Reviewerly/exHarmony huggingface dataset:
Research and Implementation of Intelligent Financial Audit System Based on Data Mining and Cloud Computing

research-article Research and Implementation of Intelligent Financial Audit System Based on Data Mining and Cloud Computing Authors: Junhong Gao Ordos Vocational College of Eco-Environment, China Ordos Vocational College of Eco-Environment, ChinaView Profile , Yuanxing Zhao Ordos Vocational College of Eco-Environment, China Ordos Vocational College of Eco-Environment, ChinaView Profile , Xiu Wang Ordos Vocational College of Eco-Environment, China Ordos Vocational College of Eco-Environment, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 714–718https://doi.org/10.1145/3544109.3544340Published:18 July 2022Publication History 0citation21DownloadsMetricsTotal Citations0Total Downloads21Last 12 Months21Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Identification model of personnel violations in material warehouse based on source information preprocessing

The existing illegal behavior recognition model has the problem of fuzzy value of human gait cycle, resulting in low recognition rate. A material warehouse personnel illegal behavior recognition model based on source information preprocessing is designed. Obtain the routine operation process of the personnel in the material warehouse, read and write the location label information, count the contour width of the continuous sequence, estimate the human gait cycle, divide the grid size by motion vector, extract the salient features of different video frames, define the time series in the video, and establish the violation identification mechanism by using the source information preprocessing technology. Experimental results: the average recognition rates of the material warehouse personnel violation recognition model designed in this paper and the other two recognition models are 61.777%, 47.950% and 45.588%, which proves that the recognition model integrating source information preprocessing technology is more suitable for material warehouse management.
Design and Analysis of Internet of Things Application Cryptosystem Based on Blockchain Technology

With IOT (Internet of Things) technology, related devices can not only share data and information safely and efficiently, but also realize more convenient dynamic monitoring and management. The application of technology in IOT not only improves the security of IOT data and information, but also effectively promotes data collection, information storage and equipment services in the current IOT system. This paper is based on the elliptic curve cryptosystem, which is applied to the research of IOT's network security transmission. Firstly, this paper analyzes the possible improvements of several elliptic curves applied to IOT, such as the creation of keys without specific numbers, characters and corresponding sources of account numbers; the scheme designed in this paper can realize the confidentiality, integrity, authentication and non-repudiation of data at the same time. At the same time, this scheme is designed according to the characteristics of IOT, so these two schemes are suitable for solving the security problem of communication messages in IOT.
Path Planning Algorithm of English Retrieval based on Associative Memory Neural Network

English collocation retrieval is to extract the phrases or idioms combined with various grammatical relations from the corpus, which is used to systematically analyze and study the collocations of words. In the absence of such corpus retrieval tools, English teachers mainly use the traditional method of definition and description to identify and analyze synonyms. In order to further enrich the English vocabulary of English learners, combined with the related algorithms of similarity, this paper proposes an English retrieval path planning algorithm based on associative memory neural network. The algorithm implements a search mechanism that uses the adjacency list data structure and restricts the search area to reasonably limit the search area of the algorithm. The BP algorithm based on the associative memory neural network model greatly reduces the network training times of the sample variable system. The system effectively reduces the network training time of the sample variable system, and provides the algorithm basis for the BP algorithm to be applied to occasions with high real-time requirements. Combined with the practical application of the path planning algorithm in the English retrieval system, through the hierarchical scheduling of tasks of different importance, a satisfactory decision result is obtained. The algorithm has the advantages of small search space and fast solution speed. Simulation results verify the effectiveness of the algorithm.
Data Mining Model of Internet of Things based on Blockchain Technology

research-article Data Mining Model of Internet of Things based on Blockchain Technology Authors: Hongping Cao Guangzhou Huashang College, China Guangzhou Huashang College, ChinaView Profile , Hongxing Cao Guangzhou Nanfang College, China Guangzhou Nanfang College, ChinaView Profile , Guiping Liang Guangzhou Huashang College, China Guangzhou Huashang College, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 1012–1015https://doi.org/10.1145/3544109.3544400Published:18 July 2022Publication History 0citation15DownloadsMetricsTotal Citations0Total Downloads15Last 12 Months15Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Construction of English-assisted Translation Learning System Based on Improved Attention Mechanism Model

research-article Construction of English-assisted Translation Learning System Based on Improved Attention Mechanism Model Author: Shuying Li Wuhan Donghu University, China Wuhan Donghu University, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 795–799https://doi.org/10.1145/3544109.3544356Published:18 July 2022Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Speed Invariant Hand Gesture Recognition System using CNN

Feature extraction process in dynamic hand gesture recognition systems sometimes become unsuccessful due to the complexity of the dynamic hand gestures. Speed variation is one of the challenges faced by the existing dynamic hand gesture recognition systems. In this paper, different deep learning networks have been used to recognize gestures which lead to avoid feature extraction process. Hence, complexity and computation time is also decreased for the recognition of the dynamic hand gestures. Comparison of various deep learning algorithms has been done for the dataset consisting of numerals from 0 to 9. Hand detection followed by the tracking of gestures is done by two-level KLT tracker and gesture trajectory is being used as input to the deep learning algorithms. It has been observed that 99.33% recognition accuracy is achieved using Resnet18 and InceptionV3 deep learning architectures. Also, least computational time is obtained using Vgg16 algorithm.
A Novel Rule-Based Approach for Mining Personal Attributes from Image Pool

Extraction of useful information from a huge volume of data is an active area of research. In this, we have considered a new problem in which we need to extract a set of predefined attribute values from an image pool, where the images in the image pool may belong to a particular person. The objective of this research is to process a set of images of a person to identify the values for various attributes such as age, gender, marital status, hobbies, physical attributes, etc. The major challenge in the proposed scheme is to identify the key face after face extraction from the image pool. The key face image will be the image that will repeat more times and there is a high probability that the image pool belongs to that person. We have used a set of state-of-the-art approaches to identify the attribute values such as age, gender, etc. from the key face image. The ground truth data are collected from them before the validation of the proposed scheme.
Active Contour with Adaptive Timestep for Brain Tumor Segmentation

Active contours with level set have been widely used across various image segmentation applications. Leakage of the active contour around weak edges is a major challenge, specially for medical image segmentation. One of the factors responsible for the leakage is a fixed time-step used in the discrete implementation of the active contour evolution. A smaller time-step results in a slow-steady evolution of the contour increasing the overall evolution time till its convergence. Whereas a larger time-step may result in skipping over weak edges resulting in erroneous segmentation results. Therefore an adaptive time-step scheme is proposed in this work. This scheme manages a larger time-step when contour is evolving in the homogeneous regions, while it updates to lower time-steps as the contour gets on reaching the boundary. It continuously adapts the time-step using a locally explored feature of the contour. This strategy is implemented on an edge-based active contour in this work. It is then evaluated on a MRI dataset having three kinds of tumors. The results show improvement in segmentation performance over the fixed time-step approach of the original edge-based active contour. Our results are also compared with a recent CNN-based segmentation on the same dataset. It is found that our method gives better tumor delineation results measured in terms of Dice Similarity Coefficient.
Student Teaching and Learning System for Academic Institutions

In today's global online environment, automation is essential for establishing a competitive advantage. Conversational Artificial Intelligence Systems are an example of an automation technology that has been embraced by some of world's most famous companies. In the field, higher education, these handy gadgets come in handy for administrators.Students' responses and performances are highly important and desired areas to improve the teaching-learning environment in the education organism. Evaluate the feedback to aid in the identification of flaws and actions. Institutes and universities in the field of education collect both quantitative and qualitative responses in order to improve the teaching-learning environment. However, most educational institutions and universities lack a sufficient student feedback evaluation system for determining students' feelings. The grading technique is currently being utilized to collect input. However, the grading process does not reveal the students' genuine feelings about the educational system, whereas written feedback allows students to describe specific areas and situations. Sentiment analysis is a type of qualitative feedback analysis that is based on records. The Nave Bayes (NB) classifier, Support Vector Machine (SVM), and Random Forest were employed in the majority of recent machine learning-based Sentiment Analysis prototypes. When it comes to measuring performance, most people utilize the kids' average grades, however this is not an accurate way. Because students can benefit from the experience indefinitely.This proposal offered a method for analyzing the sentiment of students' responses by combining Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence algorithms (AI). The goal is to combine machine learning algorithms and natural language processing approaches to achieve high accuracy. In this proposal, I suggested a collective model that connects machine learning algorithms to improve accuracy and performance. At the end of the presentation, AI planned to collect student responses in real time. The proposed method analyzes student comments from course reviews to determine mood, emotions, and satisfaction vs. displeasure. The approach categorizes sentimentalities into two categories: positive and negative, and senders' feelings into eight categories (8 emotion groups). The Fuzzy logic technique will be used to assess student performance. We intend to make our program available for broad use in the education industry so that organizations can improve the teaching-learning environment's quality.
Digitization of Chess Board and Prediction of Next Move

In recent years digital technologies and gadgets have grown so much. Along with other things, the field of Artificial Intelligence has also grown dramatically. Computer vision is one of the most compelling types of AI, which everyone must have experienced in one or the other way like in OCR, Image Recognition, object detection, google lens, etc. One might even come to the assumption that given any image or video to a computer, the computer can understand what’s going on in it and can comment a few things on it, just like WE HUMANS. But that assumption is not yet very true. It may be possible in the future but at the present these need some work to become reality. Leveraging this same motivation, we intend to give some contribution for our dream future to become reality. Recognizing and understanding any arbitrary chess board from the image, thereby digitizing it, is the thing that we will be achieving from the proposed system. Along with all that, giving a comment on the current state of chess board, thereby predicting the next optimal move in the game is another thing which is included in proposed system. Think of a human-like robot trying to play chess. Robots must have something like this in order to play the game. In the proposed research work digitization of Chess Board is done by server application from chess board image acquired by user. Current state of the board is generated and sent back as a response to client application. With the help of GUI, the chessboard is generated from the received Forsyth-Edwards Notation (FEN) on the client side. Depending upon blacks turn or whites turn next optimal move to be played is shown to the user. With our proposed system digital instance is generated with accuracy of over 90%. All blank cells of the board are generated with almost 100% accuracy.
Design and Analysis of Early Detection System of Breast Cancer Based on Mammogram Images using Neural Network

The uncontrolled and abnormal growth of the cells leads to the formation of the tumor in our human body. Two types of tumor are formed by uncontrolled cell growth. One is benign and malignant. The malignant is the cancerous cell which can spread to the other body parts very easily. When the cells grow uncontrollably in the breast region that can cause the tumor formation in the breast and finally may happens cancer. In today’s world the second most lethal cancer for human is breast cancer. So early detection and diagnosis is very important to reduce the mortality rate. This paper states that we have designed one new system for early detection of breast cancer from mammogram images based on neural network and we have compares the results with various existing methods. We have divide the work into two parts training and testing phase. In the training phase we have used adaptive median filter, Gaussian Mixture Model (GMM) segmentation and Gray-Level Co-Occurrence Matrix (GLCM) features extraction method. We have used the Mammographic Image Analysis Society (MIAS) dataset to train our system then we have taken some testing mammogram images from some local hospitals and use the same pre-processing method. After that we have used probabilistic neural network (PNN) classifier to classify the tumor whether it is benign or malignant and give the desired output with great accuracy.
RGB Camera Based Object Detection and Object Co-ordinate Extraction

Extracting the coordinates of objects has been done in the past using many methods with the most popular one being the use of Kinect Depth Sensors. However, these methods are expensive as the hardware costs are much higher than a normal RGB Camera. This paper presents a simple method to overcome the costs associated by eliminating the need for a depth sensor. The paper makes use of the YOLOv4 machine learning algorithm [6] for object detection and extraction of the co-ordinates using the Field of View of the camera using the Python package OpenCV.
eGaz: Enhanced Search Engine for Gazette Publications

A government gazette is a periodical distribution that has been approved to distribute public or legitimate takes note. Presently, governments gazettes are published in the official website with the facility of downloading the individual files in portable document format. Even though these gazettes are categorized into a few types, individual gazettes contain diverse amount of material from various sectors of the government making it stiffer to the reader to search for their preferred apprises without reading the complete file. To address this problem as a possible convenient solution, Enhanced Search Engine for Gazette Publications was developed. The system is able to scrape gazette files from the official website, which is followed by a progression of text extraction, summarizing and clustering the extracted data and hoard them in a distributed system. With the use of the Content Search Engine, user could loop through any material issued over gazettes which would bring up filtered results from the extracted data. Similarly, users can read summarized reports of advertisements on job vacancies, examination details and results of examinations and could even filter them by relevant departments.
Interactive Language Learning with VR and NLP Assistance

In the past years, society has evolved exponentially in terms of technology and so has people’s need for growth and learning. Education has encountered a structural redefinition. As companies and governments open up to a hybrid economic and work model, it can be assumed that teaching/learning increasingly rely on a combination of personal and digital learning spaces. Due to the importance of languages from an individual perspective as well as from business and cultural perspectives, it is imperative that language learning also be incorporated into these virtual spaces.(p)(p)Research has proven that immersion is the best method of learning a language. This paper explores the use of virtual reality to provide a more engaging and rewarding experience. VR boasts a number of features that are useful for education: it displays environments in 3D, offers interactivity and even presents the user with audio, visual and haptic feedback. As opposed to videos and other passive learning methods, VR allows for direct interaction with the environment.This learning system can further be complemented with the help of natural language processing (NLP) to broaden the scope of interactions and improve engagement. And these topics have been explored in detail in this review paper.
A Brief Review on Various Aspects of Steganography Followed by Cryptographic Analysis

Owing to cyberattacks risks as well as other accidental alterations while active communication across an unsecured network, establishing a secure connection betwixt two communicative parties is becoming a complex job. Confidential as well as hidden information, nevertheless, could be protected utilizing cryptography or steganography. It is a skill of concealing a message (with no traceability) in such a way that it has no significance for anybody other than the intended recipient, whereas cryptography is the competence of turning plaintext (message) into an unreadable format. Each encryption approach could be deciphered in a reasonable amount of time plus with adequate resources. With the advancement of computer technology, the likelihood of decryption has expanded, since accessible computer speeds permit a decryption procedure grounded on an exhaustive data search. As a result, steganography, a science that aims to conceal the availability of secret information, has developed. This article provides an outline of image steganography as well as approaches utilized to conceal data in the source image to create stego-images. Moreover, a brief introduction of cryptography is also mentioned for differentiating the difference between steganography and cryptography. The paper covers all the important aspects related to steganography for a deep analysis of the security behind this idea.
A Gamified Virtual Learning Environment to Enhance Online Teaching and Learning Experience

In-class teaching not only concentrates on lecture content delivery but also on maintaining strong mutuality between lecturer-students and student-student. Online lectures are gaining popularity due to the Covid-19 pandemic. However, the learning-teaching process has become ineffective because existing video conferencing solutions are not intended for academic purposes. This research was conducted to identify the pain points of online education and develop an enhanced software solution. A user survey confirmed that an isolated environment tends to diminish attentiveness during online lectures. Also, it is difficult for teachers to observe the attentiveness of all the students. As a solution, student attentiveness was measured using their facial expressions and collected data shown to lecturers through a virtual student behavioural environment. The physical separation causes students to feel isolated during lectures, which can negatively affect their academic development and social and psychological development. The developed application also provides a virtual group study environment as a feature. According to the results gathered in the user acceptance testing phase, it was found that the attention detection feature helps students keep their attention at a significant level. Further, 9 out of 10 teachers who participated in the testing acknowledged that the simulated student behavioural view could provide a more immersive experience. 71% of students preferred the new collaborative virtual environment, and students further elaborated that the virtual environment was more likely the physical group studies. 72% of students mentioned that the collaborative group study tool helped eliminate isolation during group studies.