Reviewerly/exHarmony huggingface dataset:
Multisalient Object Detection and Recognition using Deep Learning Techniques

This paper describes the idea of how multisalient object detection and recognition are done using deep learning technologies. The important problem that is faced in Computer Vision and Image processing is detecting objects’ position. Salient object detection has complexity due to diverse effects such as light and weather conditions. For that, a new method is proposed for object detection and recognition based on deep learning techniques. The proposed methodology explores the method to detect a salient object in a frame using deep learning methods. For the purpose of retrieving the desired region, Multiobject detection algorithms use a variety of image processing applications. Applications including image retrieval, security, the medical field, and the military frequently employ it. Consider employing deep learning methods to recognize and classify several objects in this project. Classification Object Using DNN (Deep Neural Networks), multiobject identification entails training a neural network model to recognize and find numerous objects in an image. The duties of object classification and object localization are carried out during object detection. The classification and detection of objects have been effectively accomplished using deep neural networks (DNN). MobileNets are also used in this system which is helpful in the detection of objects in the fastest way. Hence by using this system, object detection is done in a more efficient way.
A Combined Approach for Efficient Compression and Restoration of Multispectral Satellite Images

Our everyday lives are increasingly influenced by satellite images. They are an integral part of the daily news such as weather forecasting, waterbody survey, land surveying etc. Multispectral images have multiple bands, and the information will be present in at least one band. As the satellite images are very large in size, FFT2 compression technique has been proposed in this paper. This method is a lossy compression technique and granular noises get introduced in the compressed image. For restoring the images from the granular noises which are caused from sensor noises and lossy compression, an autoencoder neural network has been implemented. The neural network is trained and evaluated. The experimental results have shown that the proposed method for compression has resulted in a Compression Ratio of 11:1 and an autoencoder neural network model removes the granular noise with an efficiency of 83%
UNI-KYC System on Blockchain

The financial institution’s Know Your Customer (KYC) procedure is its cornerstone. With the aid of this technique, the banking industries and other service providers may confirm client identification information between trustworthy parties. A system is developed which provides a unique NFT token as the proof of ownership to the customers who have completed their one-time KYC procedure with any of the institute within the network. The method also allows the exchange of KYC related data stored on IPFS. Financial institutes can authenticate any individual by either just verifying the NFT or by the data stored under that particular token via a dedicated gateway of Infura, which enable transparency and immutability. The decentralized KYC process simplifies the acquisition of secure and rapid access to current user data.
Localization Recommendation Algorithm of Online Translation Course Based on Deep Neural Network

With the rapid development of Internet technology, more and more people begin to use the Internet for learning. However, the diversity and complexity of online course resources make it difficult for people to find high-quality educational resources that meet their own needs. Online translation courses, with the advantages of large capacity of knowledge and information, rich content, strong interactivity, and high degree of personalization, are an important and indispensable educational resource in the field of online learning. After studying a large number of domestic and foreign literatures, it is found that on the basis of studying the existing online translation courses, this paper uses deep neural network to conduct localization recommendation research on them. The research results show that under the same conditions, the number of qualified and unqualified students before optimization is 25, but the number of students with 60-69 scores accounts for the most, and there are no students with 90 scores and above. After optimization, the number of qualified students has become 47, and the number of students with 70-79 scores accounts for the most, and the number of students with 90 scores and above has also increased significantly. The number of unqualified students is 3, accounting for 6%, It shows that the deep neural network is conducive to the development of online translation course localization recommendation algorithm, and points out a new direction for the optimization of online translation course localization.
Intelligent Control of Automated Microelectronic Production Lines Based on Artificial Intelligence

Artificial intelligence is currently the most popular technology. With the advent of the era of big data, information technology based on artificial intelligence has become a new type of technology capable of most of the human labor force. Its scope of application is mainly in the comprehensive processing of improving efficiency, and those companies that need to improve efficiency do need corresponding technical support. Through artificial intelligence technology, the production line can analyze and predict the production process, so as to optimize and upgrade the production process. At the same time, intelligent production lines can also realize real-time monitoring and analysis of production data through big data analysis technology, thereby improving the reliability and stability of the production process. Automated control and optimization adjustments can also be realized in the production process. Through the intelligent control system, the production line can automatically complete the processing, assembly, testing and other work of the product, and the production process and process parameters can be automatically adjusted according to the production situation, so as to realize the autonomy and intelligence of the production process. This article first introduces the basic framework of artificial intelligence in detail, then applies artificial intelligence to the research of intelligent control of automated microelectronics production lines, conducts data tests on the overall efficiency, and finally uses questionnaires on the production personnel of the waterline and the overall efficiency of the company. It can be shown that artificial intelligence is of great help to improve the efficiency of production lines. Therefore, making good use of artificial intelligence is a very worthy topic for the research of intelligent control of automated microelectronics production lines.
NFT based Green Energy and Assets Trading Application over Decentralized Marketplace

Digital decentralized marketplace is now becoming more popular since 2015 due to invention of NFT technology. NFT is most successfully implemented in digital contents like art and music and is evolving as a new market for the future. NFT and its underlying technology blockchain is now being diversified into various application areas to solve various requirement of human and its society. In the current global scenario green house effect and its solution though alternating energy sources like renewable energy generation is major challenge due to distributed nature of these sources. Moreover, establishment of such infrastructure need much investments. Distributed management using blockchain technology is the only solution to manage energy generation and exchange between prosumer and consumer. However, to achieve net zero emission (NZE) we need much more investment in the clean energy sector and is not possible unless more and more people join and participate in the investment. This study proposes a new solution for secure and intelligent trading of solar charging stations and electric vehicle equipment in a decentralized digital marketplace, utilizing NFT technology. The approach is designed to improve the reliability and transparency of transactions, providing a more secure and efficient system for trading these valuable assets.
Implementation of a blockchain based DApp for P2P electricity trading

This article details the implementation of a decentralized application (DApp) based on public blockchain Ethereum for token backed P2P electricity trading, with electric vehicle as one the peers. A hybrid market model with a peer matching mechanism with score-based peer matching orchestrated by smart contracts is used and results are presented. Solidity language is used to create smart contracts which runs on the top of open source Ethereum public blockchain. Lockable tokens are created by modifying ERC20 APIs with wallets deployed using MetaMask. Client-side programming with Node-Express with a user interface is deployed. Truffle suite-based unit testing of whole DApp is carried out and deployed to block chain. The cost analysis for deployment of DApp both the simulation environment Ganache and test network Goerli is also presented with cost allocation to different market players.
Smart Door Keeper

With the advancements in computer vision, computers are now able to recognize and process various objects present in images. This technology has the potential to make life more convenient and improve society. Facial recognition is a task that computers perform better than humans, especially when searching through large databases of faces. In this paper, we present a system that uses computer vision to enhance home security. When someone presses the doorbell, the system detects the person in front of the door. If the person is recognized by the system, then the door can be unlocked. However, if the person is not known, the system will search for logos to identify the delivery company the person represents. If there are no logos detected, then the person is marked as an unknown guest, and the homeowner is notified. Our system includes a front-end, which allows the homeowner to add new people to the database, so on their next visit, the system can recognize them and send a notification accordingly. Additionally, we implemented algorithms for face recognition based on Histogram of Oriented Gradients (HOG) and object detection for identifying the lo-gos of different companies using Oriented FAST and Rotated BRIEF (ORB). This paper presents our system design, software algorithms, implementation, and results, along with a review of relevant work in the field of home automation systems. The traditional system only achieves alerting the home by the sound of a doorbell but nothing regarding identification, record keeping, and surveillance. The agenda is to create a smart doorbell system that incorporates the aspects of record keeping, home automation, and security.
Blockchain Based Banking Transactions Enabled by Big Data

The blockchain system has emerged as an innovative approach in a variety of areas, including. finance, supply chain, healthcare, education, and energy consumption. Although its extensive application, yet, there is a scarcity of in-depth studies to provide a thorough knowledge of blockchain technological advancement in various domains. As a result, the purpose of this comprehensive survey of literature is to examine the content analysis of 41 pertinent research articles and company surveys released from 2008 and 2023 were taken in order to evaluate the current state of blockchain technology's applications in the banking and financial services industry. The analysis examines the blockchain system's aspects, applications in financing, competitive edge, present state, and problems. Because of various limitations, the investigation suggests that the adoption of a blockchain-based financial management systems continues to be in its early stages. In summary, this study sheds light on the possible features of blockchain research in the financial sector and emphasizes the need for. further research to explore the potential of blockchain in the finance industry. The investigation is able to identify three key domains that need more investigation in order to establish blockchain systems as a disruptive system which will revolutionize the banking and finance industries. In conclusion, this investigation throws a spotlight on the prospective aspects of blockchain technology investigation in the banking and financial services industry.
Decentralized Fingerprinting for Secure Peer-To-Peer Data Exchange of Aadhaar Via Public Key Infrastructure

The evolution of Aadhaar, an identification system executed by the Indian government that provides each citizen with a unique identity, is analyzed within the context of the creation of Unique Identity Authority of India (UIDAI), which distributes and generates user identities based on and biometric and demographic information. The study gives a comprehensive outline of security issues related to the Aadhaar authentication process and analyzes the updates introduced over time. The main objective of this study is to comprehensively cover the security perspectives related to Aadhaar and provide conceivable arrangements to mitigate the security threats through the development and implementation of a decentralized fingerprinting system that uses Public Key Infrastructure (PKI) to empower secure peer-to-peer data exchange leveraging zero-knowledge proofs. The proposed solution shows the necessary data digitally by integrating data logging strategies to keep track of individuals accessing the data ensuring end-to-end encryption. In this way, it empowers Aadhaar holders to selectively disclose their information to authorized entities while preserving the confidentiality and integrity of the data.
Enhancing the Trust and Transparency of Academic Records with Blockchain-Based Systems

Academic record-keeping and verification systems face several challenges related to data security, privacy, and fraud prevention. Blockchain technology has been proposed as a potential solution to these challenges, as it offers a more secure and transparent system that can enhance the credibility and integrity of academic records. However, the implementation of self-sovereign identity (SSI) principles can further enhance the user-centric and decentralized nature of the platform. This paper proposes a blockchain-based education portal that leverages the power of blockchain technology and SSI to provide a more secure, efficient, and user-centric system for academic record-keeping and verification. The proposed system aims to comply with SSI principles, ensuring the privacy and security of student data while enabling students to control and share their academic records. By providing a decentralized, tamper-proof system for academic record-keeping and verification, the proposed system can enhance the trust and transparency of academic records, thereby enabling faster and more efficient verification processes. This investigation can provide insights into the potential benefits of SSI compliance in education record-keeping and verification and help to advance the adoption of SSI principles in other domains.
Offensive Hinglish Text Classification Using Longformer, Bigbird &amp; HingMBert

With a sharp rise in the number of users and skilled fluency in the use of “Hinglish”, a cross-lingual text of Hindi and English, in a linguistically diverse country, India, it has increasingly become important to analyze social content written in the aforementioned language over platforms such as Twitter, Reddit, Facebook. This project focuses on the classification of Hinglish text into offensive and non-offensive categories. We present a state-of-the-art novel use of “Longformer” and “Bigbird” transformer-based language modeling models on Hinglish texts for natural language processing (NLP) sentiment classification task, alongside mBERT, pre-trained for Hinglish, hence “HingMBert” model. Our dataset is a combination of pre-existing datasets and further addition of approximately 8,200 records, a total of 24,660 records. The tweepy library has been used to scrape novel tweets. Experimental results for Longformer, Bigbird, and HingMBert have achieved F1 scores of 0.76, 0.77, and 0.79, and accuracy of 76%, 77%, and 80% respectively. F1 score of 0.78 and an accuracy of 78% was achieved using the majority voting ensemble technique.
Deep Learning Model for Identification and Classification of Web based Toxic Comments

Everyday, many individuals face online trolling and receive hate on different social media platforms like Twitter, Instagram to name a few. Often these comments involving racial abuse, hate based on religion, caste are made by anonymous people over the internet, and it is quite a task to keep these comments under control. So, the objective was to develop a Machine Learning Model to help identify these comments. A Deep Learning Model (a sequential model) was made and it was trained to identify and classify a comment based on whether it is an apt comment or not. LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) that is particularly well-suited for modeling sequential data, such as text. LSTMs are capable of modeling long-term dependencies in sequential data. In the case of text classification, this means that LSTMs can take into account the context of a word or phrase within a sentence, paragraph, or even an entire document. LSTMs can learn to selectively forget or remember information from the past, which is useful for filtering out noise or irrelevant information in text. LSTMs are well-established in the field of natural language processing (NLP) and have been shown to be effective for various NLP tasks, including sentiment analysis and text classification. Binary cross-entropy is a commonly used loss function in deep learning models for binary classification problems, such as predicting whether a comment is toxic or not. Binary cross-entropy is designed to optimize the model's predictions based on the binary nature of the classification task. It penalizes the model for assigning a low probability to the correct class and rewards it for assigning a high probability to the correct class. The loss function is differentiable, which allows gradient-based optimization methods to be used during training to minimize the loss and improve the model's performance. Binary cross-entropy is a well-established loss function that has been extensively used in the field of deep learning, and there are many tools and frameworks that support it, making it easy to implement in practice. Binary cross-entropy also has a probabilistic interpretation, which can be useful in some applications. For example, it can be used to estimate the probability that a given comment is toxic. Hence, Binary Cross Entropy has been chosen as the loss function for the Deep Learning model.
A novel Kaniadakis entropy-based multilevel thresholding using energy curve and Black Widow optimization algorithm with Gaussian mutation

In this research, Kaniadakis entropy (KE) derived from the energy curve is adopted to construct an objective function for the thresholding of images at various levels. In addition to the histogram's property, the energy curve maintains the image's spatial contextual information. This additional data aids in the threshold selection process, resulting in a more accurate segmented image. To optimize the objective function, a new Black widow optimization with a gaussian mutation algorithm (BWOG) is also proposed in this paper with enhanced population diversity by incorporating an additional stage of powerful Gaussian mutation and random allocation of solutions using a levy flight mechanism in BWO. The proposed Kaniadakis entropy-based multilevel thresholding selection using energy curve and Black Widow optimization algorithm with Gaussian mutation (BWOG-KE) is performed on both grayscale and color images of different modalities and dimensions. Based on the quantitative measures: PSNR, the BWOG-KE is found superior to existing well-known methods. The results proposed method are further compared with minimum cross-entropy (MCE) based and Kapur's entropy-based thresholding and found a significant level of dominance over them.
Using Self-Driving Techniques for Blind Assistance

People with blindness tend to be more vulnerable to road accidents thus the need to have an assistance comes into picture and almost takes a form of necessity in order to ensure safety. Detection of objects and cautiousness on road tend to be utmost priority for those commuting thus various methodologies have been explored to achieve the same. For better readability of the path, one has to first classify its surrounding and further understand the traffic signs inorder to ensure utmost safety. Hence, this paper has been further classified into two sub sections in which first includes path segmentation and the latter elaborates on the classification of traffic signs, and furthermore explores methodologies accordingly.
Features of Low and Highly Susceptible Individuals in Retail Investment Fraud: A Machine Learning – Based Analysis

Investment fraud/scam is defined as the intentional misinterpretation, concealment, or omission of facts regarding promised goods, services, or other expectations by putting funds into investments that are not real, unnecessary, never intended to be fulfilled, or intentionally distorted for the purpose of monetary gain. We present in this paper, an analysis of individuals' features/characteristics of those who are highly susceptible to retail investment scamming using machine learning (ML) methods. Purposive sampling is applied in data collection, asking only those who've at least experienced being scammed in a retail investment. Participants' demographic profile, emotional intelligence scores, personality traits scores and financial literacy levels are collected as parameters for the analysis. The data (N = 177) is first submitted to a Boruta algorithm for feature selection and out of nineteen (19) input features, only seven (7) features are confirmed to be important in determining low or high likelihood of susceptibility in retail investment scamming. Afterwards, a 2 - cluster solution is revealed using the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> - means clustering. Cluster 1 is composed of individuals having higher number of times being scammed - characterized by higher social class, higher income, higher emotional intelligence scores, higher levels of agreeableness, openness and extraversion, and lower financial knowledge. Cluster 2 is composed of individuals having lesser number of times being scammed - characterized by lower social class, lower income, lower emotional intelligence scores, lower levels of agreeableness, openness and extraversion and higher financial knowledge. Findings of this study may serve as basis for prevention, protection and enforcement against retail investment frauds.