Reviewerly/exHarmony huggingface dataset:
Jump-GRS: a multi-phase approach to structured pruning of neural networks for neural decoding

Objective.Neural decoding, an important area of neural engineering, helps to link neural activity to behavior. Deep neural networks (DNNs), which are becoming increasingly popular in many application fields of machine learning, show promising performance in neural decoding compared to traditional neural decoding methods. Various neural decoding applications, such as brain computer interface applications, require both high decoding accuracy and real-time decoding speed. Pruning methods are used to produce compact DNN models for faster computational speed. Greedy inter-layer order with Random Selection (GRS) is a recently-designed structured pruning method that derives compact DNN models for calcium-imaging-based neural decoding. Although GRS has advantages in terms of detailed structure analysis and consideration of both learned information and model structure during the pruning process, the method is very computationally intensive, and is not feasible when large-scale DNN models need to be pruned within typical constraints on time and computational resources. Large-scale DNN models arise in neural decoding when large numbers of neurons are involved. In this paper, we build on GRS to develop a new structured pruning algorithm called jump GRS (JGRS) that is designed to efficiently compress large-scale DNN models.Approach.On top of GRS, JGRS implements a 'jump mechanism', which bypasses retraining intermediate models when model accuracy is relatively less sensitive to pruning operations. Design of the jump mechanism is motivated by identifying different phases of the structured pruning process, where retraining can be done infrequently in earlier phases without sacrificing accuracy. The jump mechanism helps to significantly speed up execution of the pruning process and greatly enhance its scalability. We compare the pruning performance and speed of JGRS and GRS with extensive experiments in the context of neural decoding.Main results.Our results demonstrate that JGRS provides significantly faster pruning speed compared to GRS, and at the same time, JGRS provides pruned models that are similarly compact as those generated by GRS.Significance.In our experiments, we demonstrate that JGRS achieves on average 9%-20% more compressed models compared to GRS with 2-8 times faster speed (less time required for pruning) across four different initial models on a relevant dataset for neural data analysis.
Voice Guided, Gesture Controlled Virtual Mouse

The mouse is a prime example of HCI advancement. Although the modern wireless mouse or Bluetooth mouse still requires peripherals like energy cells and cards to connect to the computer, it is not entirely peripheral- free. The suggested AI virtual mouse system addresses the aforementioned issues by capturing hand motions with an external or digital camera, then improving the system's accuracy using voice assistants and hand point detection through object recognition. The system's foundation consists of machine learning techniques. To operate a computer, navigate, and execute actions like left- and right-clicking, digital hand motion can be used instead of a physical mouse. The program employs machine learning for hand identification and Python modules, for voice assistance. The suggested approach eliminates the need for human involvement and computer- controlled equipment in the battle against the spread of COVID-19 by implementing fundamental mouse operations together with brightness, volume control, and the ability to manage noise variations.
Crime Scene Analysis for News Headline Generation

The news headline generation of crime is a tedious task in journalism reporting that requires analysis and creative skills. Often, human observation and reasoning is subject to error or fallacies, and it is a time-consuming task. Hence an automated headline generation mechanism would greatly benefit media teams. The traditional approach of news headline generation primarily relies on textual data and not on images, which may not always provide a comprehensive understanding of the story. Generating headlines from images requires a deep understanding of the visual content and the ability to extract relevant information. Thus, there is a need for a system that can leverage the power of image detection to automatically generate news headlines that accurately reflect the visual content of an image. This research, which has been proposed by this paper could potentially revolutionize the way news headlines and articles are generated, as it would allow for a more nuanced and accurate representation of the crime scene.
Leveraging Performance of Over-the-Top Media Platforms using Blockchain Technology

Blockchain is ideal for transferring data because it provides rapid, shareable, and completely simple data that is recorded on a permanent record that is essentially gathered by organized and permission businesses. A developing e-commerce paradigm in recent years is using OTT (over-the-top) platforms to deliver digital media content. Blockchain promises to improve the security and usability of the OTT industry. But as the industry has expanded, more difficulties have emerged, such as copyright violations and the unauthorized dissemination of protected information. Blockchain technology has the ability to end the long-standing troubles that the media and OTT industries have. Leaders in the media and broadcasting industries are also hopeful that blockchain-based media solutions will help them overcome their difficulties in monetizing their material. This paper highlights the importance of Blockchain in the OTT industry, such as decentralized content distribution models, accessibility and ownership, media royalty payments, etc., and the challenges faced. It also emphasizes the different components of Blockchain-based video streaming platforms.
Quantum Fingerprint Scrambling Algorithm Based on Chaos Theory

The challenges brought about by the development of information technologies bring with them vulnerabilities and threats that are difficult to manage, especially as information technologies are globally interconnected, increasing the opportunity for cybercrime. Nowadays, sending images over the internet is routine. Protecting digital data is important to preserve its confidentiality and integrity. To be transmitted securely, images must be encrypted. Unencrypted, images are not protected and can easily be copied and forged. This paper proposes a quantum scrambling algorithm for biometric fingerprints. Therefore, starting from an adapted Novel Enhanced Quantum (NEQR) model the fingerprint was stored in quantum systems. All quantum algorithms were implemented and tested in IBM Quantum as well as on a local quantum simulator QASM.
A Comparative Analysis of Real-time Crash and Fall Detection Mechanisms based on sensor-fusion, aural inputs, and visual recognition techniques

Fall and crashes can have serious consequences to people of all ages, making the development of crash and fall detection systems a critical area of research. This survey paper aims to provide a overall view of the current trends pertaining to the field related to fall detection mechanisms and sound-based car crash detection. We have discussed about recent researches in the domain, with varied approaches to fall and crash detection systems. We analyze each system's architectural design, datasets, accuracy, and limitations. We also compare and contrast the different approaches and discuss future research directions. The results of this survey research paper will help form a basis for future research in the fall and crash detection domains and further contribute to developing effective algorithms for related prevention measures.
Controlling Mouse Movement Using Hand Gestures and Voice Commands

The use of hand gestures and voice commands for controlling computer interfaces has been gaining attention in recent years due to its potential to provide a natural and intuitive input method. This research paper explores the feasibility and usability of using hand gestures and voice commands to control mouse movement, with a particular focus on the technical aspects of implementing such a system and the challenges and limitations associated with this approach. The paper presents a review of the relevant literature, an overview of the existing technologies and applications in this field, and a series of experiments to evaluate the performance and user experience of controlling mouse movement using hand gestures and voice commands. The experimental results demonstrate that the use of hand gestures and voice commands for controlling mouse movement is technically feasible, and has the potential to provide an effective and efficient input method in a variety of contexts, such as gaming, accessibility, virtual and augmented reality, industrial and manufacturing, and healthcare. However, the paper also highlights the need for further research and development to overcome the current limitations and challenges associated with this technology. The paper concludes with recommendations for future research and development in this field, including improvements to the accuracy and reliability of hand gesture and voice recognition systems, as well as the design of intuitive and user-friendly interfaces for controlling mouse movement using these modalities regenerate response .
Collaborative Recommendation System For Gadgets

The recommendation system is the backbone or main stem, part of e-commerce marketing strategies. Globally, consumers’ attitudes toward online shopping are changing, and companies like Amazon, Flipkart, Meesho, Snapdeal, eBay, and others are among the largest winners. Suggestion systems assist online users in selecting what they want and their degree of interest in each product in the catalog list in a personalized manner. The most well-known e-commerce sites employ tactics such as a collaborative filtering selection system strategy based on user recommendations. This paper proposes a collaborative filtering recommender system based on models, and hybrid recommenders as the main techniques and we can analyze and predict how recommendation system works in E-commerce and shopkeeper to give the best gadgets. By using the hybrid recommendation, we can make more reliable predictions.
Real-time Yoga Pose Classification and Correction: YogaAI

People have acknowledged the importance of health care since the Pandemic. One of the best methods for developing a healthy lifestyle is yoga. Posture is the most important aspect of yoga practice. The user of the existing yoga position detectors was required to gaze at the screen at all times because they did not offer audible feedback. Additionally, there are currently no tools that offer detailed instructions for performing yoga. Given this, the proposed system, YogaAI seeks to use computer vision technology to overcome the shortcomings of current solutions and offer efficient and successful ones. By offering tailored yoga instructions based on the user's health status, our project aspires to serve as a self-assisted healthcare companion for the user. Real-time pose identification and classification for yoga would be possible. The system will also provide a step-by-step tutorial for the posture, feedback (visual, auditory), a counter, and a duration tracker for the particular pose.
Offline Handwritten Signature Analysis for Age Classification using Deep Features

Handwritten Signatures are prominent and convincing behavioral biometric data, used in many authentication application areas such as commercial, financial data, document analysis, health care, and forensic science etc. Different writing styles, cursive characters, and inconsistency in the shapes make it tougher to identify the writer from a handwritten signature. This problem remains still a challenging task, because of the considerable intra-class variations in handwriting. The proposed work focuses on the classification of the age using the different handwritten signatures collected from male and female writers of different age groups (adolescents (18-60)). In-house of total 6010 signatures were collected from 610 individuals. Experiments were carried out using well known deep neural architecture namely VGG16 model. Classification task carried out in end-to-end framework. The proposed work achieves comparatively better results than the existing methods respectively.
Securing Data storage in Cloud after Migration using Immutable Data Dispersion

Cloud computing has emerged as a technology behemoth with applications in a wide range of fields. When data is being migrated from offline data centres and stored in multiple cloud environments part of the control is always with the Cloud Service Providers(CSPs) leads to security concerns. The data stored in the cloud may sometimes be compromised even though the CSPs may take precautions to avoid such situations. In this paper, we discuss securely storing the data using the data dispersion technique by breaking the data into multiple segments and combining it with encryption along with replication. The division of data and storing it in the cloud helps in protecting the complete data even if an attacker tries to access the data it will not be easy for him to make sense of the retrieved data because the data is already being encrypted and combined with dispersion and replication adds to the complexity of retrieval. Security is achieved as the dispersed data is spread across multiple locations which makes it difficult for an attacker to get all the segments. In most scenarios be able it depends on traditional encryption techniques alone to protect the data. Here, We propose focusing more on how data is stored in the cloud to relieve the system of costly computational methodologies. In this strategy, the trade-off between security and the data retrieval time must also be considered.
Planetary Structures Detection and Segmentation Using Deep Learning

Detecting geological structures on other planets can help in exploring fascinating features and understand the geological significance of the structures, factors such as age and the planet’s geological history. There does not exist one single algorithm that works for all the prominent structures. Hence, this paper aims to implement and test various deep learning algorithms based on CNN to accurately predict and classify the input images to respective planetary structures. The agenda is to formulate a standardized algorithm for classifying all the planetary structures.
Face Mask Detection Using Convolutional Neural Network

The creation of a real-time face mask detection system based on machine learning is the main goal of this research. With the global COVID-19 pandemic, face mask use has become essential for safety and adherence to regulations. The need to identify individuals not complying with the safety measures in public transit, retail settings, and healthcare facilities inspired this project. The goal is to develop a reliable face mask identification algorithm that performs well regardless of the user's mask type, facial angle, or lighting conditions. This research proposes the use of a Convolutional Neural Network based on deep learning, which is trained on a dataset of images of people wearing/not wearing face masks. The TensorFlow framework and Keras API are used to create the model. Transfer learning is also employed by adapting the MobileNetV2 architecture to improve the model's accuracy with less training data. The effectiveness of the proposed model is assessed on two datasets, one containing real-world photos and the other containing artificially generated images. The model performs well, achieving an accuracy rate of 97.5 per cent and 96.8 per cent, respectively, in identifying face masks in real-time. The proposed system has real-world applicability in settings such as hospitals, airports, and other public spaces, where adherence to safety measures is critical. The model can be further improved to detect other types of Personal Protective Equipment such as gloves and face shields. The project concludes with a CNN-based face mask detection algorithm capable of determining in real-time if a person has a mask on or not. The goal is to develop a reliable face mask identification algorithm that performs well regardless of the user's mask type, facial angle, or lighting conditions.
Conversion of Native Speech into Indian Sign Language to Facilitate Hearing Impairment

A large part of the globe adds up to people with specific needs. One of the most prominent aspects of today's era is communication. ISL (Indian Sign Language) provides a way to people to dumb and deaf people to communicate with each other across the Indian subcontinent and also allows them to talk to other people. Facial Expressions, Body postures, and Hand Gestures constitute the Indian Sign Language which then facilitates communication to convey the intended message and the underlying emotions. Indian Sign Language retains its lexicons and grammar rules that govern its behavior which makes it a fully-fledged natural language. This review aims to draft an application that translates the captured speech into a series of visuals of Indian sign language. The voice input is processed using the Speech Recognition technology to transform it into equivalent text, and then this text is further translated into its equivalent sign language. Word segmentation and root word extraction are achieved using the convenient Natural Language Processing algorithm. In order to drive this project further reachable to the people of India, the translation is done to Indian Sign Language.
Lung Carcinoma Diagnosis and Classification using Deep Learning

The proposed work implements the modified Inception Recurrent RCNN algorithm for Lung Carcinoma detection using segmentation and deep learning with a lesser number of epochs. Histopathological Images of the Lung Carcinoma dataset were used for training, testing, and validation. The model’s performance was tested under two conditions, first with a reduced number of input images and second with a reduced number of iterations to reduce the processing time for image diagnosis without compromising Accuracy and Precision. High performance is achieved with Inception Recurrent RCNN when applied to lung carcinoma classification against object classification and breast cancer classification.
Evaluation of Deep Temperatures in Oradea - 1 Mai - Felix Geothermal Area

Geochemistry is one of the most economical and efficient methods in studying a geothermal reservoir during exploration, exploitation and utilization. The Oradea - 1 Mai - Felix is the best promising geothermal area in north western part of Romania. In this study attempts were made to interpret chemical analyses of waters sampled from this area in view of geochemical interpretation knowledge and techniques. Chemical reactive species in the thermal fluids like sodium, potassium, calcium, magnesium, silica which vary with changing conditions take part in temperature dependent chemical interactions in geothermal systems. They were used for prediction of subsurface temperatures. Comparisons were made between the results obtained by various chemical geothermometers and temperatures estimated using silica-enthalpy mixing model.
Handwritten Equations Solver Using Convolution Neural Network

Mathematics is fundamental to everyone’s existence. We typically use calculators embedded into our current devices to solve mathematical equations. This project aims to develop a Handwritten Equation Solver that can solve mathematical equations written by a wide range of people. A neural network approach is used to recognize symbols and alphabets. Among other things, the system can recognize alphabets, all integers, mathematical symbols, and some special symbols. When one or more variables’ coefficients are multiplied by their sum of powers, the result is an algebraic expression known as a polynomial. If x is variable, ax+b=0 then it is linear polynomial equation. We show how computers can learn cognitive skills that allow them to understand an issue based on its visual context. The problem is simplified by using an image of handwritten polynomial equations as input and producing the answer as output. The proposed workflow method automatically simplifies the Handwritten polynomial equation. Horizontal compact projection analysis and combination linked component analysis approaches are used for segmentation. To classify individual characters, a Convolution Neural Network is utilized. For the solution of the problem, a character string operation is used for each proper detection. It’s also quite good at what it does. For the solution of the equation, each proper detection, character string operation is employed. Finally, the experimental findings reveal that our proposed approach is really effective.
Simulating Simple Tasks for a Kinematic Model of Differential-drive Mobile Robots

Controller design and verification by simulation is explored in this paper, for simple mobile robot tasks such as moving to a point, aligning with a line defined on the motion plane or driving to a predefined pose on the plane. The paper is set in the context of a study on vision-based control and is concerned with the step of using software to simulate the behaviour of a differential-drive mobile robot. Kinematic models are reviewed and presented, along with controller formulae for the enumerated tasks. Model implementations using Simulink are described and ran with predefined parameters, while the last part of the paper illustrates simulation results.
A Hybrid Deep Learning Model for Human Activity Recognition and Fall Detection for the Elderly

Modern society faces a significant challenge with the aging of the global population. The number of seniors is projected to surpass 1.4 billion by 2030, which will undoubtedly impact the sustainability of current healthcare systems, both in the public and the private sectors. To address some health issues that often face the elderly in terms of abnormal home behavior, or accidental falls, sensor-based systems for human activity recognition and automatic fall detection have become valuable tools which can be used for immediate notification to caregivers. These systems can monitor the health status of elderly individuals, promote healthy lifestyles, and provide timely medical intervention, leading to improved recovery and rehabilitation. In this paper, we propose a deep learning model that takes advantage of the affordability and latest technological advancements of mobile sensors to identify certain physical activities and promptly send an alert in the event of a fall. Our hybrid model combines the strength of Convolutional Neural Networks for feature extraction with the advantages of Long Short-Term Memory networks for time series forecasting and classification. Through experiments on two public datasets, we demonstrate the effectiveness of our approach, achieving superior performance in recognizing human activities and a high accuracy for fall detection, surpassing the performance of similar studies.
Morphed Face Image Detection Using PCA and Binary Classification

Every day, technology advances. The advancement of security technology raises a number of concerns. One of the major challenges of this method is face detection. Biometrics are used to detect faces by measuring the length of the eyes or the distance of features, but it can be difficult to determine if the face is original. As a result, many authors have recently proposed various techniques to address this issue. As a result, these models suffer a number of flaws, the most significant of which is the amount of time required. PCA (principal component analysis) and a binary classifier are used in the proposed model construction to speed up modeling and boost effectiveness.