Reviewerly/exHarmony huggingface dataset:
AR-Based Surgical Navigation System Based on Dual Plane Calibration and 2D/3D Registration

Surgical procedures are often associated with a range of potential risks, including nerve tissue damage resulting from complex lesion structures, narrow surgical spaces, or the low accuracy of the operating surgeon. The rise of augmented reality technology has attracted wide attention in the field of complementary medicine. The safe and low delay augmented-reality assisted surgery system can effectively guide the accurate operation. At the same time, augmented reality technology can make surgical sites more intuitive. In this work, we propose a novel surgical operating system that leverages coordinate system transformation and optical navigation to improve precision and safety. Our approach involves the integration of medical images and optical tracking devices before and during surgery to achieve coordinate transformation between the patient’s position, surgical instruments, and the pre-surgery CT scan. This process involves calibration, registration, and other critical procedures. At the same time, the study also introduced the function of augmented reality. Doctors wearing augmented reality glasses can use the system to see more vivid scenes of surgery. During the calibration phase, we utilize biplanar and interpolation algorithms to obtain the conversion relationship between the C-arm emission source and the X-ray image coordinate system. In the registration stage, we convert the preoperative 3D CT into a DRR image using digital reconstruction image technology, which we then register with the 2D X-ray during the operation. To optimize the iteration, we employ the improved Powell algorithm. The algorithm demonstrates an average angle and position error of 0.62° and 0.56mm, respectively. Through the transformation of spatial position relationship, the transformation relationship between preoperative CT and intraoperative patient position was obtained. A user-friendly, integrated software system has been developed that allows users to view the relative relationship between surgical instruments and patient position in CT in real time during surgery. An augmented reality module has also been introduced, allowing operators to see specific surgical models in specific glasses. In this study, the establishment and model test of the algorithm were realized, thus providing auxiliary functions for high precision surgical operation.
Estimation of Wiener Model Based on Neural Fuzzy Network

This paper proposes a two-stage parameter estimation approach of Wiener model based on correlation analysis method and Taylor series expansion theory. The developed Wiener model is characterized through the dynamic block modeled by a rational transfer function, followed by a nonlinear block based on neural fuzzy network. The input test signal consisting of Gaussian signal and random signal is applied to the parameter separation and estimation of Wiener model. Firstly, based on the input-output data of Gaussian signal measured, correlation analysis is used to obtain the parameters of the linear block. Then, using Taylor series expansion theory and clustering algorithm, the nonlinear block parameters are estimated based on random signals. Through theoretical derivation and experimental results, it can be seen that this method can usefully estimate the Wiener model with output noise and obtain favorable estimation accuracy.
Target Tracking and Path Planning of Mobile Sensor Based on Deep Reinforcement Learning

Path planning is a classical problem of artificial intelligence, with a wide range of applications in defense and military, road traffic, and robotics simulation. However, most of the existing path planning algorithms have the problems of a single environment, discrete action space, and manual modeling. As a machine learning method that does not require artificially providing training data to interact with the environment, the deep reinforcement learning obtained by reinforcement learning has further enhanced the ability to solve practical problems. This paper proposes to use the DDPG (Deep Deterministic Policy Gradient) algorithm on the mobile sensor to achieve path planning on the target. The DDPG algorithm combines strategies such as DQN, ActorCritic, and PolicyGrient, which introduce deep reinforcement learning to continuous action space and further enable decision-making judgments in complex continuous environments.
Survey on Automatic Text Summarization using NLP and Deep Learning

Automatic text generation is used extensively for various purposes such as creating content for websites, generating reports, articles etc. Automatic Text summarization uses several strategies to summarize the text and can save time and effort. Significant works are done regarding the text summarization process, but achieving automatic accurate text summarization without losing the meaning and context of the words is a challenge. Abstractive and Extractive methods are used in Text summarization and various researchers have investigated and contributed to the text summarization process in the form algorithms. In this paper we intend to present the text summarization methods and procedures suggested in various research papers along with their pros, and future scope. The study of various text summarization methods helps the researchers in understanding the considerations, applicability, and accuracy of these methods and suggest the need for an improved text summarization model.
Interpreting Doctor notes using Handwriting Recognition and Deep Learning Techniques: A Survey

Handwritten recognition is becoming one of the most researched areas in the field of computer science. As the technologies are growing, everyone wants advanced life, which makes life easier. Even in the recognition of handwriting, mainly doctors notes, they are very difficult for everyone to understand and it takes time for a person to analyse it. So, this paper mainly focused on interpreting doctor’s notes using handwritten recognition and deep learning techniques. The handwritten or printed document pictures are transformed into their electronic counterparts using an optical character recognition (OCR) system. Due to individuals' inconsistent writing styles, dealing with handwritten texts is significantly more difficult than dealing with printed ones. Handwritten text recognition could be done by Image processing, Machine Learning or Deep Learning Techniques. Out of these Deep Learning remains to be the most popular and prominent. Some of the Deep Learning techniques includes Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). This paper gives a review of the various handwritten recognition methodologies used for interpreting handwritten texts. This paper includes the most important algorithms that could be used for detecting the handwritten word/text/character by using various approaches for the recognition process. In the end we are thus comparing the accuracies provided by these systems.
YOLO-Log: A Light-weight Object Detector for Logistics Safe Driving

Object detection algorithms are attracting more and more attention in the application of intelligent logistics, but current object detection algorithms have problems such as high computational cost, low detection accuracy, and difficulty in deploying edge devices with limited computing resources in complicated logistics scenarios. This paper proposes a logistics object detection network (YOLO-Log) with a light-weight residual structure GhostNet. YOLO-Log is based on YOLOv5, which incorporates a light-weighted residual structure in Backbone and substantially reduces the model parameters of Backbone. Then, a simple and effective weighted bi -directional feature pyramid network is invoked to enhance the network to extract feature information of the target from the complicated logistics background. Finally, in light of the current lack of publicly available logistics object detection datasets, we collected and produced the logistics object detection dataset Logistics-3k. YOLO-Log achieves 91.4% mAP on the Logistics-3k dataset, which reduces the model parameters by 14.2% compared to YOLOv5s, and the detection time is only 10.2ms, addressing the industrial requirements of logistics scenarios.
Multimedia generation using neural network DeepDream

The field of machine learning and artificial intelligence is growing rapidly. The deep neural network is a field of ML which is showing greater possibility. The deep neural network has been widely acknowledged as being in a golden age and advancing with the advent of new technologies. It is slowly becoming the leader of the technological world in artificial intelligence. The models which are built by machine learning algorithms with great accuracy help in every sector of human evolution. Image and audio processing is done through many algorithms to intensify its behavioral pattern. The development of modern art is largely dependent on the painters who are developing the style. The Deep Dream algorithm can also do the artistic creation of the image and audio. The deep dream algorithm which googles engineer Alexander Mordvintsevin built in the year 2015. It enhances the patterns in the multimedia through algorithmic pareidolia. It creates a dream-like effect. It gives a new vision of hallucinations to images. This could be used in the health sector for the purpose of detecting diseases and defects through scans of the patients. It is redefining lower-definition multimedia to higher-definition multimedia. Previous model were able to only generate one of the multimedia format. Proposed system will be able to create multimedia based on DeepDream and link different formats together so that they appear to be generated together.
EvilSpot: Detection and Mitigation in Multi Channel

Amateurs hack systems; professionals make evil twins with a mere connection. Free Wi-Fi everywhere, but we need more awareness among us. An evil twin can mimic the IP address and the network name of a legitimate access point. The evil twin can obstruct the accessibility of the legitimate access point, leading to a man-in-the-middle attack. Thus, preventing such spoofing attacks is needed to strengthen security and prevent data breaches. In the past few decades, some efforts have taken place to detect and stop evil twin attacks, but they are less vast than multi-channel. This paper will detail the preventive algorithm to counterattack the evil twin attack. The suggested algorithm work on the multi-channel, information about IP prefix distribution, and whitelisting of legitimate points are implemented in evil twin detection. Authorization is performed at every channel to detect any unauthorized BSSID or Deauthorization frame in the network. The proposed algorithm successfully substantiate the detection of the evil twin.
Under Water Image Enhancement Using CNN

Underwater image enhancement is a crucial task in underwater photography and videography. In recent years, deep learning techniques, specifically Convolutional Neural Networks (CNNs), have been successfully applied to this task. CNNs can be trained to improve the visual quality of underwater images by removing various distortions such as colour loss, blurriness, and noise. These networks can also help in enhancing features such as contrast, brightness, and saturation, making underwater images clearer and more visually appealing. There are various approaches to underwater image enhancement using CNNs, such as multi-scale CNNs, unsupervised CNNs, and generative adversarial networks (GANs). These techniques aim to restore the original appearance of underwater images by removing the distortions caused by light scattering, absorption, and refraction in water. CNNs are trained on underwater image datasets and are used to enhance the underwater images in a supervised manner, producing enhanced images with improved colour correction and reduced backscatter. This results in clearer and more visually appealing images, which can be used in various applications, such as underwater surveying and exploration.
Automated Bengali abusive text classification: Using Deep Learning Techniques

In recent years, the breakdown of most human causes that hampers a large number of people is verbal abuse. The most noticeable fact is that most of the verbal abuse is made online by people behind the screen. While this problem has occurred with growing modern technologies, a modern solution is needed to solve the drawback. The growing rate of abusive comments and hate speech online is rapidly increasing on a large scale, and manual reports and corrections cannot help this critical situation. This proposed model introduces automation of the hate speech filtering process through deep learning. This work uses only Bengali datasets to find the real classification, as many Bengali and English mixed research works are available in this field. Using the intelligent automated classification of text comments in a limited resource-constrained language (example: Bengali) is critical for several reasons. This proposed system classifies 'Personal Offensive Text', 'Geographical Offensive Text', 'Religious Offensive text', 'Crime Offensive Text', 'Entertainment, Sports, Meme Tiktok, and others'. By combining two large datasets for this research and employing Bengali BERT, the best feasible outcome has been achieved.This paper indicates the complete results of the combined two different datasets and classified into nine segments, while the proposed Bengali BERT models achieved the highest accuracy of 0.706 and a weighted f1 score of 0.705 in the identification and classification tasks.
Fraudulent URL and Credit Card Transaction Detection System Using Machine Learning

Cybersecurity encompasses operation technology, information which are closely connected practices, technologies and concepts. The use of this information offensively to attack others is the basic feature of cybersecurity. We will focus on malicious URLs and fraudulent credit card transactions as the two key cybersecurity flaws. As we all know URLs have become an important part of everyone’s life. It is important to detect which URLs are malicious and which one is safe. Malicious URLs can potentially cause a huge economic loss to any country, organization or a human being. In this modern world monetary transactions are made online with the help of credit cards, despite its easy usability, security loopholes and user’s carelessness lead to fraudulent transactions as hackers get access to the credit card’s sensitive data, Causing an annual loss of nearly 5 billion dollars. Security tools are always in development to solve and detect malicious activity and security loopholes. In this paper, Machine Learning algorithms are used to create API’s that detect security loopholes and our API tends to detect malicious URLs and fraudulent credit card transactions and host it on the web so that users don’t have to download the software files locally. This paper is in correspondence to all results obtained from previous models, that gave a general accuracy of 70% to 90%
Pseudo Coloring Using Deep Learning Approach

The automatic image colorization technique has garnered a lot of attention over the past ten years for a variety of applications, including the restoration of old or damaged photos. Owing to many level of independence in the distribution of color information, this issue is presented in a very terrible way. Recent advances in automatic colorization either use photographs with a recurring theme or requires as an input, highly processed data such as semantic maps. An effort has been made to address this issue in the approach, by using Python and concepts of deep learning and libraries such as NumPy and OpenCV. Also, use of Convolutional Neural Network to train greyscale images. The use of Convolutional Neural Network (CNN) was made to train black-and-white images. A CNN is made up of many layers of tiny computational units that solely use feed-forward processing to process selected areas of the input image.
A Survey on Real-Time Object Detection Algorithms

One of the major technological advances that aims to more accurately perceive the real world through digital images and videos is computer vision. Amongst the many innovative breakthroughs in this field of study is object detection, a branch of computer vision. This methodology finds instances of specific objects in visual content. The terms object recognition, object identification, and image detection are all mutually inclusive for object detection. By identifying objects in visual images or videos, it enables information systems to "view" their surroundings. This paper compares and contrasts the most extensively used methodologies and approaches to achieving object detection in real time. Faster R-CNN, SSD, and YOLO are three algorithms used in this cultural shift that simplify the process for programmers to identify an instance in an image. The processes that enable the model's parameters to be trained, calculated, approximated, and quantified are compared with the conventional methodologies. The convenience and limitations of each of these techniques are further discussed, analyzed, and tabulated.
A Survey Paper on Classroom Monitoring System

Smart cities have many applications for intelligent learning environments, and monitoring student attendance in class is a crucial step in encouraging punctual attendance and ensuring the effectiveness of instruction. In this study, AMMoC, a method for managing student attendance (Attendance Management Method based on Crowdsensing) is developed to address issues with checks on student attendance, such as ineffectiveness and straightforward cheating. Initialization and authentication are the two parts of the AMMoC process. During the startup process, a teacher requests that the server check attendance. After receiving the request, the server makes a request asking students to provide their whereabouts. Once all student responses have been received, the server creates the student location map. Task assignment and attendance verification are the two components that make up the authentication step. The job assignment module of AMMoC employs the Monte Carlo technique to determine the ideal placement of subregions and verifiers before requiring the verifiers to ascertain the subregion’s student population. The attendance verification module will afterwards validate the outcomes of the data. Studies comparing and analysing experiments reveal that AMMoC offers the benefits of strong anti-cheating performance and quick response times.
Synthesis of Reversible Logic Circuits-A Technical Review

Traditional technologies such as CMOS show their limitations with an expanded growth of packing density. The usage of recent modern technologies is needed to address the traditional challenges. In modern technologies, managing the loss of energy is also a primary issue in the digital logic design for both manufacturer and as well as for the customer. Thus, Reversible logic is considered to be one of the efficient solutions to overcome such problems. The applications of reversible logic are in a wide range of domains - Digital Signal Processing, Optical Computing, Cryptography, and CMOS design with low power consumption. Thus, this paper discusses optimal-cost synthesis problems related to reversible logic circuits. The synthesis problem aims to obtain a network of gates with minimum cost that realize a given function by assuming the same cost for each gate, and each gate’s cost reflects the actual cost for its implementation. The discussion of various synthesis algorithms and a comparative study of them are presented by giving their essential features and limitations.
Detection of Violent Content in Videos using Audio Visual Features

Content moderation is an essential part of the internet, which makes the internet a safe place for people of all age groups. Content moderation is usually done manually by humans, who flag the content as violent or non-violent. The content moderators are exposed to various violent content, which affects them mentally and emotionally. In this research, we introduce a machine learning model that can identify and categorizing videos as either violent or non-violent based on the presence of violent content. Two parameters are taken as inputs namely audio and video. The audio is separated from the video initially. The audio component is processed through an audio classifier which distinguishes between violent and nonviolent sounds. If the audio classifier classifies audio as nonviolent, then the corresponding video is fed into a video classifier where it is categorized into violent or non-violent video.
Large Vocabulary Continuous Speech Recognition System for Marathi

Lots of efforts have been made in the development of Automatic Speech Recognition systems for Indian languages, and good progress has been made for Hindi, Tamil, Telugu & Bengali, and Indian English. However, practically usable robust systems are still not available for many regional Indian languages including Marathi. This paper presents an effort toward the development of a large vocabulary continuous speech recognition system for Marathi and its integration into practical application, such as Speech-based typing system. This speech recognition system has been developed using the Kaldi toolkit. The Kaldi toolkit facilitates the exploration of different acoustic models such as Gaussian Mixture Model with Hidden Markov Model (GMM-HMM) and Deep Neural Networks with Hidden Markov Model (DNN-HMM). In this paper we have developed Marathi Speech Recognition system which is trained around 95 hours of audio data. We have used the trigram language model which is having 29 lakhs of words and 3 lakhs of vocabulary size. This system gives average word error rate of 19%.
N-ary relation extraction using web-scraped data

Information Extraction refers to the process of parsing through unstructured data and extracting meaningful information into editable and structured data formats. There are two common sub-tasks of information extraction, which are entity extraction and relation extraction. The purpose of relation extraction is to find semantic links between entities which can either be binary or n-ary, where n is greater than two. The objective of this paper is to identify n-ary relations among arguments by first scraping news articles from Wikipedia and using a Bi-LSTM and Multi-Task Learning (MTL) framework which greatly enhances the accuracy of extraction.
Bangla and Nagri Language Detection and Classification Using Deep Learning Methods on Text Document Image

The development of an automated document processing system and a multilingual OCR relies heavily on accurate language recognition and classification. In the prior many researchers did a lot of research on several renowned languages. As a result of their rarity, complexity, and difficulty of use, Bangla subcontinent languages, such as Sylhoti Nagri, Chatgaya, etc., have received comparatively less attention from researchers than more widely used languages. In this research, we utilized deep learning models in an effort to categorize and detect Bangla and Sylhoti Nagri languages. Here, YOLOv5 and YOLOV7 were used to recognize Bangla and Nagri text from scanned document images, while VGG16 and ResNET50 were used to classify the text. VGG16 outperformed ResNET50 in classification with an accuracy of 97.23% and 95.89% in train and test respectively, while the YOLOv5 model outperformed the YOLOv7 model in terms of detection accuracy (99.41% vs. 94.12%).