Journal of Scientific Research and Technology

Large-Scale Antiquity Sites: Detailed 3D Reconstruction Using Holistic Methods

2024-06-10T16:29:53+00:00

Tracking an object's current condition is frequently the first and most important step in cultural heritage preservation. It takes a lot of time and effort to complete this operation, especially for large-scale items like buildings. As a result, interest in new, more effective strategies that simplify the process and lessen the financial impact of surveying actions is developing. Before the actual restoration of the façade can begin, professionals must map the damages and determine the necessary remedial actions. Here, the foundation is provided by three-dimensional drawings that show each individual stone. These plans typically come from traditional surveying. To minimise the work on site, a photogrammetric technique is frequently utilised. However, manual, timely processing is used to handle still images, including point measurements and picture registration. Incorporating structure-from-motion, dense image matching, point cloud registration, laser scans, terrestrial imaging, and photos from a UAV platform, created orthographic projections from which the drawings could be created, we presented Taj Mahal here.

Analysis Of Personal Relationships From A Sentimental Standpoint And Support For Mental Health

2024-06-10T16:33:45+00:00

Mental complaint, which is primarily brought on by depressions, and commending causes of early death moment. Suicidal studies brought on by depression seriously hamper daily performing. Sentiment analysis, which aims to discover the character of textbook and categorizes into positive, negative, and neutral, is a trendy issue that has been on exploration for decades. In moment’s digital terrain, sentiment analysis has access to a lot of data. So, our thing is to concentrate on developing a system for textbook, videotape, and audio analysis- grounded depression identification. This system will be created using sentiment analysis and natural language processing ways. Depending on the feelings deduced from stoner input, the application will classify textbook, audio, and visual signals as positive or negative.

IOT Based Women Safety Night Patrolling Robot

2024-06-13T18:13:37+00:00

The paper presents the idea that revolves around women’s safety, Women have mostly become a victim of Workplace and public place harassment which in turn are leading them to quit their passion and dream jobs. Women safety night patrolling robot is Engineered with an intention to solve this exact problem. With obstacle sensing abilities and night vision cameras embedded with GPS and Bluetooth module, makes it an effective yet easy to implement device which will address the issue in the most effective way. All its features are properly coordinated with the help of a microcontroller which will not only make it smart but also portable. By giving women a reliable shield, it helps them feel brave enough to take control of their own lives and follow their dreams without being stopped by harassment. Basically, it marks a new time where women everywhere feel included and safe. Furthermore, the paper presents experimental results and discussions, highlighting the system's capabilities in patrolling, sound detection, obstacle sensing, and live video streaming for real-time monitoring and response which addresses future enhancements, emphasizing the potential for further improvements in deploying a fully-fledged security robot with extended coverage and enhanced efficiency.

SVM-Based Innovative Assessment Of A Marker Of Inflammation For The Prognosis Of Mild Cognitive Impairment To Front Temporal Lobar Degeneration Dementia Disease

2024-06-23T03:17:50+00:00

Frontotemporal disorders (FTD), sometimes known as frontotemporal dementia, are instigated with damage to neurons in temporal and frontal regions of brain. There are a wide variety of symptoms which can appear, including unusual behavior, emotional concerns, communication challenges, career difficulties, & walking difficulties. Frontotemporal dementia (FTD) is one of the most frequent types of dementia, and its symptoms and neurobiological hallmarks overlap to some extent. The proper diagnosis and identification of disease indicators is essential for effective patient monitoring. Differentiating amongst Alzheimer's disease (AD) & FTD utilizing unsupervised as well as supervised machine learning in MRI scans of the brain. And also classifying the types of frontotemporal dementia.

Investigation of Personality Traits Using Data from Facebook, Tweet's and Handwriting Samples

2024-06-23T03:23:48+00:00

Personality characteristics are characterized as persistent habits of mind that have been related to a wide range of consequential choices and events. Personality qualities are correlated with one's level of happiness in relationships, career paths pursued, and other areas of life. As a result, there was surge in recent years in desire to create models that can anticipate personality characteristics in an automated fashion. With use of social media, anybody may establish a virtual identity & connect with others via the sharing of personal information and user-generated material. One kind of user behavior which socioeconomic and psychological factors often shape is how they present themselves. Furthermore, there has been lot of interest in the ability to anticipate a person's personality based upon their physiological signals, which may be analyzed in real time. Personality can be predicted considerably more accurately, which can be quite beneficial for society as a whole. And many of the researchers used the provided data for the methodical creation of health care, social marketing networks, and individually recognized advice. And after a brief discussion of the admiration of social networks like Twitter, Facebook, & Linked In, researchers were able to conduct their studies using the public data that was made available through these platforms as well as social behavior traits that could be used to predict personality traits for friends and followers. Online social media platforms networks offer increasing opportunities for knowledge discovery and the public's data in relation to their views. The social media networking content's structured information can be used to forecast the key and most significant personality traits. These days, people frequently communicate some of their ideas, facts, and emotions—and especially their personality traits—on social media platforms, which is how data is produced. Proposed the Myers Briggs Type Indicator (MBTI system) is a personality predicting type system that system that divides people into 16 components of personality types based on four axes: Extroversion(E) – Introversion(I) Thinking(T) – Feeling(F) using KNN and Logistic regression machine learning algorithms.

Advancements in Anomaly Detection Techniques in Network Traffic: The Role of Artificial Intelligence and Machine Learning

2024-06-25T19:43:18+00:00

Purpose: This paper examines the most recent techniques for identifying irregularities in network data, with an emphasis upon machine learning (ML) and artificial intelligence (AI). Understanding how these technologies improve anomaly detection and overall network security is the goal of the study.
Design/Methodology/Approach: A thorough examination of scholarly works, business analyses, and conference proceedings from the previous ten years was carried out. The study looks into supervised learning, unsupervised learning, and deep learning, among other AI and ML approaches. In order to evaluate these techniques' efficacy, advantages, and disadvantages in network anomaly detection, a comparative analysis was carried out.
Findings/Results: The analysis shows that the identification of anomalies in network traffic is greatly enhanced by the use of AI and ML approaches. Methods such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) have shown to be very successful in recognizing intricate patterns. Nonetheless, issues with data quality, computational complexity, and interpretability of models continue to exist.
Originality/Value: This paper offers a current assessment of machine learning and artificial intelligence applications in network anomaly detection, emphasizing emerging trends and areas for further study. For academics and practitioners looking to improve network security using sophisticated detection methods, it provides insightful information.