Reviewerly/exHarmony huggingface dataset:
American Sign Language Letter Recognition from Images Using CNN

American Sign Language (ASL) is a complex and diverse language used by millions of individuals with hearing impairments or disabilities. Accurate and efficient recognition of ASL letters from images is crucial for effective communication and accessibility.[1] However, this is a difficult task due to different hand shapes, orientations, and lighting conditions.In this study, we present a deep learning-based approach for accurately recognizing ASL characters from images. We trained three convolutional neural network (CNN) models, namely VGG16, InceptionV3, and MobileNetV2, on a large dataset of ASL letter images. These models were chosen because they have shown impressive performance in image classification tasks in various contexts. After training, we evaluated the models on a test set of ASL letter images, achieving classification accuracies of 90.7%, 95.7%, and 98% for VGG16, InceptionV3, and MobileNetV2 respectively.Our research provides significant contributions to the field of computer vision, particularly in the recognition of ASL letters from images. Our findings highlight the potential of deep learning-based research development for improving communication technology and accessibility for individuals with hearing impairments, by providing accurate and efficient recognition of ASL letters from images.
CG-Net: A Novel End-to-end Framework for Fall Detection from Videos

The convolutional neural network (CNN) is an effective tool for fall detection. It can successfully identify the fall action from images but still has the problem of predicting in videos. We propose a novel end-to-end framework named CNN-GCN-network (CG-Net) to address the problem, which can detect fall from videos directly. The proposed CG-Net consists of two modules: pose estimation and action recognition. The former is used to process videos to obtain joint points and the latter to extract features from joint points to detect falls. Furthermore, not only joint points, but also skeletons, are used for fall detection. According to the experimental results, our system achieves 99.3 % accuracy and 99.8 % recall on Le2i Fall Detection Dataset at the frame-level in videos, which provides higher fall detection performance compared with the existing CNN-based model.
Feature level Fine Grained Sentiment Analysis for Classifying Online Restaurant Reviews

In recent years, the popularity of social networking sites has skyrocketed. Social media allows individuals to express their opinions on almost every topic. These opinions take variety of ways, such as tweets, Instagram posts, blogs, Facebook posts, and so on. The process of describing and categorizing the opinions represented in the form of post, document or comment is known as sentiment analysis. The goal of sentiment analysis is typically to determine the attitude of customers toward a good or service. Customer's feedback is critical for organizations and social media that can serve as a promising tool to enhance and improve business opportunities if reviews have been evaluated on social media in a timely manner. As a result, the purpose of this study is to examine online restaurant reviews given by customers. The proposed research makes two contributions. First, it conducts sentiment analysis (SA) and categorizes each and every comment given in the text into five labels i.e.; positive, negative, neutral, highly positive and highly negative. Second, comments are automatically characterized using text categorization techniques based on the feedback from the customers about taste, service, ambiance, food, and value for money. For training and testing, dataset available from Kaggle was used. For classification, numerous algorithms were employed, such as the Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes Classifier (NB). This method evaluates the performance of these algorithms and random forest algorithm produced the best results, with highest accuracy.
Breast Cancer Classification and Recurrence Prediction Using Artificial Neural Networks and Machine Learning Techniques

Recent surveys indicate that breast cancer is an important factor in the mortality rate of women. During the recent years, the number of breast cancer patients in India has been increased by 30% and is expected to continue to rise. The early detection, classification, and prediction of cancer recurrence can save the lives of cancer patients. Breast cancer is caused when a collection of quickly dividing cells develop into a lump or excess mass in the breast tissue and eventually into a tumour. There are two types of tumours: benign (not cancerous) and malignant (cancerous). Patients with breast cancer who undergo treatment (recovery period) often experience recurrence after treatment. The study of the timing and contributing variables of cancer recurrence can lead to successful treatment intervention. This can be achieved using machine Learning models and ANNs for diagnosis and prediction. Screening and classification methods enable health care professionals to provide personalised treatments that improve outcomes and survival rates. As a result, it is aimed at classifying features into different categories with a higher degree of accuracy, precision, sensitivity, and specificity.
MR Medical Image Enhancement: An Integration of Residual Approximation and Contrast Enhancement Approach

Magnetic Resonance Images are mainly corrupted by Gaussian and Rician noises during their acquisition, which degrades the calibre of post-processing diagnostics applied to MR data, such as segmentation, registration, morphometry etc. A pre-processing technique such as MR image enhancement is needed for precise diagnostic results. Recently, deep learning techniques are gaining much popularity in various biomedical applications due to their accuracy when trained with a huge volume of biomedical images. This article presents a deep learning-based pre-processing mechanism which integrates the residual approximation and contrast enhancement method. The denoised image is estimated using a denoising Convolutional Neural Network (CNN) and residual approximation. The contrast of the denoised image is enhanced using histogram equalization. To analyze the quality of enhanced image, metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Mean Squared Error (MSE) are considered. The experimental results on synthetic and clinical data show that the proposed method outperforms the existing methods in terms of PSNR, SSIM and MSE. The proposed method obtained a PSNR of 40dB, SSIM of 99%, and MSE of. 0052 for Gaussian noise addition and for Rician noise addition, attained a PSNR of 38 dB SSIM of 99% and MSE of. 0084 for σ = 9 %.
Vision Based Fall Detection with BRISK Feature Descriptor

Falls are the major issues which occurs unexpectedly. This can also lead sometimes to death. Elderly persons are at the top most risk and their life can also be in danger. For that purpose, proper fall detection system is needed to be developed which can detect the fall as soon as the fall event takes place. To make the system accurate, pre-processing of the images from the dataset is needed to be done properly. When a person is alone and the fall takes place, there is no one to detect the fall if the fall is major, the corresponding person will not be in the state to ask for help, in that situation this system can be used. Different machine learning classifiers namely Decision Tree, Random Forest Algorithm (RF) and KNN are used to determine the accuracy of the system. All these classifiers provide the accuracy on the basis of key points which are obtained with the help of BRISK feature descriptor. Different classifiers provided different accuracy among those classifiers Random Forest classifier provided the most highest accuracy of 75.30%.
Research on Open Source Processor and Analysis of Current Development Dilemma Based on RISC-V

ISA (instruction set architecture) is a bridge and interface between the underlying hardware of the processor and the software running on it. It is also an important abstraction layer in the computer processor. With ISA, different processor hardware implementations can be used to design processors with different performance. RISC-V is the interface specification between software and hardware, which is also the origin point of an information technology ecosystem. RISC-V is the inevitable product of computer architecture gradually moving towards openness. It brings a new paradigm for system research, i.e. software research issues can be tracked down to ISA, which expands or even subverts the traditional full-stack design theory on the system function, performance, security and other issues, showing a promising development prospect. In this paper, RISC-V is introduced in detail, including Instruction Length Encoding, instruction set, general register set, encoding format and privilege mode, etc. Several open-source processors using RISC-V architecture are introduced, and the current difficulties and breakthroughs of RISC-V are analyzed. Finally, the future development of RISC-V is prospected.
Deep Learning for Classification of Salient Object Classification in Unmanned Crime Spot

The identification of saliency objects is a crucial and difficult step in the process of identifying potential suspects from an occurrence. The first step in conducting investigations is to gather a variety of artefacts, marks, the location, and the extent of the occurrence. The examination of machine learning proved to be of great use in the creation of automatic model investigations. Markings play a significant role in the decision-making process while trying to identify things found at a unmanned crime spot (UCS). Because of the extremely sensitive nature of the investigation situation, the prompt identification of artefacts at the unmanned crime spot is of the utmost importance. The recordings gathered from CCTV cameras at the site of the incident are the primary input that the strategy takes into consideration. The conversion from video to images is the first step in being executed. In this step, structural element extraction using the Sigma-Histogram approach is carried out. The backdrop is subtracted from the image based on the feature. The morphology factor and cumulative probability feature matching are both used to recover the semantic item from the picture.
Comparative Analysis of Various Techniques of The Natural Language Processing

The field of computer science that focuses on how computers can understand and interpret human language is called “natural language processing”. In particular, it is an automated method of extracting information from human language through analysis. For organisations that place a premium on efficiency, automation is the ultimate aim, and natural language processing is key to getting there. A machine can only completely comprehend humans when it talks with them in human language; it's like speaking to someone in your mother tongue as opposed to someone you hardly understand or can barely speak. In addition to discussing the specific algorithms that are employed, this manuscript will concentrate on how a machine can really comprehend human language. This manuscript will examine text conversions in-depth and go over how natural language processing proceeds. For those who are curious about the fundamentals of comprehending and mastering natural language processing, this review essay may be helpful. NLP can be used to extract relevant information from large amounts of text data, and to summarize this information in a concise and easy-to-understand format. This can be useful for tasks such as document classification, sentiment analysis, and information extraction. NLP can also be used to translate text or speech from one language to another, allowing users to communicate with people who speak different languages. Recently Chatbots and voice assistants have become a vital part of every organisation. Another important application of NLP is that it can be used to transcribe spoken words into written text, allowing users to interact with computers and other devices using their voice. It can be challenging to fully grasp the intricate nature of human language, particularly when it comes to interpreting emotions and expressions through voice and body language in data. In the near future, it will be necessary for people to communicate with machines for both household and space work, therefore it is very vital to understand how natural language processing works.
Design and Analysis of Three Operand Binary Adder

In any electrical gadget, the logical and mathematics unit has always been the most important component. A logic and mathematics unit must have an efficient algorithmic action that includes basic arithmetic and addition in order to be relevant in contemporary advances. The three input adder appears to be the main functional unit utilised in several cryptographic and pseudo-random number bit generator (PRBG) techniques to do known algorithms. The most common three-operand addition mechanism appears to be the carrysave adder (CS3A). The cascading effect stage of the CS3A, resulted in a high network latency of O, on the other hand (n). Additionally, a parallel prefix two-operand adder like the Han-Carlson (HCA) may be utilised for three-operand addition at an additional hardware cost, considerably lowering the design time. As a consequence, an advent of high and area-efficient adder design is developed, which executes three-operand binary addition utilising pre-compute bitwise adding followed by carry prefix calculation logic, consuming much less area, low power, and reducing adder latency greatly. Many into one is an acronym for multiplexer. An electronic circuit known as a multiplexer chooses and directs one or more input signals to one or more output signals.
Atlas based breast registration and segmentation in the Mediolateral Oblique and Craniocaudal Views

In the clinical evaluation of the risk of breast cancer, breast density obtained from analyzing mammograms is of high importance. Higher breast density increases the likelihood of getting breast cancer and also the likelihood of radiologists missing small lesions. Consequently, we analyze breast mammograms to determine which mammograms need greater radiological review. In this paper, we come up with a new atlas-based segmentation technique to register and segment the breast region in mammograms. Atlas-based segmentation is a technique that seeks to segment the pertinent anatomy in medical images. This method is suitable for segmentation of images, whenever there are ambiguous relationships between regions and pixel intensities. This paper evaluates how an atlas-based method can be used to segment the breast region and breast tissue from a mammogram. In our dataset, we have exactly 50 CC images and 15 MLO images. We show the segmented images and, using the dice similarity coefficient to find the performance of the segmentation, show that the accuracy of the segmentation using atlas-based techniques are at 0.87.
Data Integrity Verification Algorithm of Accounting Informatization Cloud Based on Genetic Optimization Neural Network

As an emerging storage method, storage is favored by users due to its advantages of large storage space, high flexibility, and low storage cost. In the process of rapid development of cloud storage, its security is also particularly important, especially in terms of data integrity verification. When a user uses cloud storage, the data is stored in the cloud, and the user will no longer have any backup of the data locally. Once the cloud device fails or is attacked by an intruder, the user data will be damaged or even lost. Firstly, the accounting data informatization system based on this technology is analyzed, and the overall structure of the system and the network topology of cloud storage data are given; then the existing single-user data verification algorithm is improved, and the multi-user parallel verification algorithm is applied to accounting Informatization in order to reduce the number of verifications. The experimental results show that the multiuser parallel verification algorithm can ensure the integrity and consistency of data files.
Construction of Mobile Terminal University Laboratory Management System Based on Ant Colony Algorithm

With the transition from higher education to mass education, the number of graduates has increased dramatically, and the employment problem has become the focus of society. A large number of graduates leave a huge amount of data for colleges and universities. How to use these data to provide scientific management basis for college administrators has become an urgent problem to be solved. The development of computer technology makes it possible to solve this problem. Firstly, based on the research on the basic idea, principle, process and application scope of ant colony algorithm under swarm intelligence framework, this paper makes in-depth research on several well-known improved versions of ant colony algorithm, which provides the basis and reference for the research work of the core theoretical part and practical engineering application part of the paper. The laboratory is an important practice place for colleges and universities to cultivate high-quality talents meeting the needs of society. Experimental facilities are an important factor for the comprehensive evaluation of private colleges and universities. They are not only the places where colleges and universities carry out basic subject education for students, but also the important places for each school to carry out scientific research projects. Effective use of the laboratory can expand students’ practical ability and spread students’ thinking. The continuous advancement of information technology and the continuous exploration of scientific research and innovation projects need the support of modern computer technology. A new simulated evolutionary algorithm proposed by ant colony algorithm can meet the needs of school educational activities and improve the utilization rate of school teaching resources.
Security Risk Identification of Financial Accounting Software System Based on SVM

The application of accounting software system not only brings convenient and fast information services to human beings, but also brings many potential safety hazards because of its interactive information, resource sharing and network transmission. Learning to recognize these security risks, finding out the reasons and taking necessary preventive measures is the basic guarantee for the normal operation of the financial information system. Enterprise financial accounting information is not only the indispensable information of the capital market, but also the important information source of the government’s macro and micro management of the national economy. The authenticity of financial accounting information is its foundation and guarantee, especially important. In this paper, the framework of classification and recognition model of support vector machine (SVM) is constructed, and the risk of accounting information is identified by SVM algorithm.
Medical Image Denoising Using BAT Optimization Algorithm

Denoising is critical in medical imaging for the study of pictures, the diagnosis and treatment of illness. Image denoising approaches based on optimization are now effective, however the methods are constrained by the need for a large training set size (i.e., not successful enough for small data size). Medical picture denoising may be accomplished using the discrete wavelet transform (DWT) and a coefficient thresholding-based BAT method (CTB BAT). Denoising images by removing a residual from a noisy image yields denoised images, while most other image denoising methods start with latent clean images and work their way up to learning noise from the noisy images. Additionally, the wavelet transform is incorporated with CTB_ BAT to increase model learning accuracy and training time. Denoising strategies are compared to our model's performance in terms of peak signal-to-noise ratio and structural similarity in order to determine how well it performs compared to other medical picture denoising approaches. Our methodology outperforms other approaches in experiments, as shown by the findings.
On Applying the BRH Curve in Digital Signature Scheme

One of the most popular algorithm security solutions for ensuring the enforgeability and non-repudiation of digital data is the elliptic curve digital signature technique. The scalar multiplication process is strongly reliant on it for speed. The proposed BRH curve is used to implement the digital signature algorithm to give alternative version of elliptic curve digital signature algorithm (ECDSA). Using BRH-digital signature algorithm (BRH-DSA) reduces calculating the addition and doubling operations of points that are lying on BRH curve. On BRH-DSA, more secure communications for encryption schemes are been done in compare to the original ECDSA.
Intelligent Analysis of Sports Video Based on Fuzzy Clustering Algorithm

Because of its huge commercial value and broad application prospects, sports video analysis has attracted extensive attention from researchers at home and abroad, and has become a hot research topic in the field of multimedia. The work of this paper mainly focuses on the extraction of midlevel features of sports videos and the detection and extraction of highlights based on multimodal feature fusion, and on this basis, the exploration of personalized customization is carried out. Drawing on the basic ideas of natural language processing, we the rule-based sports video analysis method is studied, and a grammar-based video analysis system is proposed. First, we decompose the video stream into a basic sequence of shots; then, through event detection, we assign a semantic label to each shot to form a sequence of tokens; finally, through parsing and verification, we generate a directory hierarchy for the video. Compared to existing video analysis systems, our system is not only able to annotate semantic events, but also recognize the hierarchical structure of sports games. Experiments show that with multimodal fusion, our method is better able to handle semantic events in sports videos. On the basis of video semantic analysis and annotation, some exploratory research on personalized customization is carried out, and a set of sports video personalized customization system based on mobile platform is developed, which provides convenient and fast sports video personalization for mobile device users. Customized service.
SMS Spam Classification and Through Recurrent Neural Network (LSTM) model

Short messaging service (SMS) spam is the unwanted messages delivered to the inbox of mobile devices from spammers. Service providers are worried about these spam messages as their clients get dissatisfied with services due to the spam data reaching on their mobile phone. There are most of the service providers has given facility Do Not Disturb (DND) activation for their clients to save them from most of the spam messages. Even though the spam messages are not controlled fully, the delivery of such messages are unstoppable. To overcome this issue extensive research has been done. Artificial intelligence made it possible with extensive learning model and accuracy of detection. This paper is proposed to classify short messages as spam or ham based on a deep learning model. In this paper, the spam detection through Recurrent Neural Network (RNN) model, in specific Long Short Term Memory (LSTM) model is used. The dataset used for this study is extracted from Grumbletext website and it has a total 425 short messages with ‘Ham’ and ‘spam’. The LSTM model classified the SMS dataset effectively with the learning model. Experimental study showed that the model has achieved an accuracy of 88.33% accuracy on SMS spam classification with the LSTM model.