Reviewerly/exHarmony huggingface dataset:
Multiple Magnification Spatial Super-Resolution Network for Light Field Images Based on EPI Solid

The Light field (LF) imaging technology can obtain the spatial information and angular information of light simultaneously. Since, the sensor captures multiple images from different angular directions, the spatial resolution of each view image is limited. LF image super-resolution (SR) methods based on deep-learning have been proposed to solve this problem. However, correlation of LF image at two angular dimensions have not been fully exploited in algorithms that based on epipolar plane images (EPIs). In this paper, we propose a joint LF image SR network by combining a multiple magnification single image SR (SISR) network and an inpainting network for EPI solids. The EPI solids, which contain information of both angular dimensions, are extracted from the output of SISR network and used as input data of the inpainting network. The inpainting network uses channel attention mechanism in feature extraction module to extracting line and gradient feature contained by EPI solids and correlations between EPI slices of EPI sold for restoring the geometric continuity of different views. Experimental results show that the proposed multiple magnification spatial LF image SR network which is trained by dataset with mixed magnifications has better performance than other approaches that are specially trained at each magnification.
Augmented Reality Long-term Tracking Based on Multi-Attention

Aiming at the problem that the augmented reality system is susceptible to complex scenes leading to the failure of tracking registration, a long-term augmented reality tracking algorithm combining attention mechanism is proposed. Firstly, the improved ResNet-50 network is used to replace AlexNet network to extract richer semantic features. Secondly, the attention-based feature fusion network effectively fuses template and search area features. The network is consisted of the dual self-attention module and the cross-attention module. The dual self-attention module effectively enhances the context information, and the cross-attention module adaptively enhances the features of the two self-attention branches. The anchorfree mechanism is introduced in classification and regression network, which greatly reduces the number of parameters. Experimental results show that the algorithm achieves high success rate and accuracy on various public datasets, and also shows good robustness in complex environments.
Smart Memory Companion for elderly

Not all age-related problems can be solved using technology. However, it has the potential to relieve a significant amount of stress on elderly and their family. In this paper we present the idea of a virtual companion, which leverages the personalized digital journal curated using historical, daily life experiences of the aged person to uplift their mental wellbeing. The virtual buddy has a dual purpose of reminiscence therapy through memory triggering and to aid in recalling. The implementation proposed in this paper is an application of natural language processing and focuses on using fine-tuned generative transformers for personalized dialog generation, sentence transformers for visual search and zero shot topic classification to build an intelligent conversational agent. The companion can be used to store memorable experiences and activities by the user, their family and caretakers in the form of text and images so as to utilize them when needed and to recollect happy glimpses of life.
Cloud Network Of a City Using Routers, Switches, IPs etc

The term "Cloud Computing" has become an in- teresting and a new buzzword in the IT industry. Cloud com- puting offers the services over the internet to its users. It also reduces the time required to do heavy resources and creates new servers which helps to construct and maintain application dynamically. There are so many users ho use cloud to store their data and they also need security for their data. So cloud computing help them to store their data and help them to secure their data. This network all about the one network the name is "Chandigarh Cloud Network" which provides network to 4 places of Chandigarh through one source.
(POIRS) Point of Interest Recommendation System using Data Mining

Nowadays, a huge amount of data is available about the location of visitors through Web sites and visitors. Travelers prefer to get important information easily and should be presented in a clear and concise manner. This paper suggests how to create a user-friendly tourist promotion system. The recommendation program is designed as an online application that can generate a personal list of popular tourist attractions. Modern technology of the old commendation system, such as collective filtering is considered to be a successful adoption in the tourism industry.
Analysis on the Estimators to the OSELM Model

The OSELM model is one of many classifiers for software defect prediction that can detect the specific oversampled input problems (pattern). OSELM was a more suitable model for defect prediction when used with the extremely unbalanced data and noisy like NDP repository datasets. Prior metrics were measured using balanced accuracy and recall. In this study, the estimator metrics for the OSELM model are G-Mean, F1-Score, and Matthews Co-Relation Coefficient
An empirical evaluation of proposed model based on CNN for handwritten digit recognition

Computer vision is an emerging field of computer science that deals with visual inputs such as images and videos. It enables computers and systems to understand and process information from videos and images as processed by human beings. Computer vision is brought to life by convolutional neural networks (CNN) and deep learning. Deep learning is a subfield of machine learning that teaches computers to learn by example. CNN is an artificial neural network that enables deep learning models to break down images into pixels which are given tags and labels. Handwritten digit recognition is the process of identifying handwritten digits by computer systems. In this paper, a CNN model is used on the MNIST dataset to identify handwritten digits. The aim of this paper is to find the model that gives maximum accuracy based on various hyper parameters such as kernel size, learning rate, batch size, number of hidden layers, epochs.
An approach to improve analysis of product reviews using sentiment analysis

Customer’s opinion holds a lot of importance for the business to succeed. The reviews and the ratings obtained are helpful to analyse the products and improve the marketing strategy for the products and gain profits. The conventional methods used for the product reviews include the star ratings system and textual feedback of the product. The latter is more descriptive and reliable and better in terms of providing genuine feedback of the product. This paper discusses and implements the sentiment analysis using Naïve Bayes model to obtain the sentiment rating of the product, later, sarcasm detection using Sequential model is integrated to obtain and improve the overall sentiment rating of the product. An aspect-based sentiment analysis (ABSA) is also integrated to analyse the product on various aspects. This methodology will help sellers and customers to save time and understand how a particular product is performing in the market. The results obtained will help them gain a clear understanding that if the product is a success or not.
Blockchain based E-Voting

In recent years, the use of the internet to vote has become popular and several governments in Europe and worldwide are intending to experiment with it. Online voting is becoming more popular in today’s culture. Many people are currently benefiting from cold digital technologies. Unlike the election system, it makes far more frequent use of paper. A standard (offline) method poses a security and transparency risk to popular elections. National elections are still centralized, with only one political party in authority. One of the main issues with traditional election systems is the organization that has complete control over the website and system, which might disrupt the database of many opportunities. Because we accept a separate system and the complete database is handled by many people, blockchain technology is one of the answers. The blockchain technology has been employed in a Bitcoin system called as a separate Bank. One of the most fraudulent sources on a website can be reduced by using a blockchain in the distribution of information on electronic voting systems. This research examines the use of a blockchain algorithm to record vote results at all polling sites. It’s no longer necessary to print ballot sheets or open polling stations. The goal of this research is to look into the use of blockchain as an electronic voting mechanism.In this paper, an e-trust voting system is divided into blockchain. The classification system means that the calculation depends on the blockchain segmentation. The voter does not have to trust the election administration in an unreliable system, and the trust is divided among all voters. The entire protocol affects system accuracy. Furthermore, all voters will have cryptographic assurance that their privacy will be safeguarded. The article covers the needs for developing electronic voting systems and identifies legal and technical barriers to employing blockchain as a monitoring service for such systems.
Handwritten Digit Recognition using Ensemble learning techniques: A Comparative performance Analysis

The detection of some digits written by humans may seem very easy; however, in reality, it is a very challenging and tedious task to extract text from real images because of the wide variety of font sizes, shapes, textures and backgrounds, etc. This study aimed to recognize handwritten digit images by extracting the features from datasets. Then theses learned characteristics are subsequently submitted to the classifier to improve the overall performance. The Bagging and boosting techniques of ensemble learning were performed by integrating different base models. Each single base model corrected the errors of the previous model. Six bagging and boosting ensemble learning algorithms were used to recognize the handwritten digit images. Therefore, with 98% of accuracy, the extreme gradient boosting machine outperformed the other methods in terms of overall performance. A detailed analysis of the allocation of data among the base learners and their overall performance, including the model correctness, computation time, and error rate, was evaluated and projected.
End-to-End Model for Heavy Rain Image Captioning

The field of image captioning has seen significant progress in the past decade. This verily makes it surprising that the models developed to do so are practically always trained to work on clear images and as a consequence, they fail to provide satisfactory captions on rainy images, especially heavy rain images. Researchers have achieved state-of-the-art results in constructing models capable of single image de-raining. But limited work has been done to perform the task of heavy rain image captioning as a whole. This paper emphasizes the development of an end-to-end architecture capable of constructing suitable captions on the MS COCO dataset containing images with added heavy rain noise. An end-to-end model is proposed for this purpose, with the use of GAN-based architecture for deraining images, Inception V3 architecture for encoding image features, and GRU-based architecture for generating captions. The proposed model can generate high-quality captions even on images with significant degradation due to rain noise.
A Novel Double-Threshold Neural Classifier for Non-Linearly Separable Applications

Classification of data finds applications in various engineering and scientific problems. When real-time operation is desired, hardware solutions tend to be more amenable as compared to algorithmic/heuristic solutions. This paper presents a novel current-mode dual-threshold neuron designed and implemented at 32nm CMOS technology node. Subsequently, a current-mode double-threshold classifier is presented which is capable of classifying input patterns of non-linearly separable problems. Thereafter, application of the current-mode dual-threshold neuron in the realization of the XOR function using only a single neural unit is discussed. The proposed neuron as well as both the applications discussed are capable of operating from sub-1V power supplies. Computer simulations using HSPICE yield promising results with the values of delay and power consumption estimated to be lower than existing circuits.
A Deep Learning based approach for MultiModal Sarcasm Detection

Sarcasm detection is used to single out natural language statements where intended meaning differs from what the surface meaning implies. A number of tasks in natural language processing areas like sentiment analysis and also mining of opinions use sarcasm detection underneath. Many of the key research in the area of sarcasm detection primarily focus on only text-based input. In the present day scenario , there has been a sudden explosion in the amount of multimodal data mainly due to social media. As a result of that, users these days are not just limited to text while expressing themselves , but also make heavy use of visuals like in images and videos. The objective of our paper is to incorporate multimodal data so as to enhance the performance of present sarcasm detection algorithms. Multiple methods that leverage data in the form of image and text, and also as a combination of both, have been presented thus far.We present a unique architecture which works on the Robustly Optimized BERT pre-training approach(RoBERTa) which is a facebook modified version of well known model BERT with a co¬attention layer on the top for including the context incongruency between input text and attributes of the image. Using Gated Recurrent Unit(GRU), the text-based features are extracted and the features of the image are retrieved by conditioning the image through Feature-wise Linearly Modulated ResNet Blocks. These combined with CLS token from the model RoBERTa are used to obtain the final predictions. In the end, we show performance enhancements from our model when it is stacked against the other latest models with competitive results which make use of the same publicly available Twitter dataset.
Dual Multi-Scale CNN for Multi-layer Breast Cancer Classification at Multi-Resolution

The resolution dependent textures & diverse color of histopathological images comprising the hard-to-discern high coherency of cancerous cells which makes it very challenging to categorize breast cancer. As such, generic learning models are proposed in the existing state-of-the-art unable to address the different magnification factor with single learning stage. Additionally, the feature extracted from the existing deep learning based models often unable to extract rotation or scale-invariant features which is a key in addressing the breast cancer from rich zoom intrinsic histopathological images. In this work, we have proposed fully-automated robust Dual Multi-Scale CNN capable of extracting linearly seperable rich scale-invariant features. Dual Multi-Scale CNN has been validated on large scale open source BreakHis dataset in different magnification factor. Additionally, our proposed model can be deployed at tertiary hospital settings as clinical deployment in hand-held smart devices for accurate BC categorization.
Smart Face Recognition Based Attendance System Using ML Algorithm

The system's goal is to make the attendance marking process quick and easy because the teacher's mundane job in class is time consuming in monitoring the students while marking attendance and ensuring that no proxy attendance is marked.To solve the problem efficiently, the system employs a machine approach that makes use of Python's OpenCV library. The Haar cascade algorithm and the LBPH algorithm are used for face detection and recognition. The system is designed to be s ufficiently accurate in comparison to other systems.
Predict the Game Analysis of Cricket Match Winning Using K-Nearest Neighbor and Compare Prediction Accuracy Over Support Vector Machine

The primary goal of this research is to forecast game analysis utilising the Novel Support Vector Machine method, compare its performance to the KNN approach. Classification was carried out using the SVM algorithm (N=10) and the KNN method (N=10), and the outcomes were contrasted depending on the precision of the two methods. The usage of this work to implement Machine Learning for the SVM and KNN algorithms. The findings demonstrated that the Novel Support Vector Machine Algorithm outperformed the KNN Algorithm in terms of accuracy.
Low-Light Image Enhancement Using Multi-Branch Deep Fusion Enhancement Network

Deficiencies like lack of visibility, blurry images, and noise affect almost all images captured in less-than-ideal lighting conditions. To cope with this issue, we propose the multi-branch Deep Fusion Enhancement Network (DFEN) that utilizes artificially generated multi-exposure images for the image enhancement operation. The model extracts feature from various levels from the different multi-exposure images and then combine the re-calibrated features to generate an overall enhanced low-light image. The model makes use of the Feature Extraction Module (FEM) to draw out features from different levels of the image, Feature Enhancement Module (EM) to enhance the features extracted, and Feature Fusion and Re-calibration Module (FFRM) to re-calibrate enhanced features, and finally merge them into an enhanced low-light image. The proposed model was evaluated on various datasets and showed to outperform various state-of-the-art techniques significantly. Additionally, numerical results for the proposed DFEN model show it outperforms other approaches in either qualitative or quantitative metrics.
Using Prediction Based Approaches to Improve Diagnostic Accuracy for BreastCancer

Diagnosis BC is a tremendous challenge in the medical domain. In ongoing exploration, the classification of imbalanced dataset certainly stands out enough to be noticed. Imbalanced classification reduces the model's performance by favoring the majority class, although we used a balanced dataset called "Diagnostic Wisconsin BC" in this research. To measure the performance, we compared the different ML algorithms. In order to deploy ML techniques the dataset was divided into train and test data. The hyper tuning settings wereemployed to boost the models' accuracy. Further the ML algorithms compare with the ensemble-based learning techniques like voting classifier, bagging and boosting. Thesecond important part of this article is feature selection for diagnosing BC based on the tumor traits chosen.
Detection of Fake Online Recruitment Using Machine Learning Techniques

There are a numerous amount of job postings on the internet and sometime these vacancy postings turns out to be fake. Even on the reputed and trusted job advertising platforms, people fall prey to these fake advertisements . After selection in the job, hiring people start demanding for money and details of bank account. Good number of candidates gets duped and lose loads of money and sometimes even their current jobs. So it would be very helpful to identify if the job listed on the website is real or fake. In this paper, we have used machine learning to detect fraud job vacancy postings.In our proposed ML technique, we have applied two data balancing techniques namely "Adaptive Sympathetic" and "Synthetic Minority Oversampling Technique" in combination with "Term Frequency-Inverse Document Frequency" which if a feature extraction method. However, in the literature, some research have used "Bag of Words" for extraction of features which could just count the no. of times of the word appeared whereas the technique used in this research work (i.e TF-IDF) also provides the importance of words. The public "Employment Scam Aegean Dataset" (EMSCAD) was used which contained around 18000 job listings out of which about 800 are fraud listings. We have used two machine-learning models such as Random Forest and k- nearest classifiers to detect the online job is face or real. We have compared the performance of proposed models with existing models and it performs better in terms of recall, precision and f1-scores.
An Automated Monitoring System for Abnormal Event Detection

Public events are source of entertainment for every people. Number of such events have been increased with increase in population. Security is the main concern of people especially in such events. Multiple surveillance systems have been utilized to keep track of security concerns and keep an eye on activities of crowd. These systems help to track people and identify suspicious activities or unexpected events occurs. In this paper, a novel method has been proposed to detect abnormal activity occurred at any in-door/out-door environment. Initially, Gaussian filter has been used as preprocessing to detect the foreground objects and background removal. Then fuzzy c-mean has been opted to verify human silhouettes and shadow removal has been performed. After that novel method for region shrinking has been implemented to isolate occluded humans and feature descriptor comprised of velocity and wavelet analysis has been extracted for each silhouettes. Feature has been optimized using Gray Wolf optimizer and abnormal event classification is performed using the XG-Boost classifier. Performance is evaluated using UMN dataset and UBI-Fight dataset, each having different a nature of anomaly. The mean accuracy for the UMN and UBI-Fight datasets is 90.14%, and 81.3% respectively. These results are more accurate as compared to other existing methods.
Evaluation of Supervised Machine Learning Models for Handwritten Digit Recognition

In the processing of information, handwriting recognition is crucial. The handwritten documents are large and the cost of their processing is also quite high. In the process of handwritten document processing, digits form an important component. Digits are used in large numbers in any document. This paper deals with the concept of recognition of handwritten digits using machine learning techniques. Various application areas of handwritten digit recognition are vehicle license-plate recognition, postal letter-sorting services, Cheque truncation system, etc. The core problem is that it is really difficult to distinguish handwritten numbers because everyone writes in their style. This paper presents an empirical analysis to compare the different supervised machine learning techniques for handwritten digit recognition and evaluate them using various evaluation measures such as accuracy, F1-Score, etc. the objectibe of this paper is to analyse the algorithms and find the best algorithms for handwritten digit recognition by improving the performance of different supervised machine learning techniques namely, Naive Bayes, k-Nearest Neighbor, Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, Convolution Neural Network, and Decision Tree.