Turkish Journal of Computer and Mathematics Education (TURCOMAT)
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RETRIEVAL-AUGMENTED GENERATION WITH SMALL LLMS FOR KNOWLEDGE-DRIVEN DECISION AUTOMATION IN ENTERPRISE SERVICE PLATFORMS
No full textEnterprise service platforms connect various knowledge artifacts and office applications in organizations to enable automation of routine decision-making. During this automation, service requests are expressed as domain-independent knowledge queries to capture gaps in knowledge related to governance, operations, risk management, customer service, and other enterprise aspects, and stored in a knowledge repository. Retrieval-augmented generation driven by small-scale pre-trained transformers offers an ideal means to automate responses to such queries because information retrieval and text-to-text generation can be achieved using state-of-the-art—if not better—large language models without incurring the high inference costs associated with their larger counterparts. A system architecture providing this functionality is presented, together with an exploration of the elements of the knowledge-retrieval phase. Empirical evaluation of the effectiveness of the retrieval step shows that it satisfies the requirements of a diverse set of queries.
Deployments of enterprise service platforms within organizations have shown that a significant proportion of service requests relate to knowledge gaps in domains such as governance, operations, risk management, customer service, and so on. Efforts to support automation of these decision-making tasks attempt to address such requests by posing knowledge-retrieval queries for the pertinent answers. Cross-domain databases, policy repositories, internal and external knowledge bases, and other such information collections serve as knowledge sources. To support these requests, retrieval-augmented generation leverages a combination of information retrieval and large language models
Investment Objective, Investment Portion and Risk Taking Capacity of the Respondents in Agricultural Commodity Market of Narmadapuram Division with Reference to Soyabean and Wheat Crop
No full textNarmadapuram division consists of Harda, Betul and Hoshangabad district and they are chosen for the present study as per stratified random sampling. The said districts were selected because Soyabean and Wheat crop are cultivated at large while comparing with other districts. The study pertaining about the commodity market covers the period of 3 years (2014-2016). In the present study the investment objectives, investment portion and risk taking capacity of the respondents in agricultural commodity market of Narmadapuram division
Taguchi Analysis of Pervious Concrete Mixtures: A Way to Increase Strength and Permeability
No full textA unique variety of concrete called pervious concrete is created by combining water, cement, and open-graded coarse particles. Usually, it contains very little to no fine aggregate concrete and only enough cement paste to coat the aggregate particles while preserving the interconnectedness of the spaces. The terms porous concrete, permeable concrete, no fines concrete, gap graded concrete, and improved porosity concrete are also used to describe pervious concrete. The experimental technique and findings for compressive strength, flexural strength, and permeability are presented in this work. Using Taguchi analysis, we were able to create an experiment with three variable factors—the mix proportion, the percentage of fine aggregates, and the percentage of human hairs as fibers—each with three levels. L9 arrays were utilized in this experiment. While varying the proportions of human hair as fibers of fine aggregate with coarse aggregates from 0.25%, 0.50%, and 0.75% of human hair of 0%, 5%, and 10% of fine aggregate accordingly in each proportion, the w/c ratio of 0.4 was used in this study. Whose findings show that the maximum compressive strength of M9 mix is between 1.45 and 3.48 N/mm2, the maximum flexural strength of M9 mix is between 0.135 and 2.11 N/mm2, and the maximum permeability of M8 mix is between 91.67 and 163.70 M/hr. That goes to show that adding more fibers helps to boost flexural strength, while adding more fine aggregates increases compressive strength at the same time
Lagrange formula conjugate third order differential equation
No full textThe paper considers a boundary value problem for a third order with no smooth coefficients and pure derivatives. Odds. This is due to the fact to introduce the concept of the conjugate Green\u27s function. It is very difficult to write the form of the conjugate differential operator corresponding to equation in the Lagrange sense. Therefore, in this work, without using strict conditions smoothness under the conditions and boundedness, an explicit form is found conjugate operator since the initial-boundary value problem for integral-differential equations has been studied based on the introduction special conjugate systems in the form of an integral-algebraic equations’ system. In this article, it can be said that Green\u27s function is considered based on Lagrange\u27s formula for the third-order differential equation with boundary conditions and its conjugate
IMPACT OF CLIMATE CHANGE ON ARCTIC FOX POPULATION DYNAMICS: A MATHEMATICAL MODELING APPROACH
No full textThis study focuses on the impact of climate change on Arctic fox populations using mathematical modeling. The research employs a basic Lotka-Volterra-style model to simulate the effects of temperature, precipitation, and snow cover on the Arctic fox population dynamics. The model is based on the assumption that the population growth rate is limited by the carrying capacity of the environment and is influenced by these environmental factors. The study provides insights into the complex relationship between environmental factors and population changes, highlighting the need for more sophisticated models to holistically understand the impact of climate change on ecosystems. The findings underscore the importance of mathematical models in guiding adaptive strategies for ecosystem management amidst changing climates, emphasizing the necessity for further research to comprehensively address climate-induced challenges and ensure a sustainable future for ecosystems and species.
 
DETECTION OF FRAUDULENT OR DECEPTIVE PHONE CALLS USING ARTIFICIAL INTELLIGENCE
No full textWith an increase advancement of technology, fraud phone calls, including spam’s and malicious calls have become a major concern in telecommunication industry and causes millions of global financial losses every year. Fraudulent phone calls or scams and spams via telephone or mobile phone have become a common threat to individuals and organizations. Artificial Intelligence (AI) and Machine Learning (ML) has emerged as powerful tools in detecting and analyzing fraud or malicious calls. This project presents an overview of AI-based fraud or spam detection and analysis techniques, along with its challenges and potential solutions. The novel fraud call detection approach is proposed that achieved high accuracy and precision. The Proposed approach was evaluated using a dataset of real-world fraudulent calls. And results demonstrate that the approach achieved high accuracy in detecting malicious calls and identifying potential indicators of frauds or spam’s. The analysis of fraud calls also provided insights into the tactics and methods employed by fraudsters, which can be used to develop countermeasures
AN EFFCIET FORCASTING MENTAL HEALTH CONDITION USING MACHINE LEARNING
No full textNowadays, people are becoming more and more concerned with their physical health, but mental health is not given the same level of attention. Even if they are aware that they have been afflicted by chronic mental illnesses, many people choose not to seek treatment out of fear of what others would think, a belief that they have lost their minds, a dislike of doctors, or all three. These circumstances make it urgently necessary to find a solution so that more individuals are not inclined to mental diseases. This paper focuses on forecasting mental health using deep learning approaches and machine learning algorithm that is support vector machine. Support vector machine is used to solve the existing problem, as many machine learning and deep learning techniques are helping to solve these contemporary difficulties. SVM gives more accuracy compared to other machine learning algorithms to predict the mental illness
A ROBUST DETECTION OF CYBER INCIDENTS UTILIZING MACHINE LEARNING TECHNIQUES
No full textA reliable Cyber Attack Detection Model (CADM) is a system that works as safeguard for the users of modern technological devices and assistant for the operators of networks. The research paper aims to develop a CADM for analyzing the network data patterns to classify cyber-attacks. CADM finds out attack wise detection accuracy using ensemble classification method. LASSO has been used to extract important features. It can work with large datasets, and it has more visualization capability. Gradient Boosting and Random Forest algorithms have been used for classification of network traffic data to build an ensemble method. Gradient Boosting algorithm trains weak learning models and select the best decision trees to deliver more improved prediction accuracy and Random Forest algorithm trains each tree in parallel manner. In this research work, Jive datasets such as NSL-KDD, KDD Cup 99, UNSWNB15, URL 2016 and CICIDS 2017 are also applied to check the efficiency of the proposed model
VLSI Implementation of Chest X-Ray Image Segmentation Using Hybrid Clustering
No full textMedical image segmentation plays a crucial role in various clinical applications, facilitating accurate diagnosis and treatment planning. In communication systems, particularly telemedicine and remote healthcare monitoring, real-time processing and transmission of medical images are essential for timely diagnosis and intervention. Leveraging VLSI technology for implementing Kmeans offers a promising solution to address the computational demands of image segmentation while meeting the stringent requirements of communication systems. Existing systems often rely on software-based implementations such as threshold segmentation, leading to significant computational overhead and latency, particularly in resource-constrained environments. Moreover, these implementations struggle to achieve real-time performance, hindering their practical utility in communication systems for healthcare. So, this work proposed VLSI-based approach aims to overcome these challenges by offloading the computational burden from the software to hardware, enabling parallel processing and efficient utilization of resources. By exploiting the inherent parallelism of the K-means algorithm and optimizing hardware architecture, our design ensures high throughput and low latency, making it suitable for real-time medical image segmentation in communication systems
COST-EFFECTIVE AND EFFICIENT BLOCKCHAIN FRAMEWORK FOR VERIFYING CERTIFICATE IN YEMENI UNIVERSITIES
No full textRecognizing the necessity to preserve the integrity and good name of degrees awarded by Yemeni universities. It should provide an efficient way of verifying certificates. The effectiveness of the conventional method of confirming the validity of certificates in reducing fraud has not been very strong. Therefore, it is necessary to stop this kind of fraud by using blockchain technology, which has several benefits, such as encryption, sharing of data, and the capacity to store information as permanent data that cannot be altered. Low latency and low cost will be available for the issuance, sharing, and verification of these certifications in universities if the suggested system is implemented. The paper presents the proposed framework, which uses smart contracts in the Ethereum blockchain and a distributed peer-to-peer network (filebase) for certification verification by a certificate’s hash immediately without waiting for the facility’s response. It also contains an estimate of the average cost of publishing a certificate. It also frees students from having to constantly carry paper copies of their documents by enabling them to access them via a certificate\u27s hash. Furthermore, there is no extra cost for the verification process, and it does not require an Ethereum network account