Volume 12 | Issue 5 |

 Abstract

Within cloud environments, Searchable Encryption (SE) serves as a vital tool for protecting data while maintaining its accessibility. The Cipher text-Policy Attribute-Based Keyword Search (CP-ABKS) relies on Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to allow for keyword retrieval and precise access control. However, existing CP-ABKS setups face challenges due to their dependence on a single attribute authority for user certificate validation and secret key distribution. This reliance leads to performance bottlenecks in distributed cloud setups. To address this issue, our research introduces a secure Multi-authority CP-ABKS (MABKS) system. This system aims to overcome these limitations and reduce computational and storage overhead on resource-constrained devices within cloud infrastructures. Additionally, enhancements have been made to the MABKS system to enable the detection of fraudulent attribute authorities and facilitate attribute updates

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 Keywords

Cipher text-Policy Attribute-Based Encryption, MABKS, , Multi-authority CP-ABKS

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the realm of machine learning, the Extreme Learning Machine (ELM) excels in classification and regression but falters with imbalanced data. To address this, we introduce OWA-ELM, an enhanced ELM algorithm with output weight adjustment. OWA-ELM strategically adjusts connection weights to favor minority classes, improving their classification accuracy without compromising majority class performance. Furthermore, we integrate OWA-ELM with the deep learning SMOTE algorithm to automatically synthesize similar data, addressing data imbalance more effectively. Our extended OWA-ELM with SMOTE outperforms other algorithms, achieving higher FSCOREs and demonstrating superior classification performance on imbalanced datasets.

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 Keywords

Imbalanced Data, OWA-ELM

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Creative Commons Attribution 4.0 and The Open Definition

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Ayurveda is the Oldest Medical System which Utilizes Different Surgical and Para-Surgical Interventions for Treating Disorders; Wide Description is Available in Ayurvedic Classics Regarding Various Surgical Instruments (Yantra) Used in Different Branches of Ayurveda for Carrying out Clinical Examination; Medical and Operative Procedures. Surgical Instruments Such as Forceps, Dilators, Speculums, Needles, Lancets etc. are Similar to the 101 Yantras Mentioned in Ayurveda as Swastika Yantra, Samdansha Yantra, Nadi Yantra, Shalaka Yantra, etc. to Perform Various Procedures, Use of Blunt Instruments are Described in Detail. Most of the Modern Surgical Instruments are only Slight Modifications of the Instruments Used by Ancient Hindu Surgeons. In this Article we have Discussed about those Yantras (Blunt Instruments) Which are Mentioned In Ayurveda and their Counterparts in Modern Surgical Practice.

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 Keywords

Ayurveda, Yantra, Modern Surgical Instruments

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project aims to enhance water quality monitoring by leveraging machine learning for improved accuracy and efficiency. It utilizes data collection, visualization, and pollution detection. A key feature is a web-based application that presents data interactively and generates comprehensive reports, aiding in timely decision-making. This system underscores the potential of technology in addressing environmental challenges and advocates for its wider application in resource management. The project serves as a testament to the power of technological innovation in preserving the environment.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Water Quality, Quality Monitoring, Water, Machine Learning, Water Pollution

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The importance of security in the authentication process as well as the increase in threat level posed by such malware has attracted many researchers to the field. Many attacks are successful in accessing social network accounts since the current password-based authentication paradigms are not efficient and robust enough as well as vulnerable to automated attacks. The traditional two-factor authentication mechanisms are not applicable to online social networks because physical token or biometric data cannot be easily used to log into users' profiles. The selection process ensures that each user's password is unique and virtually impossible to guess or replicate by an attacker. The system will be designed with user-friendliness in mind, offering an intuitive and engaging interface for symbol selection. The goal is to create a secure and memorable authentication system that mitigates the risk of unauthorized access.

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Keywords: Graphical Password, Shoulder Surfing, Authentication Scheme, Passwords, Graphical Authentication, Password Attacks.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Effective soil management is essential for maximizing agricultural productivity and ensuring sustainable food production. This project presents an integrated Soil Fertility Measurement and Automatic Spraying System designed to streamline soil management practices and optimize soil conditions in real-time. The system utilizes sensors to measure key soil parameters such as moisture content and NPK values, providing farmers with valuable insights into soil fertility levels. The measured data is displayed on an LCD screen, enabling farmers to make informed decisions about crop management practices. Further-more, the system incorporates an automated spraying mechanism controlled by water pumps driven by an L298 module. When soil parameters deviate from optimal levels, the system activates the spraying system to adjust soil conditions, ensuring that crops receive the necessary moisture and nutrients for healthy growth. By combining soil parameter measurement with automated spraying capabilities, the proposed system offers a comprehensive solution for enhancing agricultural productivity and sustainability. Real-time monitoring of soil fertility levels allows for timely inter-ventions to maintain optimal growing conditions, resulting in improved crop yields and re-source efficiency. The integration of sensor technology and automation reduces the reliance on manual labor and streamlines soil management processes, enabling farmers to achieve higher yields while minimizing environmental impact. Overall, the Soil Fertility Measurement and Automatic Spraying System represent a significant advancement in soil management practices, with the potential to revolutionize agricultural productivity and con-tribute to global food security efforts.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Arduino UNO, Soil Moisture Sensor, NPK Sensor, L298 Motor Driver

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The healthcare industry struggles to find and keep skilled workers who can adapt to patient care needs. This mixed-methods study examines the complex dynamics of healthcare workforce issues using a comprehensive literature review and primary survey data analysis. Primary data analysis can illuminate demographics, job satisfaction, and how workforce issues affect patient care and organisational performance. Healthcare personnel span many ages and genders, according to the findings. There is a strong pattern showing how many factors make a happy workplace. Clear advancement paths, a decent work-life balance, and supportive coworkers are examples. The results suggest that business culture, leadership styles, and personal preferences affect healthcare worker retention. The study shows that workforce issues greatly impact patient care and organisational effectiveness. Exhaustion and burnout increase personnel shortages, which threaten quality care. Patient safety and organisational productivity are affected across healthcare facilities. Such findings emphasise the need for targeted healthcare worker shortage relief and resilience strategies. This study adds to healthcare worker dynamics knowledge by giving empirical facts and practical insights. Its detailed synthesis of primary survey data and secondary literature help map workforce management and planning for health care organisations, legislators, and others. Impacts extend beyond academia and affect healthcare personnel acquisition, retention, and development strategies. This report urges the healthcare industry to collaborate to overcome its workforce crisis. These findings and ideas can help healthcare companies develop workplaces that encourage employee health and satisfaction while providing excellent, person-centered care.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Healthcare workforce, workforce challenges, job satisfaction, patient care outcomes, organizational performance, mixed-methods approach, recruitment, retention, workforce resilience, healthcare management.

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Creative Commons Attribution 4.0 and The Open Definition

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In this study, we present an enhanced version of the DenseNet deep neural network model tailored specifically for tuberculosis (TB) detection using chest X-ray images. Leveraging the inherent advantages of DenseNet's densely connected layers, we propose novel architectural modifications and optimization strategies to improve both the model's performance and efficiency. Through extensive experimentation on a diverse dataset, we demonstrate significant enhancements in sensitivity, specificity, and overall accuracy compared to existing methods. Our improved DenseNet model exhibits robustness in detecting TB manifestations in chest X-ray images, holding promise for enhancing diagnostic capabilities in clinical settings

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Convolutional neural network, deep learning, tuberculosis, chest X-ray, and disease diagnosis.

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Creative Commons Attribution 4.0 and The Open Definition

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Rice varietal identification plays a crucial role in agricultural research, food safety, and quality control. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have emerged as powerful tools for image classification tasks, including the identification of different rice varieties. This paper presents a comprehensive approach to leveraging CNNs for accurate rice varietal identification. The methodology begins with data collection and preparation, involving the assembly of a diverse dataset encompassing various rice varieties under different lighting conditions and backgrounds. Supervised learning is employed, with images labelled according to their corresponding rice variety. Preprocessing techniques such as normalization and augmentation are applied to enhance dataset robustness. Next, a suitable CNN architecture is designed, drawing upon established models like sequential, or developing custom architectures tailored to the task. Emphasis is placed on maintaining spatial information and handling input images of varying sizes effectively. Techniques such as batch normalization, dropout, and appropriate activation functions are incorporated to enhance model generalization and prevent overfitting. The model is then trained on the prepared dataset, with careful consideration given to training-validation-test set splits and hyperparameter tuning. Various optimization algorithms such as stochastic gradient descent (SGD) and Adam are explored to optimize model parameters while preventing overfitting through regularization techniques.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Rice varietal identification, Deep learning, CNNs, Supervised learning, Data collection, preparation, CNN architecture

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Creative Commons Attribution 4.0 and The Open Definition