Volume 12 | Issue 5 |

 Abstract

The blood cell classification from smear images of peripheral also known as PBS is a crucial step in diagnosing blood-related illnesses such as anemia, leukemia, infection, polycythemia and malignancy. Hematologists regularly utilize a device which zooms to microscopic level to count, shape, and distribute the cells before making a judgment in blood cell-based analysis. Hematology analyzers and flow cytometry give an accurate and precise CBC that identifies and gives the abnormalities in given smear slides of blood. In multiple hospitals, the techniques that are used are costly, least effective in terms of time and are hectic as they are manual. As a result, a reliable, affordable, and automatic method for identifying different sickness through given PBS image is required. The new proposed model automatically does the examination of the provided data, also does in a faster manner. Therefore, in the studied research we have properly and accurately done the classification of images in such a way that the different cells are classified as the WBCs and RBCs. Feature extraction is done to classify the images. These extracted texture features are subsequently inputted into various classifiers, including artificial neural networks (ANN), machines that are SVM and many other model which are in ML and DL. After comparing the performance metrics, it is determined that the logistic regression method is the most appropriate for the task at hand.

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 Keywords

Images of microscopic blood smears, ML, RBC, Feature identification and export, WBC, CNN, Neural Networks.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The agricultural sector faces significant challenges in terms of transparency, traceability, and operational efficiency within its supply chains. Conventional methods often struggle to provide timely tracking, authentication, and trust among stakeholders. In recent years, blockchain technology has emerged as a promising solution to tackle these challenges. This study investigates the application of blockchain in agricultural supply chains, known as Agrichain. Agrichain utilizes the decentralized and immutable features of blockchain to enhance transparency and operational efficiency across agricultural supply chains. Through distributed ledger technology, Agrichain facilitates secure and transparent data exchange among farmers, distributors, retailers, and consumers. This study delves into the core elements of Agrichain, including smart contracts for automating transactions, blockchain-enabled traceability for verifying product origin and quality, and decentralized data management for improved supply chain transparency. The research underscores Agrichain's potential to transform the agricultural industry by fostering trust, reducing costs, ensuring food safety, and advancing sustainability efforts.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Agrichain, blockchain, supply chain, solution, transparent, study, fostering, safety, sustainability

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In contemporary democracies, guaranteeing the security and transparency of voting procedures holds paramount importance. Vote Chain, a blockchain- based voting system, addresses these crucial concerns. This paper conducts a thorough review of existing literature regarding the incorporation of blockchain technology into voting systems. By analyzing different algorithms and performance metrics, it assesses the efficacy of Vote Chain in improving the precision and integrity of electoral processes. Furthermore, the paper deliberates on significant challenges and suggests future research avenues to further enhance the capabilities of blockchain-based voting systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Block chain, Smart Contracts, Vote Chain, Meta Mask, Ganache, Online Voting

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cryptocurrency represents a digital form of currency characterized by electronic transactions, devoid of physical representation. It contrasts with traditional fiat currency, which is typically centralized and subject to thirdparty oversight. The emergence of digital currencies has introduced a decentralized alternative to traditional financial systems, offering users greater autonomy and control over their financial transactions. Despite the absence of physical form, cryptocurrencies hold significant value and have gained traction as viable mediums of exchange. However, the accessibility of cryptocurrencies has the potential to disrupt international relations and financial markets due to their inherent volatility. The fluctuating prices of cryptocurrencies, including Bitcoin, Ripple, Ethereum, and others, pose challenges for investors, traders, and policymakers alike. Among these virtual currencies, Bitcoin protrude as one of the broadly accepted and recognized cryptocurrencies, enjoying widespread adoption across various sectors. Our analysis goals to develop effective prediction models leveraging deep learning techniques, specifically Long Short Term Memory (LSTM) to forecast changes in Bitcoin prices accurately. By utilizing the potential of deep learning algorithmic programs, we seek to examine historical Bitcoin cost data and analyze designs that can inform future price movements. By leveraging machine learning methodologies, we aim to offering valuable perspectives for investors. for investors, researchers, traders, and policymakers looking to traverse changing landscape of cryptocurrency markets with greater confidence and precision.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Bitcoin , Machine Learning , Electronic , LSTM, Finanace, Trading, Digital currency, Neural Network, Analysis.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cryptocurrency stands as a digital representation of value, existing purely in the digital realm, free from any physical form or centralized control. All transactions are made electronically. Cryptocurrency is a valuable asset that transcends traditional notions of physical currency, existing solely in digital formats within decentralized connectivity. Here we highlight the difference between fiat currency, which is decentralized and free of third-party intervention, and the service offered to all virtual currency users. However, access to cryptocurrencies can disrupt international relations and markets due to their volatile prices. There are many virtual currencies such as Bitcoin, Ripple, Ethereum, Ethereum Classic, Litecoin and more. In our research, we focus specifically on Bitcoin, a popular cryptocurrency. Among various virtual currencies, Bitcoin is widely accepted by many organizations, including investors, scholars, traders and legislators. To the best of our capability, our aim is to make effective prediction models based on deep learning, especially Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), to control Bitcoin price change and achieve its accuracy. Our research involves comparing two real-time deep learning techniques and demonstrating their effectiveness in predicting Bitcoin price.

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 Keywords

Cryptocurrency , Bitcoin , Transactions , Decentralized , LSTM , Finance, Trading, Virtual currency, Forecasting.

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 Abstract

Accurate identification of patients is a pressing issue for many countries in the Asian region. However, accurate patient identification remains the cornerstone of healthcare care and quality. There are several reasons for poor patient identification systems in regional hospitals: Larger hospitals tend to have poor patient management, and clinics exercise a level of financial and personal control that facilitates competition for patient management. system (each department must be responsible for its own bookkeeping). As a result, patients have multiple medical department records and identification numbers. In addition, the lack of a Master Patient Index (MPI) is a standard practice, meaning there is no centralized system for patient identification that cross-references available patient data.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

patients, healthcare, master patient index (MPI), biometric authentication in healthcare, patient records, medical records

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Accurate identification of patients remains not less than a headache in many countries in the sub-Asian region. Nonetheless, accurate patient identification is still essential to providing high-quality, safe healthcare. The following explanations help to explain why patient identification processes in Sub-Asian hospitals are flawed: Larger hospitals tend to have decentralized patient administration because the adoption of a degree of financial and managerial autonomy for clinical departments has encouraged the proliferation of redundant administrative patient management systems (every department wanting to handle its own bookkeeping). Patients so obtain ID numbers and medical records that are department-specific. Furthermore, it should be noted that the lack of a master patient index (MPI) is a general rule, meaning that there are no central patient identification systems that make use of the department's current patient information.

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 Keywords

patients, healthcare, master patient index (MPI), biometric authentication in healthcare, patient records

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

PCOS is a common endocrine condition that affects women who are fertile. It is typified by irregular period, ovarian cysts, and assesses. In order to mitigate the potential long-lasting health issues linked with... PCOS, premature discovery and intervention are essential. In this study, WeCare--a thorough women's wellbeing guide that incorporates front-line technologies for PCOS detection--is introduced. WeCare provides a novel method for the prompt and reliable diagnosis of PCOS by analyzing medical imaging data, including sonography scans and lumbar MRI images, by utilizing Convolutional Neural Network (CNN) algorithms. WeCare improves access to healthcare by utilizing deep learning to enable early intervention, individualized treatment programs, and better symptom management for PCOS By combining advanced technology with healthcare expertise, this interdisciplinary approach enables women to proactively manage their reproductive health and overall well-being.

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 Keywords

Convolutional Neural Network (CNN), Diagnosis, Medical Imaging, Early Intervention, Personalized Treatment, Reproductive Health, Polycystic Ovary Syndrome Detection, Deep Learning- based Hormone Analysis, PCOS Diagnosis Algorithm

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In institutions with a high student population, manually creating schedules takes a lot of effort and frequently leads to scheduling issues, where teachers teach multiple classes at once or where classes compete with one another over timing or space. Numerous limitations and problems with the technology arise from this manual procedure. The organization's inability to meet demands in a timely manner and the possibility of inaccurate results are mostly attributable to frequent human errors that are challenging to avoid in such procedures. Our suggestion is to create an automated mechanism in order to address these problems. The Automatic Timetable Generator system would receive a variety of inputs, including information on the faculty, students, and subjects, and use this information to create a workable schedule that makes the most use of all available resources and best fits the given constraints or college policies. An automated system called the Adaptive Timetable Generator creates timetables based on user-provided data. Gathering information on the branch, semester, subjects, laboratories, and total number of periods is one of the application's primary needs. Students must select their electives from a list of subjects that may contain both core and elective courses. After that, the application creates the schedule based on the parameters.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Automated, time-table, constraints, college, clashes.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

To streamline and expedite the process of contributing to charities, a software project known as the Donation System was developed. This method acts as a link between donors and recipients in an increasingly digital environment, guaranteeing an open and effective approach for helping the underprivileged. Donors can examine a variety of charity organizations, track the impact of their efforts, and make online contributions thanks to the system. Beneficiaries benefit from the system's assistance in efficiently managing and allocating donations. By working together on this initiative, we hope to improve the philanthropic ecosystem as a whole and encourage a culture of giving while improving the lives of people in need. The Helping Hand Donation System (HHDS) is an all-inclusive platform created to make charity donations and aid to underprivileged people and communities easier. This method attempts to close the gap between help providers and recipients by offering an easy-to-use interface for sending money and allocating aid efficiently. To optimize its effects and guarantee sustainability, accountability, and openness, the HHDS integrates a number of characteristics and capabilities. The goal of the HHDS design and development phase is to create a platform that is easy to use and suitable for a wide variety of users. This provides functions for tracking donations, processing secure payments, and reporting. To guarantee that everyone who could donate or benefit from the system may do so, special consideration is given to accessibility and inclusivity.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Donation, Charitable Organization, Donors, Beneficiary, Android

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Creative Commons Attribution 4.0 and The Open Definition