Reviewerly/exHarmony huggingface dataset:
Prediction of Disease from Blood Microscopic Analysis Classification using Deep Learning Algorithm

Traditional blood disease detection based on visual inspection of blood smears using a microscope is time consuming, error-prone, and limited by the physical acuity of the hematologist. To facilitate clinical decision-making, an automated optical image processing system is required. Leukemia is a kind of cancer that is distinguished by the aberrant development of immature, abnormally shaped white blood cells (WBC) known as "blasts." Leukemia is a cancer of the white blood cells (WBCs) that affects the bone marrow and/or blood. A timely, safe, and accurate diagnosis of leukemia at an early stage is critical to treating and preserving patients' lives. The white blood cells in a blood smear are often examined under a microscope to determine the diagnosis.Many machine learning have been created to diagnose various illnesses, such as leukemia, and to deliver a high mis-classification error rate. As a result, we may use a deep learning system to identify microscope pictures for White Blood Cell Count study. The WBC differential counting system was divided into two modules: detection and classification. The detection module first processed the raw bone smear pictures, detecting all WBCs including red blood cells, platelet counts, staining impurities, and so on. The discovered cells were then sent into the categorization module.The categorization module was divided into two phases. In the first step, we separated a large number of cells, such as crushed cells, degenerated cells, and so on, that are not employed in the diagnosis for leukemia. The countable WBCs were then presented for multi-class separation using the Convolutional neural network technique in the second step
San-Eng: Sanskrit to English Translator using Machine Learning

In the past decade, machine learning has made great strides in improving automatic translation. In general, machine learning algorithms have been able to achieve better translation quality by learning from large amounts of data. One approach to machine translation is to use a statistical machine translation (SMT) system. SMT systems learn to translate by statistical methods, using a large parallel corpus of text in multiple languages. The most successful SMT systems are based on the translation model known as the phrase-based translation model. In recent years, a new approach to machine translation has emerged, known as neural machine translation (NMT). NMT systems use artificial neural networks to learn to translate. NMT systems have shown to be very successful in translating between languages that are closely related, such as English and Hindi or Reverse is also true. In recent years, a new approach to machine translation has emerged, known as neural machine translation (NMT). NMT systems use artificial neural networks to learn to translate. NMT systems have shown to be very successful in translating between languages that are closely related, such as English and Hindi or Reverse is also true
Fertilizer Supply Chain Management using Blockchain

This paper explores the potential use of blockchain technology in fertilizer supply chain management. The project begins by identifying the current issues and challenges in the fertilizer supply chain and how blockchain technology can address them. The project team then designs and implements a blockchain-based supply chain management system for the fertilizer industry. The allocation of subsidies is also covered in the study, along with problems including corruption, poor management, and a lack of openness.. The team utilizes smart contracts, decentralized data storage, and consensus mechanisms to improve transparency, traceability, and efficiency in the fertilizer supply chain. The system is evaluated through user testing, and feedback is collected to identify strengths and weaknesses. The results show that the blockchain-based supply chain management system can significantly improve the efficiency and accountability of the fertilizer supply chain. The project concludes by providing recommendations for the implementation of blockchain technology in the fertilizer industry, emphasizing the need for standardization, collaboration, and stakeholder engagement. This project aims to contribute to the practical application of blockchain technology in the fertilizer industry and provide a foundation for further research in this field.
Information Retrieval System for College Search

An information retrieval system for college search is a crucial tool for students to find the best college for their academic needs. The system uses text mining, web mining, data mining, machine learning, and semantic web techniques to provide relevant and accurate results to users. The system collects data from various sources such as college websites, student reviews, and academic databases to create a comprehensive database of colleges and their relevant information. Users can search for colleges based on their preferences, such as location, academic program, admission requirements, tuition fees, and other factors. The system also provides personalized recommendations based on the user's search history and preferences. The proposed system aims to improve the college search process for students by providing a user-friendly interface and accurate information to make informed decisions about their education.
Privacy Preserving Voting Scheme Based on Blockchain Technology

Democracy has made voting incredibly important in any nation due to a general scepticism of the traditional voting process. Individuals have witnessed violations of their fundamental rights. Lack of transparency has led to challenges to other digital voting systems. The majority of voting methods are not sufficiently transparent, which makes it highly challenging for the government to win over voters' trust. The previous and present digital voting systems have failed because they are vulnerable to abuse. The main goal is to fix issues with the traditional and electronic voting systems, including any form of error or unfairness that may occur when voting. To ensure a fair election and lessen unfairness, blockchain technology can be included into the voting process. The computerised voting methods are not flawless enough to be used on a broad scale, and the physical voting systems have numerous problems as well. This evaluates the requirement for a remedy to safeguard citizens' democratic rights. In order to establish a trustworthy working connection between voters and election officials, this article introduces a platform based on cutting-edge blockchain technology. Without using any actual polling places, the proposed technology offers a framework that may be used to conduct voting activity digitally through blockchain. Our suggested design uses adaptable consensus algorithms to support a scalable blockchain. Blockchains with pre-established validation procedures were developed for a specific voting storage.Voter verification will be provided by IRIS Recognition. All systems in a value chain save blocks of time-stamped voting. Blockchain is a log of transactions where members of a community may track asset transfers. Using the SHA-256 technique, two blocks are connected to one another. It has also been elaborated on how to encrypt transactions using cryptographic hashes and guard against 51% attacks on the blockchain. Also, the method for conducting blockchain transactions during the voting process has been developed using Blockchain Lastly, the performance assessment of the suggested method demonstrates that it may be used to a sizable population
Staying Safe Online: Security Measures by University Students to Secure Privacy

Security measures are meant to protect the three key principles of security which are confidentiality, integrity and accountability. Previous research has shown that humans are the weakest link in any information security system and focus must be given to them. In light of the steady increase of cybercrime incidents, this study aims to investigate the security measures applied by university students in order to secure their own privacy, security and the organization as a whole. Through quantitative descriptive analysis, it is evidenced that the students have good security awareness through their selection of security measures that they apply. However, none of the security measures suggested were 100% implemented by the students. This shows that there are still loopholes available for security vulnerabilities to happen. There are needs for continuous effort from the management to ensure that there are Security Guidelines for the students to follow and that all the security measures suggested are applied accordingly.
Dog Breed Prediction Using Deep Learning and E-commerce

This project uses computer vision and machine learning techniques to predict dog breeds from photographs and provides a trustworthy and efficient e-commerce platform for dogs. With the help of various machine learning models such as convolutional neural networks, our model gives the correct breed 90% of the time. Apart from that, our web application also provides an e-commerce platform for buying of dogs.
Data Integrity Verification in Cloud Computing

High security is one of the major obstacles for opening up the new era of the long dreamed vision of Cloud computing as a utility. In today’s trend of cloud computing, all the sensitive applications and data are moved towards cloud infrastructure and data center that run on virtual computing resources in the form of virtual machine. These attributes poses many security challenges such as accessibility vulnerabilities, virtualization vulnerabilities and web application vulnerabilities. We primarily aim to achieve better data integrity verification technique for Data Storage as a Service (Daas) in cloud computing and help users to recognize the threats associated with their uses.
Ekart - Smart Shopping PushCart

In newfangled wen, snap up at large emporium is a quotidian divertissement. On commemoration and on induct salvage days, one can descry a facund fleet at emporium. kins chaffer profuse chattels, hauls them onto gigs, and then prosecute to the ackers register to compel reckoning. They obliged to halt in procession in the interim that time for their chattels to be scrutinize to wield a RFID reader and to receive a bill a barcode scrutinizer are wield in edict . To transposed that, patrons must profuse while booty in arcade. When put in a smart snap up e-kart, every totality commodities has an RFID tag adhere, which can be instantaneously peruse by a gig with an RFID reader so that exhibiting can be crisp from the snap up gig itself.. kins can circumvent endurance in procession at the appraise in antithetical manner. Extra smart shelving that is RFID reader equipped and can oversee the repertory as well as refurbished the central server, and it can also cognizant of the stature of the commodity in the store, can be append. Because all the merchandise can be instantaneously scrutinize by an RFID scrutinizer somewhat than had operated scrutinize by labor, catalogue admin also flatter much unadorned. The current method of shopping is time-consuming and involves many steps, such as selecting the goods, standing in line, scanning them, and checking out. This is a time-consuming procedure, and IOT can be used to speed it up and address some of its problems. The EKART is the answer to the aforementioned issue; this cart is fitted with sophisticated microcontrollers and sensors that shorten the billing process by scanning the items instantly as soon as they are added to the cart, which also ends lines in shopping malls. Every cart could be fitted with inexpensive sensors and microcontrollers, and a security scanner would be installed at the exit to sound an alarm if money had not been received. By doing this, the market's recent challenges would be resolved. A digital shelf would be one of this system's applications. This logically results in the following advantages. The artufacts accomodate in a smart snap up gig (which has RFID reading capabilities) can be hurriedly scrutinize, and the smart gig can also procure the exhibhiting information. kins no prolonged obliged to wait in extensive procession at the bursar. All replete commodity can be oversee by the smart shelves with RFID scrutinizer, so that can also be televise server-side commodity statures amend. The server can alert staff to restock when an object runs out. As all items can be automatically read and readily logged, inventory management for the shop is made simple. wherefore we conclude, that a circumspect smart snap up system that compel the employ of RFID techno. Our study is a trailblazer in the creation of a smart snap up gadget because it is anticipated that future stores will be outfitted with RFID technology. The present system can be improved, for instance, by lowering the computational overhead at the smart cart side for greater efficiency, or by figuring out how to improve communication efficiency.
Syntactic reasoning with conditional probabilities in deductive argumentation

Evidence from studies, such as in science or medicine, often corresponds to conditional probability statements. Furthermore, evidence can conflict, in particular when coming from multiple studies. Whilst it is natural to make sense of such evidence using arguments, there is a lack of a systematic formalism for representing and reasoning with conditional probability statements in computational argumentation. We address this shortcoming by providing a formalization of conditional probabilistic argumentation based on probabilistic conditional logic. We provide a semantics and a collection of comprehensible inference rules that give different insights into evidence. We show how arguments constructed from proofs and attacks between them can be analyzed as arguments graphs using dialectical semantics and via the epistemic approach to probabilistic argumentation. Our approach allows for a transparent and systematic way of handling uncertainty that often arises in evidence.
Semantic-guided de-attention with sharpened triplet marginal loss for visual place recognition

Thanks to Earth-level Street View images from Google Maps, a visual image geo-localization can estimate the coarse location of a query image with a visual place recognition process. However, this can get very challenging when non-static objects change with time, severely degrading image retrieval accuracy. We address the problem of city-scale visual place recognition in complex urban environments crowded with non-static clutters. To this end, we first analyze what clutters degrade similarity matching between the query and database images. Second, we design a self-supervised trainable de-attention module that prevents the network from focusing on non-static objects in an input image. In addition, we propose a novel triplet marginal loss called sharpened triplet marginal loss to make feature descriptors more discriminative. Lastly, due to the lack of geo-tagged public datasets with a high density of non-static objects, we propose a clutter augmentation method to evaluate our approach. The experimental results show that our model has notably improved over the existing attention methods in geo-localization tasks on the public benchmark datasets and on their augmented versions with high population and traffic. Our code is available at https://github.com/ccsmm78/deattention_with_stml_for_vpr.
Comprehensive study of the relationship between multiverse and big data

Studies linking two broader spectra of topics have fascinated scholars in many aspects. Here we tried associating two such far-reaching aspects which have finite connectivity between them. Multiverse has been the talk of the hour which explains the theory of multiple universes which exist in parallel. This is a topic in physics concerned with many relative matters. On the other side, Big data is the subject in computing and information science describing the volume, velocity, and variability of the data hitting computer-connected systems. Big data can only be handled with newer architectures, algorithms, and methodologies as its features are contradicting regular computer systems and networks. It is well known that multiple processors are required to handle big data existing in parallel performing a single job given by the data analyst. So as we know, multiverse consist of hypothetical concepts of several parallel universe having everything like information, energy, and time. However, we see this situation to draw an association connecting parallel universes of the multiverse with parallel processors of big data by incorporating the concepts of working of parallel universe in the processing of Big Data. We provide a comprehensive observation on both the topics and take positive lenience on bringing a newer terminology in data science. History of multiverse along with big data structures are brought in with related parameters. This aspect is novel in its nature and we complement the literature carried out by the researchers and scholars appropriate analysis. We also showcase a model of the school of thought mentioned above in drawing conclusions.
Music Segmentation and Similarity Estimation Applied to a Gaze-Controlled Musical Interface

Assistive technology, especially gaze-controlled, can promote accessibility, health care, well-being and inclusion for impaired people, including musical activities that can be supported by interfaces controlled using eye tracking. Also, the Internet growth has allowed access to a huge digital music database, which can contribute to a new form of music creation. In this paper, we propose the application of Music Information Retrieval techniques for music segmentation and similarity identification, aiming at the development of a new form of musical creation using an automatic process and the optimization algorithm Harmony Search to combine segments. These techniques for segmentation and similarity of segments were implemented in an assistive musical interface controlled by eye movement to support musical creation and well-being. The experimental results can be found in [https://bit.ly/2Zl7KSC].
Utilization of Used Bus for Children’s Reading and Learning Facility in Kemayoran Area

Abstract Utilization of used bus by redesigning its interior which takes the concept of curved, non-sharped, and non-angled from ladybug anatomy. Looking at the problems of dropouts in the Kemayoran area, used bus can serve as a means and learning facility for them. The bus is located in the Kemayoran Forest area with consideration of easy-to-reach distance, safety, and user comfort. The method used is a qualitative research method with data collection and analysis of case studies described narratively. The results of the design can help underprivileged children continue to be able to open insights and get training for certain skills to improve their standard of living of them and their families in the future.
Kernel-Free Nonlinear Support Vector Machines for Multiview Binary Classification Problems

Multiview learning (MVL) frequently uses support vector machine- (SVM-) based models, but it can be difficult to select appropriate kernel functions and corresponding parameters. Then, by introducing kernel-free tricks, two multiview classifiers are proposed, called <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>C</mi> </math> -multiview kernel-free nonlinear support vector machine ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>C</mi> </math> -MKNSVM) and its <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>ν</mi> </math> -version, namely, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>ν</mi> </math> -MKNSVM. They try to find a quadratic hypersurface under each view to classify the sample points and employ a consistency constraint to fuse the sample points from two views. Both the primal and dual problems of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>C</mi> </math> -MKNSVM and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>ν</mi> </math> -MKNSVM do not involve kernel functions; thus, they are allowed to be solved directly. In addition, the relationship of solutions between the primal and dual problems is discussed in each classifier. For the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>C</mi> </math> -version and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mi>ν</mi> </math> -version of MKNSVM, the meanings of their parameters and the relationship between them are analyzed in detail. The experimental results of artificial and benchmark datasets show that our methods are superior to some traditional MVL classifiers like SVM-2K, PSVM-2V, and MvTSVM, especially <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mi>ν</mi> </math> -MKNSVM.
Performance Analysis of Fully Connected Parametric Activation Functions

Modern studies have shown that the choice of activation function can significantly affect the performance of several learning methods and deep networks. The activation function plays an important role in solving the nonlinear problem, and various nonlinear fully connected activation functions have been studied. In this paper, we propose a combined parametric activation function that can improve the performance of a fully connected Artificial intelligence methods and neural network. Combined parametric activation functions can be created by simply adding parametric activation functions. The parametric activation function is a function that can be optimized in the direction of minimizing the loss function by applying a appropriate parameter that converts the scale and location of the activation function according to the input data. The development of Artificial Neural Networks (ANNs) has achieved a lot of fruitful results so far, and we know that activation function is one of the principal factors which will affect the performance of the networks. In this paper, we discussed about the impact of varying model width and depth on robustness, the impact of using learnable parametric activation functions (PAFs).
Predictive Analysis in Banking using Machine Learning

This paper discusses the utilization of machine learning for predicting loan approval and credit card fraud detection. Specifically, the paper proposes the use of the Random Forest Algorithm and Support Vector Machine Learning Algorithm for achieving better accuracy. The banking sector's main objective is to ensure their assets are in safe hands, and to achieve this, a verification process is carried out. However, the process takes a long time, and there is no guarantee of selecting deserving applicants. To address this problem, a system has been developed that predicts the suitability of an applicant for loan approval based on a model trained using machine learning algorithms. The system has achieved 92% accuracy using the Random Forest Algorithm. The system has a user interface web application where users can input necessary details for the model to predict. The system's drawback is that it considers multiple attributes, whereas, in real life, a loan application may be approved based on a single strong attribute, which the system cannot detect. With the increasing number of online transactions, credit card usage has become more prevalent. Losing physical credit cards or credit card information can result in significant financial loss. Therefore, there is a need to detect fraudulent transactions and secure them. To address this issue, the paper proposes the use of the Support Vector Machine Algorithm, which focuses on analyzing and preprocessing data sets and deploying fraud detection using Credit Card Transaction data.