Volume 13 | Issue 1 |

 Abstract

Social media is a weapon that is capable of construction as well as destruction. The real power of the prevailing social media platforms becomes evident by witnessing the influence created by these platforms on a large scale. It plays a significant role in everyday life. The rising popularity due to its ability to make people attached with kith and kin have paved the way for the world to share photos, feelings, videos, which bears a high-security concern. However, most social media users do not know the underlying security level(s) of the respective account(s), including which features of these social media have to be considered if there is a risky situation. Hence, this would help the police to identify the type of people who would create more crimes. These results would help the police to narrow down their search on criminals for better surveillance. The police must focus on those with these factors while monitoring social media.

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 Keywords

social media, cyber crime ,cyber criminals

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The early detection of sexually transmitted diseases (STDs) and sexually transmitted infections (STIs) is critical for effective treatment and prevention of complications. With advancements in artificial intelligence, machine learning algorithms offer promising tools for accurate and efficient diagnostics. This study presents a comparative analysis of tree-based algorithms, with an initial focus on Random Forest, for the detection of STDs/STIs using publicly available datasets. The model achieved an accuracy of 96%, demonstrating the effectiveness of tree-based methods in medical diagnostics. A detailed evaluation of the model's performance, including a confusion matrix and feature importance analysis, is included. Future work will focus on implementing deep learning algorithms to enhance detection accuracy and generalizability. This paper serves as Phase One of an ongoing study aimed at leveraging machine learning for improved healthcare diagnostics.

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 Keywords

Tree Learning, Deep learning, Sexually Transmitted Disease (STD) Prediction, Random Forest.

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This study reviews the changes in the Gulpha Sandhi Sharir, with a specific focus on Amavata. Gulpha Sandhi Sharir refers to the ankle joint in Ayurveda, which is crucial for movement. Amavata is a chronic inflammatory disorder that resembles rheumatoid arthritis and primarily affects the joints, leading to pain, swelling, stiffness, and limited mobility.The review examines key structures in the Gulpha Sandhi, including bones, ligaments, tendons, and synovial membranes, and explains how Amavata impacts these structures. By analyzing classical Ayurvedic texts alongside modern medical literature, this study clarifies the development of Amavata, highlighting the accumulation of Ama (toxins) and its interaction with Vata dosha. The study discusses significant issues such as joint tissue damage, abnormalities in synovial fluid, and inflammation. It aims to link Ayurvedic principles with modern anatomical understanding, providing valuable insights for the management and treatment of this challenging condition.

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 Keywords

Gulpha Sandhi, Sharir, Amavata, Synovial Fluid

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Potato diseases, such as late blight and early blight, pose significant threats to global agricultural productivity. Manual disease detection is time-intensive and error-prone, necessitating automated solutions. This study proposes a deep learning-based system using Convolutional Neural Networks (CNNs) for identifying potato leaf diseases. The model was trained and tested on an augmented dataset, achieving a classification accuracy of 97%. It effectively distinguishes healthy leaves from diseased ones. The system's robustness is validated through cross-validation and practical testing. Key advantages include high accuracy and scalability for agricultural applications. The approach can be extended for real-time use through IoT devices. This work aims to assist farmers in early disease detection, reducing crop losses and enhancing productivity.

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 Keywords

Potato disease identification, deep learning, CNNs, late blight, early blight, image classification, agricultural technology.

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 Abstract

In this paper, we propose a Vehicle Recommendation System leveraging the power of Artificial Intelligence (AI) and Random Forest methodology. Our system is designed to recommend vehicles to users based on their specific preferences and requirements. Using a dataset of 1265 entries and 27 features, the AI model identifies the most suitable vehicle for customers by analyzing their responses to a series of pre-determined questions. This project demonstrates the application of Random Forest for classification and recommendation tasks, showcasing its effectiveness in decision-making processes.

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 Keywords

Vehicle Recommendation System,Random Forest,Artificial Intelligence

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the age of artificial intelligence (AI) and graphics processing units (GPUs), the flip-flop (FF) has emerged as one of the processor's most power- hungry elements. A unique single- phase-clock dual edge triggering (DET) FF employing a single transistor clocked (STC) buffer (STCB) is suggested as a solution to this problem. The clock redundant transitions (RTs) and internal RTs present in previous DET designs are eliminated entirely by the STCB's use of a single- clocked transistor in the data sampling path. The suggested STC-DET beats the previous state-of-the- artlow-powerDET in power consumption by 14% and 9.5%, at 0.4 and 0.8 V, respectively, when running at 10% switching activity, as shown by post-layout simulations in 22 nm fully depleted silicon on insulator (FD-SOI) CMOS. Among the DETs, it also attains the lowest power-delay-product (PDP).

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 Keywords

Flip-Flop, Dual-Edge Triggered, Low Power, TSPC (TrueSingle-PhaseClock), Redundant- Transition-Free Clocked Single Transistor

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Since its beginning in 1987, the Research Department has successfully completed more than 1,200 projects, leading to a substantial accrual of files and papers related to each study. Currently, these records exist in the form of an Excel sheet and Word documents and pose quite an obstacle for Research Administration. to keep abreast, monitor, and track their progress as ongoing studies. The increasing volume and complexity of these projects have made it difficult to supervise research activities effectively and ensure smooth progress across multiple stages. This paper explores the challenges faced by the department due to the existing manual record-keeping system and proposes a more efficient, centralized approach to project management. By incorporating advanced technology and the most up-to-date data management, this research strives to smooth the process of monitoring while upgrading tracking abilities to ensure maximum efficiency in administering research.

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 Keywords

Project management

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid adoption of automation technology has changed how organizations work repetitively and with high performance. This paper presents a new automation approach that combines artificial intelligence (AI) with robotic process automation (RPA). The proposed AI-powered RPA system addresses issues in areas such as insurance avoidance, payment delay prediction, and vulnerability detection, where traditional RPA systems often fall short. Using machine learning models to identify anomalies and predict payment delays, thereby improving the entire decision-making process. Furthermore, the system is designed to efficiently generate receipts, distribute transactions, and plan by combining business-based rules with intelligent measures. This study evaluates the efficiency, accuracy, and scalability of AI-enhanced RPA systems compared to traditional RPA solutions, highlighting the advantages in dynamic environments and data-driven environments. These studies aim to establish guidelines for the technology and lay the groundwork for the future advancement of smart plugs.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

email automation , finance receipting, robotic process automation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Alpha hydroxy acids are the organic compound which are used in dermatology for treating and enhancing the skin health and aesthetics. These acids are obtained from natural sources such as fruits and milk. Alpha hydroxic acids have clinical applications on various skin disorders such as Actinic Keratosis, Psoriasis, Scarring, Seborrheic Dermatitis, Hyperpigmentation, Razor Bumps, Eczema, Stretch Marks, Calluses etc. There toxicological concerns are concentration and exposure related. Alpha hydroxy acids have great potential to improve it's scope in medical field.

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 Keywords

Alpha Hydroxy Acids, Dermatology, Citric Acids, Chemical Peels

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The seizure episodes of epilepsy have a considerable effect on the patients whom they occur. The aim of this paper is to demonstrate that the early assessment of machine learning approaches in conjunction with EEG signals can be beneficial in preventing or minimizing these episodes. Such methods particularly include data preprocessing, feature selection for, and classification of EEG signals but not limited to. This study is concerned with methods which allow modeling of the preictal condition, that is the one which occurs close to the onset of seizure. It has been shown that better signal to noise ratio (SNR) can assist in identification of electromagnetic activity and estimation their loss potential. Support Vector Machines (SVM) were among the techniques deployed in machine learning in this approach, precision and sensitivity were the key parameters in determining effectiveness. The methods used here are both simpler and more effective than previously.

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 Keywords

Epilepsy, Seizure Prediction, Neurological Disorder, Electroencephalogram (EEG)

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Creative Commons Attribution 4.0 and The Open Definition