Volume 12 | Issue 4 |

 Abstract

Abstract: Crop yield prediction is crucial for agricultural decision-making, impacting import-export dynamics, pricing strategies, and crop management practices. Soil quality, determined by nutrients and pH levels, significantly influences crop productivity. Despite advancements, farmers often rely on traditional soil analysis methods. Emerging technologies like Data Mining, AI, Machine Learning, Deep Learning, and Predictive Analytics offer promising avenues for agricultural improvement. Predictive analytics, leveraging historical data, aids in predicting soil nutrient levels for crops like Paddy, Raagi, and Cumbu, enabling proactive measures to optimize yields and mitigate losses. This study proposes a method employing Machine Learning with LSTM to predict crop yields based on geographical and climatic data, capturing temporal dependencies and operating on diverse datasets. Additionally, the system facilitates real-time monitoring of soil nutrient evolution, integrating key chemical elements, rainfall, and temperature data.

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Keywords: Support Vector Machines (SVM), Long Short-Term Memory (LSTM)

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Due to their network accessibility and frequent weaknesses, web applications are common targets for cyber-attacks. When a hacking attempt is discovered, a system for intrusion detection monitors online applications and sends out alerts. Intrusion detection systems that are now in use often take features from input strings or network packets that are carefully chosen to be pertinent to attack analysis. However, picking characteristics manually takes a lot of time and requires in-depth understanding of the security area. Furthermore, in order for supervised learning algorithms to differentiate between normal and aberrant behaviours, they require a significant amount of labelled legitimate or attack request data, which is frequently prohibitively expensive for use in production web services. This work adds three new insights to the field on autonomic intrusion detection systems research. Using the Robust Software Modelling Tool (RSMT), which autonomously monitors and characterises the runtime behaviour of web applications, as a basis, we first assess the viability of an unsupervised/semi-supervised strategy for web attack detection. In the second section, we walk through how RSMT trains a stacked blurring auto encoder to encode as well as reconstruct a call graph for end-to-end deep learning. The reconstruction error of the request data is computed to identify anomalies using a low-dimensional representation of the feature set with unlabelled sample data. The findings of our empirical testing of RSMT on purposefully vulnerable production applications as well as artificial datasets are examined in our third section. According to our findings, the suggested method requires little domain expertise and a little amount of labelled training data in order to effectively and accurately detect attacks such as SQL injection, cross-site scripting, and deserialization.

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Deep Learning, RSTM , SQL

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 Abstract

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Machine learning, Opinion Mining, Text Mining

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Information and Technology has grown up to apex level in the 21st Century especially after 2010. Thus, learning has become easier and interesting for the students. Learners are exploring learning materials through various mediums such as: internet, social media, learning platforms and many more. As per the changing needs of the learners, the teachers are to be trained and well-acquainted with the use of new technologies. TPACK stands for Technological Pedagogical Content Knowledge. It is a framework for the integration of Technological knowledge with the Pedagogical and Content knowledge of a teacher in order to impart effective knowledge to the students. This study is basically a descriptive survey conducted to find the level of Techno-Pedagogical Competence among the Teacher-Educators with reference to their streams of education. This study was delimited to the Teacher-Educators of Teacher-education institutions of Odisha only. The objective was to study the Techno-Pedagogical Competency of Teacher-Educators with reference to their stream. The Null Hypothesis formulated for the study stated that, there was no significant difference of mean scores of Techno-pedagogical competency of Teacher Educators with references to their stream. The investigator had selected Disproportionate Stratified Random Sampling Technique to acquire the samples out of the total population. The investigator had selected 50 Teacher-Educators particularly 28 from Arts and 22 from Science of different Teacher Education Institutes as sample. The data were collected through a self-made Techno-Pedagogical Competency Scale and sent to the Teacher-Educators of Teacher-Education Institutes of Odisha through Google form. The data were analyzed using t-test for comparing two sample mean scores between science and arts stream. The finding of the study revealed that, the calculated t value was 1.36 and the table t value was 2.01 where df was 48 and level of significance was 0.05. The finding of the study states, there was no significant difference between science and arts Teacher-Educators with reference to their Techno-Pedagogical competencies.

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Technology, Pedagogy, Techno-Pedagogy, Competence, Techno-Pedagogical Competency, Teacher Educator

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The microscopic inspection of blood smears provides diagnostic information concerning patients' health status. They use a microscope and count the percentage of the occurrence of each type of cell counted within an area of interest in smears. Obviously, this manual counting process is very tedious and slow. In addition, the cell classification and counting accuracy may depend on the capabilities and experiences of the operators. Therefore, the necessity of an automated differential counting system becomes inevitable. In this project, CNN models are used. In order to achieve good performance from deep learning methods, the network needs to be trained with large amounts of data during the training phase. We take the images of the white blood cells for the training phase and train our model on them. With this method we achieved good accuracy than traditional methods. And we can generate the results with in the seconds also. white blood cells account for only about 1% of your blood, but their impact is big. White blood cells are also called leukocytes. They protect you against illness and disease .Think of white blood cells as your immunity cells. In a sense, they are always at war. They flow through your bloodstream to fight viruses, bacteria, and other foreign invaders that threaten your health When your body is in distress and a particular area is under attack.

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To demonstrate about this project, CNN models are used. In order to achieve good performance from deep learning methods, the network needs to be trained with large amounts of data during the training phase. We take the images of the white blood cells for the training phase and train our model on them. With this method we achieved good accuracy than traditional methods. And we can generate the results with in the seconds also.

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This is a study of Ride sharing applications. Ride-sharing platforms have changed urban transportation by offering a convenient and eco-friendly alternative to taxis and public transits. These platforms use mobile apps and algorithms to connect riders with drivers, reducing traffic congestion. Ride-sharing also fosters social interaction and improves accessibility for people with disabilities. However, there are safety concerns and potential negative impacts on traditional public transits which is a challenge. Overall, ride-sharing platforms offer a promising solution for urban mobility, with continuous innovation addressing current limitations.

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Urban Transit, Ride-Sharing, Mobile App, Ride-Sharing Safety, Urban Traffic Congestion

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 Abstract

paper uploaded

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small scale industries, economic development, employment, production and export

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An automatic fish aquarium is a system that utilizes technology to control and maintain the environment within a fish tank without the need for constant manual intervention. This system typically includes features such as automated feeding, water filtration and circulation, temperature regulation, and lighting control. By automating these essential tasks, the aquarium owner can ensure that their fish are provided with optimal living conditions and reduce the time and effort required for maintenance.

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Automatic Fish Aquarium

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``In the beginning, cyber shopping was really about getting commodity products online as cheaply as possible.Now,we're moving into the more exciting phase of e-commerce, where it's about emotional products the things people really cherish''. -Jason Goldber, Founder and CEO of Fab.com This study highlights on the consumers opinion towards cyber shopping with special reference to Theni town. The total number of Respondents is 150. The shift to cyber shopping is a result of some specific advantages that cyber shopping offers over traditional shopping from a customer's perspective. Today multichannel brand presence is a basic necessity and marketing efforts need to be optimized for breaking the clutter and grabbing the customer's attention. The homogenous amount of user generated content available digital channels and social media has made it easy for consumers to access reviews, ratings and recommendations before buying products. Consumers are empowered to research about the products, brands and companies.

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Cyber shopping, Products, Consumer's opinion.

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Artificial Intelligence (AI) in education is like having a helpful digital friend that makes learning more fun and personalized. It's about using smart technology to make studying easier. AI in education helps computers understand how you learn and what you need help with. It's like a clever assistant that adapts to your unique style of learning. To Evaluate the Student's utilization of AI tools used for Education purpose among Students. The sample size taken for this study is 270 respondents. This study aims to examining how the students gets benefited in the usage of the new AI tools, and explore the ways to enhance its accessibility and relevance to the student's academic journey. The main objective of the study is to examine how the students gets benefited by using the AI tools, in Coimbatore city has been taken for the current research work. For this study we have adopted the Convenience sampling. The analysis involved simple percentage analysis, rank analysis, chi-square test, and one-way ANOVA. Therefore, it can be concluded that the majority of the students are not updated with the knowledge of new technologies in AI tools for educational sector. So, they have to know about the tools which is available for education and the students can improve their skills and they can achieve their future goals.

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 Keywords

Artificial intelligence, AI tools, Education, Students

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