep Learning Model

People from all over the world can express their thoughts and opinions through various online social media platforms. People use social media platforms online daily to communicate with one another and stay informed about current events. A large number of tweets covering a wide range of subjects are sent to Twitter daily. Twitter is one of the most well-known and widely used online social media platforms. Extracting features and locating trends can be accomplished through the use of machine learning algorithms. Tools and strategies designed specifically for working with large amounts of data are required to successfully extract useful information from the never-ending stream of data that is produced by Twitter. In this paper, we mainly focus on hashtag identification and identify the industry that possesses the highest share of voice. In this paper, we collect live data from Twitter by using Apache Spark. After that, we classify each tweet by making use of the machine learning techniques that are provided by the Apache Spark machine learning library. To test the model, Convolution neural network (CNN) and logistic regression (LR) are being utilized. The CNN method outperformed the Logistic Regression strategy by performing with an accuracy of approximately 95% on average and scoring 0.60 on the F1 scale. Both the accuracy and the F1 score are currently sitting at 0.59. According to the findings, real-time tweets can be evaluated considerably more quickly using the Apache Spark tool for big data as opposed to the conventional execution environment. The results show that real-time tweets can be evaluated much faster using the Apache Spark tool for big data instead of the usual execution environment.
Assessing the impact of Eight EfficientNetB (0- 7) Models for Leukemia Categorization

Leukemia is the foremost common sort of childhood cancer and accounts for roughly 25% of the pediatric cancers. These cells have been sectioned from minuscule pictures and are representative of pictures within the real-world since they contain a few recoloring clamor and light mistakes, in spite of the fact that these mistakes have generally been settled through innovative strategies. The errand of recognizing juvenile leukemic impacts from ordinary cells beneath the magnifying lens is challenging due to morphological similitude and in this way the ground truth names were clarified by oncologists to cure blood related diseases. In this research, comparison is performed on various EfficientNet models for classification of Leukemia infected blood cells. Main focus is to tackle one of the major childhood cancer types by creating an accurate EfficientNet model with precision value of 98.08% to classify normal from abnormal cell images.
Particle Swarm Bacterial Foraging Optimization method for Enhanced digital image watermarking system for data security comparison with Genetic algorithm

The primary objective of this study is to enhance the data security of digital image watermarking systems through the application of Bacterial Foraging with Particle Swarm Optimization (BF-PSO) and compare Peak Signal Noise Ratio (PSNR) with a Genetic algorithm (GA). The dataset in this paper utilizes the publicly available Kaggle database. The sample size for analysing the data security in a digital image watermarking system with enhanced PSNR was 20 (Group 1 = 10 and Group 2 = 10), and calculations were conducted using G-power 0.8, alpha and beta values of 0.05 and 0.2, and a 95% confidence interval. Bacterial foraging with particle swarm optimization (BF-PSO) and while number of samples (N=10) and Genetic algorithm (GA), where number of samples (N=10) are taken into consideration are used to analyze the digital image watermarking system with improved PSNR. The novel Bacterial Foraging with Particle Swarm Optimization (BF-PSO) has 58.30 higher PSNR when compared to the PSNR of Genetic algorithm (GA) is 40.55. The significance level of the study is p<0.05, or p=0.037. Bacterial Foraging with Particle Swarm Optimization (BF-PSO) in comparison to Genetic algorithm (GA) yields superior results in Peak Signal Noise Ratio (PSNR) when it comes to improving digital image watermarking systems and ensuring the safety of the data.
HoneyGAN: Creating Indistinguishable Honeywords with Improved Generative Adversarial Networks

Honeywords are fictitious passwords inserted into databases in order to identify password breaches. Producing honeywords that are difficult to distinguish from actual passwords automatically is a sophisticated task. We propose a honeyword generation technique (HGT) called HoneyGAN and an evaluation metric based on representation learning for measuring the indistinguishability of fake passwords, together with a novel attack model for evaluating the efficiency of HGTs. We compare HoneyGAN to state-of-the-art HGTs proposed in the literature using both evaluation metrics and a human study. Our findings indicate that HoneyGAN creates genuine-looking honeywords, leading to a low success rate for knowledgeable attackers in identifying them. We also demonstrate that our attack model is more capable of finding real passwords among sets of honeywords compared to previous works.
Integrating Implicit Feedback into Crowd Requirements Engineering – A Research Preview

[Context/Motivation] In crowd requirements engineering, users are asked specific questions (explicit pull feedback) to elicit requirements. Existing approaches collect explicit pull feedback by asking the same questions to all users. [Problem] Not all questions are meaningful for all users, e.g. regarding a functionality they have not yet used. Furthermore, without knowing the user behaviour giving rise to the feedback, it is difficult to understand the reasons for the feedback. These reasons are important for deriving requirements. [Principal ideas] Our idea is to use the user behaviour (implicit feedback) to adapt the collection of explicit pull feedback and the derivation of requirements. We embed this collection of explicit pull feedback into a novel approach that makes use of a rich palette of discussion elements from crowd-based requirements engineering to motivate user participation and to support requirements derivation. [Contribution]. To our best knowledge, this is the first approach that combines the collection of implicit feedback and explicit feedback with discussion elements from crowd-based requirements engineering. We sketch our approach and our research and evaluation plan regarding the application of the approach in the context of the interdisciplinary and large-scale research project SMART-AGE with around 500 users.
Semantic-Guided Information Alignment Network for Fine-Grained Image Recognition

Existing fine-grained image recognition works have attempted to dig into low-level details for emphasizing subtle discrepancies among sub-categories. However, a potential limitation of these methods is that they integrate the low-level details and high-level semantics directly, and neglect their content complementarity and spatial corresponding correlation. To handle this limitation, we propose an end-to-end Semantic-guided Information Alignment Network (SIA-Net) to dynamically pick out the low-level details under the guidance of accurate semantics to make selected details spatially corresponding to high-level semantics and complementary in content. Technically, SIA-Net consists of an Accurate Semantic Calibration (ASC) module for providing accurate semantics and a Discriminative Feature Alignment (DFA) module for aggregating low-level details and high-level semantics using accurate semantics generated by ASC. ASC learns the pixel-level feature shifting caused by convolutional operations, which is utilized for replacing the incorrectly highlighted semantics by shifting discriminative semantics or background features. After obtaining the accurate semantic features, DFA digs into the complementary details and simultaneously makes the selected details spatially corresponding via applying the guidance of accurate semantics to obtain the reassembly features. Finally, the reassembly features, which serve as discriminative cues, are used for more accurate discriminative region localization. Extensive experiments verify that our proposed method yields the best performance under the same settings with the most competitive approaches on CUB-birds, Stanford-Cars, and FGVC Aircraft datasets.
VLSI Architecture of DCT-Based Harmonic Wavelet Transform for Time–Frequency Analysis

The complex harmonic wavelet(CHW) is being used to directly compute frequency content with respect to time by employing discrete Fourier transform(DFT) and inverse discrete Fourier transform. However, DFT coefficients suffer severe leakage of energy from one band to another band of frequency. The leakage between bands is minimized by employing discrete cosine transform(DCT) in the harmonic wavelet transform, which leads to a better representation of the time-frequency spectrum. This paper introduces a new VLSI architecture for DCT-based harmonic wavelet for hardware implementation and prototyped on a commercially available virtex5 field-programmable gate array(xc5vlx110t). To validate the proposed implementation, its real-time captured results in the logic analyzer are verified with simulation results. The maximum operating frequency targeting the FPGA mentioned above device is reported as 114.34 MHz. The total On-Chip power of the above implementation is 1.102W, out of which 68mW is the dynamic power dissipation at a toggle rate of 12%. Finally, for the area utilization of the above implementation, its resource utilization targeting the above FPGA device is reported.
Sparse Quadratic Approximation for Graph Learning

Learning graphs represented by M-matrices via an l1-regularized Gaussian maximum-likelihood method is a popular approach, but also one that poses computational challenges for large scale datasets. Recently proposed methods cast this problem as a constrained optimization variant of precision matrix estimation. In this paper, we build on a state-of-the-art sparse precision matrix estimation method and introduce two algorithms that learn M-matrices, that can be subsequently used for the estimation of graph Laplacian matrices. In the first one, we propose an unconstrained method that follows a post processing approach in order to learn an M-matrix, and in the second one, we implement a constrained approach based on sequential quadratic programming. We also demonstrate the effectiveness, accuracy, and performance of both algorithms. Our numerical examples and comparative results with modern open-source packages reveal that the proposed methods can accelerate the learning of graphs by up to 3 orders of magnitude, while accurately retrieving the latent graphical structure of the data. Furthermore, we conduct large scale case studies for the clustering of COVID-19 daily cases and the classification of image datasets to highlight the applicability in real-world scenarios.
Inference-Domain Network Evolution: A New Perspective for One-Shot Multi-Object Tracking

The supervised one-shot multi-object tracking (MOT) algorithms have achieved satisfactory performance benefiting from a large amount of labeled data. However, in real applications, acquiring plenty of laborious manual annotations is not practical. It is necessary to adapt the one-shot MOT model trained on a labeled domain to an unlabeled domain, yet such domain adaptation is a challenging problem. The main reason is that it has to detect and associate multiple moving objects distributed in various spatial locations, but there are obvious discrepancies in style, object identity, quantity, and scale among different domains. Motivated by this, we propose a novel inference-domain network evolution to enhance the generalization ability of the one-shot MOT model. Specifically, we design a spatial topology-based one-shot network (STONet) to perform the one-shot MOT task, where a self-supervision mechanism is employed to stimulate the feature extractor to learn the spatial contexts without any annotated information. Furthermore, a temporal identity aggregation (TIA) module is proposed to assist STONet to weaken the adverse effects of noisy labels in the network evolution. This designed TIA aggregates historical embeddings with the same identity to learn cleaner and more reliable pseudo labels. In the inference domain, the proposed STONet with TIA performs pseudo label collection and parameter update progressively to realize the network evolution from the labeled source domain to an unlabeled inference domain. Extensive experiments and ablation studies conducted on MOT15, MOT17, and MOT20, demonstrate the effectiveness of our proposed model.
Underwater Image Enhancement Based on Zero-Reference Deep Network

Due to underwater light absorption and scattering, underwater images usually suffer from severe color attenuation and contrast reduction. Most mainstream underwater image processing methods based on deep learning require a large amount of underwater paired training data, leading to a complex network structure, longer training time, and higher computational cost. To address this problem, a novel Zero-Reference Deep Network for Underwater Image Enhancement (Zero-UIE) is proposed in this paper, which transforms the enhancement of an underwater image into a specific parameter map estimation by using a deep network. The underwater curve model based on the classical haze image formation principle is specially designed to remove underwater color dispersion and cast. A lightweight deep network is designed to estimate the dynamic adjustment parameters of the underwater curve model, and then adjust the dynamic range of the given image pixels according to the model. A set of non-reference loss functions are designed according to the characteristics of underwater images, which can implicitly drive the network learning. In addition, adaptive color compensation can be optionally used as the pre-processing step to further improve the robustness and visual performance. The significant contribution of the proposed method is zero reference, i.e., it does not require any paired or unpaired reference data for training. Extensive experiments on various benchmarks demonstrate that the proposed method is superior to state-of-the-art methods subjectively and objectively, which is competitive and applicable to diverse underwater conditions. Most importantly, it is an innovative exploration of zero reference for underwater image enhancement.
Breast Cancer Risk Detection using XGB Classification Machine Learning Technique

Breast cancer is cancer that forms in the breast tissue. Among all cancer deaths worldwide, many women are dying, majorly due to breast cancer. Approximately 10% of women in the world are getting affected by breast cancer at some point in their lives. Therefore, it has become a significant health problem that needs to be dealt with urgently. However, diagnosing breast cancer at early stages can increase the survival rates of patients as they can receive prompt treatment at the correct time. Additionally, it is essential to identify which type of tumor a patient has to avoid unnecessary treatment procedures. There are two types of cancers. One is benign, and the other is malignant. Both have different treatment methods, so identifying the type plays a significant role. However, the process is tiresome and may lead to disparities among pathologists. Machine learning procedures have been found to show high prediction accuracy. In order to decrease the mortality rate due to breast cancer, it is essential to predict at early stages. Wisconsin Breast cancer datasets are used for our study. Two classification models, i.e., XGB (Extreme Gradient Boosting) classifier algorithm and Gradient boosting Classifiers, are used to work on BC datasets. Datasets have been divided into training data set (80%) and test data set (20%). Initially, threefold validation methods are employed. A later prediction experiment was carried out using an XGB classifier and various performance evaluation metrics such as Confusion matrix, recall, precision, etc. By using the results obtained from performance metrics of algorithms, the best-suited model has been chosen. In our study, the XGB classifier showed the best prediction accuracy than the Light Gradient Boosting classifier (LGBM).