Volume 11 | Issue 9 |

 Abstract

The academic achievement of students is a fundamental concern for educators, parents, policymakers, and researchers alike. Achieving excellence in education not only benefits individuals but also contributes to societal progress. In this pursuit of academic success, various factors have been identified as crucial determinants, ranging from teaching methodologies to learning environments. Among these factors, the role of self-concept in shaping academic achievement has gained considerable attention in recent years. A positive self-concept encourages resilience, adaptability, and a growth mindset, nurturing individuals who are better equipped to face challenges both inside and outside the classroom. Parents play a pivotal role in shaping a child's self-concept. The study's findings can provide parents with valuable insights into how to nurture self-confidence, support their children's learning journeys, and contribute to their overall well-being. In this study, thematic analysis serves as the chosen methodology to delve into the nuances of how self-concept acts as a determinant of academic achievement. The methodology involves identifying, analyzing, and interpreting recurring patterns or themes within qualitative data. By employing strategies that nurture self-esteem, resilience, and a growth mindset, educators can create classroom environments where students feel empowered to learn and excel. Similarly, parents' unwavering support, belief in their children's potential, and provision of nurturing environments are instrumental in fostering positive self-concepts that translate into academic success.

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 Keywords

Self Concept, Academic Achievement, Parents, Teachers, Self-Efficacy

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Block chain technology is an advanced database mechanism that allows transparent information sharing within a business network. It stores data in block that are linked together in a chain. It most safe,secure and fast. Our project is to create web application to the people who lodge a complaints to the government regarding common problems. The complaint where move to the respective departments and the problem which can be solved were hold and report back to the government. The government will allocate the tender to the contractors the satisfying coded amount is chosen then transactions starts more secure using block chain technology.

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 Keywords

A SECURE FRAME WORK FOR GOVERNMENT TENDER ALLOCATION USING BLOCK CHAIN

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 Abstract

In recent years, cyber security incidents have occurred frequently. In most of these incidents, attackers have used different type of spam email as a knock-on to successfully invade government systems, well-known companies, and websites of politicians and social organizations in many countries. The detection of spam mail from big email data has been paid public attention. However, the camouflage technology of spam mail is becoming more and more complex, and the existing detection methods are unable to confront with theincreasingly complex deception methods and the growing number of email. In this project, we proposed to design a novel efficient approach named Spam Spoiler for big e-mail data classification into four different classes: Normal, Fraudulent, Harassment, and Suspicious E-mails by using LSTM based GRU. The new method includes two important stages, sample expansion stage and testing stage under sufficient samples. This project The LSTM based GRU efficiently captures meaningful information from E-mails that can be used for forensic analysis as evidence. Experimental results revealed that Spam Spoiler performed better than existing.ML algorithms and achieved a classification accuracy of 98% using the novel technique of LSTM with recurrent gradient units. As different types of topics are discussed in E-mail content analysis. Spam Spoiler effectively outperforms existing methods while keeping the classification process robust and reliable.

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 Keywords

AI BASED SPAM SPOILER FOR PUBLIC AND PRIVATE E-MAIL SERVICES

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Robots can play an important role in recovery operations that present considerable dangers for human rescue teams, such as after chemical explosion and fire explosion. However, the robots currently used are limitedin their ability to interact and cooperate with humans byusing three sensors. This paper introduces a framework for the rescue robot development and control. Humans can be used for rescuing people in these areas, but due tohigh risk of building collapses it is not possible to send human rescue teams in these areas. Thus affordable high technology equipment which makes this risky job quicker and safer is needed for the hour, which has beendescribed in this paper.

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 Keywords

Arduino Uno Microcontroller, Zigbee, Temperature sensor, gas sensor, MEMS sensor, buzzer, Robot mechanism, ESP32 Modulo (Board)

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Contact tracing is a critical tool in the fight against infectious diseases, including the ongoing COVID-19 pandemic. Traditional contact tracing methods involve manually identifying and notifying individuals who have come into contact with an infected person. However, with the increasing scale of outbreaks, manual contact tracing has become increasingly difficult and time-consuming. Machine learning can help automate and enhance the contact tracing process. In this context, ML algorithms can analyze large volumes of data to identify potential transmission chains, predict the likelihood of an individual being infected, and prioritize high-risk individuals for testing and isolation. This paper presents an overview of recent research on contact tracing using machine learning. The paper covers ML techniques and their applications that involve Clustering and DBSCAN Algorithms and Proximity Graph. Additionally, the paper discusses the challenges and ethical considerations associated with using ML in contact tracing, such as data privacy and bias. Overall, this paper highlights the potential of ML to improve the effectiveness and efficiency of contact tracing, ultimately helping to curb the spread of infectious diseases.

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 Keywords

contact tracing, outbreaks, transmission chains, isolation, clustering and db scan algorithm

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In recent year, there has been rapid increase in the number of deaf-mute victims due to birth defects, accidents and oral diseases. Since deaf-mute people cannot communicate easily with normal people so they have to depend on some sort of visual communication. This system is based on a skin-color modelling technique so the skin color range is predetermined that will extract pixels(hand) from non- pixels(background). The images were fed into the model called the Convolutional Neural Network (CNN) for classification of images. Keras was used for training of images provided with proper lighting condition and a uniform background; the system acquired an average testing accuracy of 93.67%, of which 90.04% was attributed to ASL alphabet recognition and 97.52% for static word recognition, thus surpassing that of other related studies. The framework is developed by using python flask for a Web Based Application to establish the connection between deaf-mute and normal users. So that the Sign Language can be converted into Normal text or voice as output for the normal people. on the other end voice of the normal people is converted into text for the convenient of the deaf-mute people

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 Keywords

DEVELOPING A GESTURE BASED VIDEO CALLING FOR DEAF AND MUTE PEOPLE USING MICROSOFT KINCET

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Driver support system is one of the most important features of the modern vehicles to ensure driver safety and decrease vehicle accident on roads. Apparently, the road lane detection or road boundaries detection is the complex and most challenging tasks. It is including the localization of the road and the determination of the relative position between vehicle and road. A vision system using onboard camera looking outwards from the windshield is presented in this paper. The system acquires the front view using a camera mounted on the vehicle and detects the lanes by applying few processes. The lanes are extracted using Hough transform through a pair of hyperbolas which are fitted to the edges of the lanes. The proposed lane detection system can be applied on both painted and unpainted roads as well as curved and straight road in different weather conditions. The proposed system does not require any extra information such as lane width, time to lane crossing and offset between the center of the lanes. In addition, camera calibration and coordinate transformation are also not required. The system was investigated under various situations of changing illumination, and shadows effects in various road types without speed limits. The system has demonstrated a robust performance for detecting the road lanes under different conditions.

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 Keywords

Image processing, Hough Transform, Python, Deep learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Vascular-based biometrics is increasing in importance due to their accuracy and security. Finger vein- basedbiometric systems elegantly solve the problems associated with fingerprint systems. The vein-based authentication system is a promising biometric model for personal identification in terms ofits security and ease of use. Vein patterns can only be taken from a living body. Therefore, it is natural and conclusive evidence that a subject whose veins have been successfully struck is alive. The finger vein detection technique was improved using a neighborhood elimination technique to reduce repetitive features in the removed finger vein-specific image. The neighborhood elimination technique is used to remove redundant data while retaining effective raw data for subsequent processing.

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 Keywords

Finger vein image authentication, deep learning, Convolutional neural network, Vein- based authentication

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In supervised learning, missing values usually appear in the training set. The missing values in a dataset maygenerate bias, affecting the quality of the supervised learning process or the performance of classification algorithms. These imply that a reliable method for dealing with missing values isnecessary. In this project, we analyze the difference betweeniterative imputation of missing values and single imputation inreal-world applications. We propose an iterative imputationmethod, in which each missing attribute-value is iterativelyfilled using a predictor constructed from the known values and predicted values of the missing attribute-values from the previous iterations. Meanwhile, we demonstrate that it is reasonable to consider the imputation ordering for patching upmultiple missing attribute values, and therefore introduce amethod for imputation ordering. We experimentally show that our approach significantly out performs some standard machine learning methods for handling missing values in classification tasks.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

HANDLING MISSING DATA TO IMPROVE GENERALIZATIONPERFORMANCEOF MACHINELEARNING CLASSIFIER

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project is an IoT-based solution designed to replace traditional ID cards for students and faculty members in educational institutions .The system will utilize sensors and microcontrollers to capture and transmit each user's unique identifier, such as a biometric signature or NFC tag, RFID to a cloud-based platform.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

NFC-Near Field Communication, RFID-Radio Frequency identification, IoT- Internet of Things.

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Creative Commons Attribution 4.0 and The Open Definition