Reviewerly/exHarmony huggingface dataset:
Network Optimization of Large-Scale CNNs for Applications in Autonomous Machines

In the age of automation, a growing number of devices require high dimensional data to increase the reliability of the machines to better predict and classify objects for autonomous decision-making. 3D object detection directly links to environmental interpretation and therefore builds the base for prediction and movement planning for autonomous systems. With greater computing and storage capacities at the edge level, higher-density data can be captured and analyzed, and hence better predictability of natural environments is achieved. Convolutional Neural Networks are becoming increasingly important for object detection and tracking in many autonomous systems due to the improvement of affordable edge-computing devices capable of working with higher-dimensional data, such as LiDAR sensors, in recent years. Although the capabilities of edge-computing devices are growing, the computational requirements for real-time applications are also rising. This paper proposes two types of optimizations to the networks: pruning and quantization. The proposed optimizations are applied to a base model trained on the KITTI dataset, which shows an average inference speed-up of up to 20% while minimizing the loss of performance.
Bone Cancer Identification and Separation Using K-Means and KNN Classifiers

The unregulated cell growth will lead to the dangerous and deadliest cancer disease. In the body of humans various kinds of cancer has been detected, after going on many researches. Among all these, bone cancer is the one which will spread more widely, because of this it will leads to death. There is no anticipation for the bone cancer, therefore the bone cancer detection is more critical. Nowadays, methods like data mining and the image processing methods are utilized for the most of the studies in the process of medical image analysis. Many scientific researchers have been predicted the data, related websites and the collection of knowledge from the large databases. There are many methods used in the approaches for bone cancer detection and classification like supports vector machines, Association rule mining and fuzzy theory. In this approach of segmentation k-means will be utilized for segmenting the bone regions. In further processes for bone cancer detection the segmented image will be used by the mean intensity evaluation of the area identified. To check whether there is a presence or absence of bone cancer in the medical images that is for the classification process threshold values are used. This approach can be used for jpeg images, CT scan images. The proposed work will use the K-Nearest Neighbor (KNN) classifier as a classification technique and achieved to produce better accuracy.
Big Data Security management through Task Role Based Access Control Mechanism

Big data is the term used to describe the enormous, varied, and complicated collections of information that businesses and individuals regularly gather, store, and analyze. These data may come from a variety of sources, such as social media, IoT devices, and transactional systems, and is often too large and complex to be effectively analyzed using traditional data processing methods Big data deploy a large amount of sensitive data from government and non-government organizations. This data velocity increases day by day, for storing a variety of data in the distributed environment there is a predominant software framework called Apache Hadoop. The Hadoop ecosystem provides a fault-tolerance platform that is highly scalable for storing and analyzing data. And for authorization capabilities, the Hadoop ecosystem provides Apache Ranger and Apache sentry framework. Access control is one of the important mechanisms of data security. In this paper, we introduce a model for new Access control with the collaboration of Role-based access control with task methodology. And to illustrate the framework we have a use case in IoT.
Improving the Quality and Readability of Ancient Brahmi Stone Inscriptions

inscriptions are ancient texts that were carved into stones or etched into metals. They were discovered engraved on copper plates, construction stones, and other materials in the southern areas of Asia. Stone inscriptions from ancient times have degraded over time due to various factors. There are also countless documents and manuscripts that contain valuable information. The information contained in these inscriptions is important and should be safeguarded. Our project aims to document ancient Brahmi stone inscriptions along with documents and to further improve the quality and readability of these Brahmi texts by converting them into a digital format. Once in a digital format, the stone inscriptions can be easily read, studied, and preserved. This project is paramount in preserving old inscriptions found throughout India and the world.
Sign Language Conversion using Hand Gesture Recognition

Communication is difficult for speech and hearing impaired when interacting with others. Such people struggle to communicate their ideas because not everyone can understand sign language. Gesture recognition is a perceptual computing user interface that enables computers to record and interpret human motions and execute commands based on those movements. Gesture recognition can be seen of as a method for computers to start comprehending body language, creating a more complex connection between machines and people than simple text user interfaces. To facilitate easier communication between the two, we provide experiments of gesture recognition and text translation in this study.
Recogniition and Speech Conversion of Devnagri Script using CNN

In this paper, we are introducing an innovative, efficient, and real-time handwritten text-to-speech conversion technique for the Devanagari script. It combines the concept of Optical Character Recognition (OCR) and Text to Speech Synthesizer (TTS). This type of system can be helpful for visually impaired persons, for reading the number plates of a vehicle, and for medical applications. Text extraction from colored images is a challenging task in computer vision, for that, we have trained our model using Convolution Neural Network (CNN) with 1000s of different handwriting styles written by different people and of different age groups for each character. A trained model has been used for the recognition of input characters which was taken from the gesture movement of a fingertip with blue ink. The system is developed in Python 3.6.0.
Using AI and Blockchain in Case of Algorithm Regulation

The implication of AI is determined by some basic applications where the important analysis is properly modulated as much as possible. All of the basic applications are determined by the growth of AI technologies for finding a better perspective appropriately. Fundamentally, there are different aspects based on the advantages of AI developed by the basic application on the organized basic application of the various variable perspective. In addition, all of the importance regarding the basic development basis is properly incremented on the functional approach. Examination of some basic applications to determine all of the appropriate resolutions has to be evaluated in this research article.
Eye Gesture Interface and Emotion Detection Tool

This paper gives insights on a tool which is developed using Python and OpenCV. An integration of controls in the tool whose input is the eyeball movement of a user facilitates the control of the infotainment system of an automobile vehicle. The eyeball movements and blinks are linked to the functionalities of clicking and movement of cursor for the infotainment system. The tool also tracks the emotion of the driver, thereby giving information about the emotional state of the driver during the entire drive. Just as touchscreens revolutionized our way of communicating with computers, thereby bringing about a new class of machines called smartphones, this tool is designed to create yet another way of interacting with machines and hopefully create a new race of machines designed around this specific interface.
Online Transaction Anomaly Detection Model for Credit Card Usage Using Machine Learning Classifiers

The methods for payment of an online transaction are credit or debit card, online bank transfer, e-wallets, mobile payments, etc. The process of electronic fund transfer is secure, and password protected, as claimed by the vendors. The security threats for all payment methods exist with an intention, like credit and debit card frauds. Monitoring fraudulent activities becomes more important as the number of online transactions for credit card usage grows over time. The detection of deviations from the large number of transactions in cases of frauds in credit card is desired using the classifiers of machine learning. We proposed to develop a Transaction Anomaly Detection (TAD) model for online transactions during credit card usage by customers using machine learning. The model is built to exploit and expose fraudulent transactions during online transactions at e-commerce sites. This work considers a data set from kaagle.com that has 28,4807 records of credit card transactions online with a class label. The proposed TAD model applies various machine learning algorithms to calculate the performance metrics and finds an efficient algorithm for detecting online transaction anomalies with good accuracy and recall. We observed that the XGB Classifier classifies the fraudulent transactions with an accuracy of 99.96% and a recall of 83%, which is the best suitable algorithm for this dataset.
An Analysis of Heterogeneous Device Middleware for Quality Metrics

The use of distributed computing to create middleware-based applications is growing quickly in the business. In these applications, the quality parameter is critical. Devices with different processing, storage, and output viewing capabilities are known as heterogeneous devices. Response time and security parameter considerations are two important challenges in the middleware solution for such devices. Web services keep device resources for supplying inputs to the services being executed in a distributed context. The examination of a few key qualities of service factors for middleware for heterogeneous devices with their constrained capabilities is presented in this research. The examination of the quality of service parameters utilized in current and well-liked middleware works for such devices is provided in this paper's work. Response time and security are two of the most frequently utilized quality of service criteria, and it has been discovered through survey and analysis that they deserve attention and further study. However, when these criteria were analyzed, the paper advises using these characteristics to create effective and safe middleware solutions for dependable distributed systems.
Analysing The Sentiments Of Marathi-English Code-Mixed Social Media Data Using Machine Learning Techniques

A vast amount of data is generated every day through social media platforms. Various techniques and methodologies are used to bring different forms of data to use. One such form of data is textual data generated from social media platforms in the form of chats, comments, and tweets. The term “code-mixed data” describes data that combines components of different languages or linguistic subgroups such as text written in several different languages or speech that shifts between languages. Due to increased social media use and worldwide communication, many individuals are using multiple languages in their daily communication, making this type of data even more crucial. Machine translation, speech recognition, and text categorization are just a few examples of natural language processing activities that can be performed on code-mixed data. Research on code-mixed data can also aid in the understanding of multilingual communication. In this paper, we present an empirical study on the problem of word-level language identification and text normalisation for Marathi-English code-mixed text. We have created a new dataset of 1009 sentences that exhibit code-mixing of Marathi (Romanised) and English textual data. This data was collected from Whatsapp chats and Youtube comments.
Blockchain Security Implementation using Python with NB-IoT deployment in Food Supply Chain

Both academia and Industrialists are paying keen attention to the Narrow Band Internet of Things (NB-IoT) developments in the modern era. While the Narrow Band Internet of Things is gradually being adapted for commercial purposes, it continues to raise a slew of security concerns, including those around authentication and privacy. Because blockchain technology incorporates a framework for maintaining confidence, auditing, and openness, it can be a promising tool for ensuring the Network's data exchange is secure. To enhance the level of security afforded narrowband data and to maintain their accuracy and transparency. We leverage the advantages of blockchain technology with Narrow Band Internet of Things (NB-IoT) with Food supply chain management. The system identifies the damaged agricultural product during the transportation between the Manufacturer to the Retailer and executes the python program to find the damaged product during the transportation that is used to authenticate data and generate the blockchain our scheme is deemed to possess attractive confidentiality, authentication, accountability, and quality characteristics, rendering it an ideal method for secure transportation with traceability future added in all-time possible in a decentralized and distributed manner using sha-256 secure algorithm in blockchain technology and narrowband internet of things.
Automate Lifelong Hate Speech Detection: Current Challenge In Cross-Domain Adaption

Hate speech is a phenomenon which presents offensive content that people may find on social networking sites. Recognizing and reducing inappropriate content is critical for preventing and reducing hate speech. This article highlights the diversity of possible hate speech datasets and developed challenges in four domains by investigating 27 related papers in several trusted databases. The findings point to future system development directions to trace objectionable content transformations over time on various online social platforms and domains.
An Approach for Copy-Move and Image Splicing Forgery Detection using Automated Deep Learning

Image forgery detection plays a vital role for thorough incident investigation and social media crime preventions. An innovative approach for image forgery detection by utilizing a DenseNet-201 convolutional neural network is presented. The proposed method utilizes DenseNet-201 architecture to extract features from an input image and then uses a fully connected layer to classify the image as either genuine or forged. The model is trained on a dataset of authenticated and forged images and evaluated on a separate test set. The results show that the proposed approach achieves an accuracy of 94.12 %, outperforming existing methods. It demonstrates that the proposed model can accurately detect image forgeries across a wide range of image types. Also, the model is evaluated on various image transformations, like scaling, rotation, and translation. Furthermore, the proposed approach is computationally efficient, making it suitable for real-time applications.
Review of Features and Classification for Spoken Indian Language Recognition using Deep Learning and Machine Learning Techniques

The most common and organic method of interpersonal communication is speech. Humanity has long aspired to creating a computer with human-like comprehension and communication abilities. To recognize language (words and phrases) using voice signals, multilingual countries like India must be taken into account. More research on speech has been demanded by specialists over the past ten years. Researchers require a specific database, or previously recorded collection of information, for that specific recognition system when they seek to construct it. There are several speech databases available for European languages, but only a small number for Indian languages. The several Speech Databases developed in various Indian languages for Text to Speech, Speaker Identification and Speech Identification systems are discussed in this article. To accurately identify the spoken language, first need to collect information from speech signal. In the initial step of the pre-processing phase, audio feature based approach were used, and then deep learning and machine learning classification methods. This survey will explore a variety of feature extraction methods as well as classification methods.
Hand Landmark Distance Based Sign Language Recognition using MediaPipe

The deaf and hard-of-hearing community uses sign language for communication and interaction with the external world. Sign language recognition has been an active area of research for many years, and there has been progress in both sensor-based and vision-based methods. Sensor-based methods, such as those that use gloves or other wearable devices, have historically been more accurate, but vision-based methods are becoming more prevalent due to their cost-effectiveness. The study aimed to recognize sign language words using hand pictures captured by a web camera. The mediapipe hands method was used to estimate hand landmarks, and features were generated from the distances between the landmarks. Support Vector Machine (SVM) classifiers were used for character and words classification. The study used its own dataset and it compared different scaling factors, including the distances from positions 0 to 17, 5 to 17, and 0 to 12, to determine which one worked best. The best results were found using the palm size distance (o–9). The proposed approach is economically feasible and computationally simple, requiring no specialized equipment.
The Smart Bacteriological Examination and Treatment of Urinary Tract Infections in Children using Fuzzy Logic Control

It is very important to pay attention to the discomfort and various changes in the body. Diagnosing and treating urinary tract infections is a time-consuming but important process. There are diseases that affect both men and women. Infections of the genitourinary system fall into the category of those diseases to which no one is immune. Pathogens do not distinguish people by gender, do not look at age, do not pay attention to social status. Genital infections are diseases characterized by specific changes in the body caused by certain types of pathogens, excluding sexually transmitted diseases. The treatment of genital infection directly depends on the degree of inflammation of the affected area, the type of microorganism that caused the disease, and the specific pathology that occurs in the body during the disease. The symptoms that bother the sick person are also important, because they are often very pronounced. Various pathogens can cause infectious diseases of the genitourinary system. Often there are cases where the cause of ill health is not one, but several microbes. Depending on the type of pathogens, infections are divided into two main groups - opportunistic and pathogenic.
A Novel Approach for Recognition of Face by Using Squeezenet Pre-Trained Network

A face recognition in current generation plays a vital role. Day by day new techniques are emerging for face recognition. Now a days deep learning based face recognition techniques have become very popular due to the less recognition time and fast computation by using the sequence of layers and drop out is employed for training the deep neural network for some layers for reducing the over fitting of data in neural networks. Leveraging ready-to-use pre-trained deep neural networks for specific applications, some of the layers are configured with transfer learning to perform face recognition tasks, significantly simpler and more accurate than previous techniques too. The squeezenet pre-trained convolutional neural network is used here by replacing two final layers with new layers adjusted to new data set. The Experimentation is performed on data sets such as ORL and Extended YALE B. Data is partitioned as training and validation datasets. 80% of image as training, 20% of image data is used as validating data. 97% of accuracy is achieved on training data and 92% of accuracy is obtained on validation data.
Deep Learning Technology in Film and Television Post-Production

China”s economic growth is developing rapidly, whether it is hard power or soft power, movies are often leaders in cultural output, and their production technology and film content need to be evaluated at different levels. How to grasp film and television in the new era Technical post-production technology has become more and more important. China's film and television production has not yet formed a mature industrialization system. There are no standard operating specifications and specific standards for film and television production, which leads to confusion in the application mode of film and television production and the film and television production system. The imperfect level of the film and television production industry has led to poor animation preview effects and quality. This article mainly analyzes the application of deep learning technology in film and television post-production, and uses deep learning technology to make a reasonable analysis of the current development of film and television post-production technology. The experimental results in this paper show that the application of deep learning technology in film and television post-production has increased the processing efficiency of film and television by 15%, and the film and television post-production technology combined with deep learning methods. The combination of the two features has better abstract expression capabilities, and can better learn to reflect the nature of data. More importantly, deep learning methods are more suitable for the fast-developing big data era we are now in. The amount of data is increasing. In the case of, traditional algorithms are difficult to take advantage of the amount of data, and the algorithm model of deep learning is a method that has grown and developed under this background. It can process rich data and has better generalization capabilities to adapt to More extensive scenes, better practicability. Responsible for the development and optimization of film and television post-production to the greatest extent.
Research on Target Extraction System of UAV Remote Sensing Image Based on Artificial Intelligence

In many mountainous areas, geological disasters are prone to occur, such as landslides, debris flows and landslides, which are devastating to the affected areas. However, the rescue of the disaster area is very difficult. Because it is difficult to accurately identify the complex terrain, it is impossible to accurately grasp the specific information of the disaster area in a short time. unmanned aerial vehicle (UAV) has the advantages of all-weather, high-resolution and long-distance photography, and is widely used in geological hazard monitoring. With the development of artificial intelligence technology, the accuracy of classification and recognition of remote sensing images is continuously improved, and the deep learning algorithm can be well applied to the target extraction system of unmanned aerial vehicle remote sensing images. In this paper, the Faster Region-Convolutional Neural Network (R-CNN) and YOLOv3 algorithms are compared for target detection. The results show that YOLOv3 algorithm has better vehicle target extraction accuracy compared with Faster R-CNN algorithm. The recognition accuracy of station wagon, off-road vehicle, pickup truck and engineering vehicle is 91%, 92%, 89% and 93% respectively. Therefore, applying YOLOv3 algorithm to target extraction system of UAV remote sensing image can improve the accuracy of target extraction.