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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Kumar, Santosh Bisht, Jyoti Sarawgi, Tarun Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350381887

The World Wide Web is an international network of linked files and resources that may be viewed online. It consists of web pages, multimedia files, and apps connected by hyperlinks for a variety of uses, including e-commerce and communication. This paper explores the diminishing efficacy of conventional SEO techniques such as PageRank and HITS Algorithm in the rapidly evolving online environment. To address this challenge, the research investigates the application of natural algorithms, including Grey Wolf Optimization (GWO), Ant Colony Optimization (ACO), and the Best-Fit Search Algorithm, to optimize web page ranking. The study focuses on the utilization of these adaptable algorithms to adaptively rearrange website content, enhancing user engagement and navigation. Experimentally it has been shown that the proposed GWO based algorithm for selecting top ranked web links achieves 0.98% of improvement in cost keeping runtime comparable with other algorithms. © 2024 IEEE.

关键词： Ant colony optimization

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Robust Hybrid Machine Learning Model for Financial Fraud Detection in Credit Card Transactions 2

Robust Hybrid Machine Learning Model for Financial Fraud Det...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Jahnavi, Dingari Mona, A. Pulata, Sandeep Sami, Sasank Vakamullu, Bharadwaj Mohan Bharathi, G. Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350327533

Financial fraud is a rising problem that affects both organizations and people, necessitating cutting-edge solutions to lessen its effects. The majority of machine learning models used now in the field of fraud detection include Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF). While these traditional models frequently lack sensitivity, have trouble figuring out complex fraud patterns, and are rigid in their ability to adjust to new threats. To overcome them, this research work provides a significant advancement by combining Decision Trees and Logistic Regression into a hybrid model, boasting an impressive accuracy of 98.1%. The proposed methodology not only improves the system's ability to detect fraud accurately but also makes it more flexible to the changing fraud strategies. Additionally, a thorough comparison analysis highlights the approach's undeniable benefits. In a nutshell, this work provides a method for detecting financial fraud using a hybrid model that expertly strikes a compromise between sensitivity and accuracy. In addition to improving fraud detection, this innovation also strengthens the security of financial systems. © 2024 IEEE.

关键词： Comparison analysis Contemporary world Financial fraud Flexibility Fraudulent transactions Hybrid model

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Evaluating Consistency and Reasoning Capabilities of Large Language Models 2

Evaluating Consistency and Reasoning Capabilities of Large L...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Saxena, Yash Chopra, Sarthak Tripathi, Arunendra Mani Galgotias University School Of Computing Science And Engineering Greater Noida India

ISBN: (纸本)9798350381665

Large Language Models (LLMs) are extensively used today across various sectors, including academia, research, business, and finance, for tasks such as text generation, summarization, and translation. Despite their widespread adoption, these models often produce incorrect and misleading information, exhibiting a tendency to hallucinate. This behavior can be attributed to several factors, with consistency and reasoning capabilities being significant contributors. LLMs frequently lack the ability to generate explanations and engage in coherent reasoning, leading to inaccurate responses. Moreover, they exhibit inconsistencies in their outputs. This paper aims to evaluate and compare the consistency and reasoning capabilities of both public and proprietary LLMs. The experiments utilize the Boolq dataset as the ground truth, comprising questions, answers, and corresponding explanations. Queries from the dataset are presented as prompts to the LLMs, and the generated responses are evaluated against the ground truth answers. Additionally, explanations are generated to assess the models' reasoning abilities. Consistency is evaluated by repeatedly presenting the same query to the models and observing for variations in their responses. For measuring reasoning capabilities, the generated explanations are compared to the ground truth explanations using metrics such as BERT, BLEU, and F-1 scores. The findings reveal that proprietary models generally outperform public models in terms of both consistency and reasoning capabilities. However, even when presented with basic general knowledge questions, none of the models achieved a score of 90% in both consistency and reasoning. This study underscores the direct correlation between consistency and reasoning abilities in LLMs and highlights the inherent reasoning challenges present in current language models. © 2024 IEEE.

关键词： Computational linguistics

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Enhanced Steel Defect Detection using U-Net Model with Attention Mechanism 5

Enhanced Steel Defect Detection using U-Net Model with Atten...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sai, D. Gowtham Sri Mani Maheswari, Uma Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

In this research, detection of steel defects is a significant use of computer vision that can enhance industrial quality control. New prospects for automated defect segmentation are presented by recent developments in deep learning. This paper investigated the use of attention processes in a U-Net convolutional neural network architecture for improved steel flaw identification. Images of steel and matching defect masks from the Severstal Steel Defect Detection dataset were used to train the U-Net network. Preprocessing actions such as image rotation and scaling were performed in addition to data augmentation. When tweaked at various probability thresholds, the model produced defect mask predictions on a test set with good accuracy. When compared to ground truth masks, qualitative results showed that different defect types could be segmented well. Forecasts on fresh test pictures without any masks provided also shown a believable ability to identify flaws. According to this research, deep learning using attention blocks and U-Nets has the potential to improve automation in tasks related to semantic segmentation, such as steel inspection. To assess the model's efficacy on other defect types and steel materials, more work is required. The code and trained model serve as a basis for creating systems that are ready for production. © 2024 IEEE.

关键词： Attention mechanisms Convolutional neural networks Image processing Steel defect detection U-Net

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Optimal aeration management strategy for a small-scale food waste composting

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Carbon Resources Conversion 2024年第1期7卷 79-88页

作者： Jia Chi Lai Yi Lung Then Siaw San Hwang Chung Sien Lee School of Engineering and Science Faculty of EngineeringComputing and ScienceSwinburne University of Technology93350 KuchingSarawakMalaysia Department of Electrical and Electronic Engineering Faculty of EngineeringUniversiti Malaysia Sarawak94300 Kota SamarahanSarawakMalaysia

Food waste is a significant contributor to greenhouse gas emissions when it ends up in *** turns out to be a sustainable solution to this problem,but it requires controlled and continuous airflow for optimal *** study focused on the effect of aeration rates and airflow directions on food waste composting using a closed system with forced *** was entered into the composting vessel in three directions,which were upward,downward,and a combination of both *** direction was run at aeration rates of 0.1,0.4,and 0.7 L/*** findings showed that the compost pile aerated at 0.4 L/min by using two-directional airflow can reach the thermophilic temperature within half of the *** compost pile achieved temperature of 40.94℃ after 10.5 *** the compost experienced slightly high in moisture loss(4.3%),the compost still attained the standard values for *** compost produced from food waste could be applied in soil to improve its fertility.

关键词： Small-scale composting Two-directional airflow Intermittent aeration Food waste

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Leveraging Explainable AI for Improved Understanding and Prediction of Drug Responses in IGF1R Signaling Pathways

Leveraging Explainable AI for Improved Understanding and Pre...

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2024 International Conference on Advances in Data engineering and Intelligent computing Systems, ADICS 2024

作者： Janiel, J. Rajendran, P. Selvi School Of Computing Hindustan Institute Of Technology And Science Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Pharmacogenomics showcases the aim of precision medicine, which strives to customize treatments for individuals and specific populations. This field delves into exploring how an individuals DNA influences their response to medications. A persons genetic composition can impact the likelihood of experiencing reactions or determining the effectiveness of a medication. By providing insights into the safety and effectiveness of drug therapies pharmacogenomics holds potential for significantly enhancing health outcomes. Through advancements in targeted therapies we can precisely target abnormalities that trigger tumor growth in patients. For instance IGF1R (Insulin like Growth Factor 1 Receptor) which belongs to the tyrosine kinase receptor family plays a crucial role in promoting cell growth, survival and proliferation across different types of cancers. The overexpression of IGF1R has been observed in cancer types indicating its involvement in fueling continuous growth and survival of cancer cells. Targeting IGF1R helps address the dysregulation of this receptor within cancer cells. Artificial Intelligence (AI) comes into play by enabling prediction of suitable drugs based on a patients genomic profile thereby reducing adverse effects and improving treatment effectiveness. Parallel, here has been growing concern regarding model explanation due, to the opaque nature of model predictions. This is particularly important when it comes to modeling drug responses. In our research paper we have employed AI to gain a clear understanding of the prediction model and the factors that affect its results. The findings show that lower valued counts of YAP-pS127-Caution protein tend to negatively impact the output. Similarly lower values of YAP-pS127-Caution protein and higher valued counts of YAP-pS127 -Caution protein, Xanthine, Tyrosine tends to positively impact the output. This helps as an aiding reference in knowing which feature of an unknown cell line should be focused to know

关键词： Proteins

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Accurate Estimation Technique Development for Second Hand Car Through Linear Regression Algorithm 5

Accurate Estimation Technique Development for Second Hand Ca...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Jain, Satwik Punia, Sanjeev Kumar Galgotias University School of Computing Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

Car production volumes have increased dramatically over the past decade, reaching 92 million vehicles produced by 2019. This increase has particularly boosted the used car market, emerging as a growing industry thanks to the internet so fashionable methods Need to stay current market trends for determining real value have increased in many applications of machine learning, its use in predicting problems stands out in the way in a remarkable way. This work focuses on the use of machine learning algorithms, in particular linear regression, to predict used car prices and to develop mathematical models using data with specific characteristics. In this period marked by rapid growth, our research is at the forefront of a new approach to car price forecasting. Our solution, using machine learning techniques, aims to provide comprehensive support, significantly saving time and costs associated with car pricing calculations. Forecasting the price of a car is a major one, requiring a great deal of expertise and thorough diligence. We thoroughly examined a variety of factors to make accurate and reliable predictions. We used Linear Regression, a leading machine learning technique, to develop a +model to predict used car prices. This method incorporates multiple independent variables and one dependent variable, so that actual and predicted values can be compared to assess forecast accuracy Our study uses a system with a forced dependent variable on the cost depending on factors such as mileage, year of production, and variety come other characteristics. © 2024 IEEE.

关键词： Data Preprocessing Feature engineering Kaggle Dataset Linear Regression Numpy Vehicle Price Prediction

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Bitcoin vs Gold: Techno Legal Aspects of Global Market Implications 18

Bitcoin vs Gold: Techno Legal Aspects of Global Market Impli...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Devi, Suman Kumar, Avadhesh Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9789380544519

A decade-old innovation, cryptographic currency facilitates sophisticated transactions over an encoded distributed network. Bitcoin, the initial and most widely recognized cryptocurrency, is driving the development of secure and immutable financial payment systems as a challenging innovation. Although digital currencies are unlikely to replace traditional government-issued money, they may have an impact on how global online marketplaces interact with one another by removing the constraints associated with traditional public currency forms and exchange rates. Gold is another commodity which greatly influences the world economy. This research aims to provide a study on hypothetical monetary differences between bitcoin and gold. This article also analyzes the legality of digital currencies and their susceptibility to future government regulations along with investment in Bitcoin and Gold. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Bitcoin Blockchain Cryptocurrency Digital Currency Gold Monetary

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Improving Facial Recognition: Evaluating CNNs for Detecting Manipulated Images 11

Improving Facial Recognition: Evaluating CNNs for Detecting ...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Bansal, Prakhar Shankar, Ajay Kumar, Pratiyush Vaid, Nandan Rajakumar, P. School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350350357

The ability to distinguish between real and fake imagery is extremely important in the field of computer vision, especially when it comes to facial recognition. In order to detect real faces from fakes, this study explores the creation and assessment of convolutional neural network (CNN) models. Our study compares the performance of well-known CNN architectures, such as VGG, ResNet, and EfficientNetB0, using the TensorFlow framework. After extensive testing and analysis, EfficientNetB0 stands out as the top performer, attaining a previously unheard-of 99% accuracy. This astounding outcome demonstrates the effectiveness of EfficientNetB0's sophisticated architecture, which is tuned for optimal performance in tasks requiring subtle distinction between real and fake facial images. © 2024 IEEE.

关键词： Image enhancement

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Deciphering Brain Tumor Diagnosis: A CNN-Based Deep Learning Approach 4

Deciphering Brain Tumor Diagnosis: A CNN-Based Deep Learning...

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4th IEEE International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Tiwari, Pradeep Kumar Johri, Prashant Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350360165

A brain tumor is defined as the rapid growth of abnormal cells in the brain. Magnetic resonance imaging (MRI) is the best way to diagnose these tumors. Using MRI images, doctors can carefully identify and define specific areas of soft tissue growth to determine the presence of brain tumors. Today, brain cancer diagnosis uses machine learning and deep learning algorithms along with advances in artificial intelligence. Using these algorithms has many benefits, such as increasing the brain's focus, reducing errors, and providing more accurate results. These benefits increase the efficiency of the decision-making process and make it easier for the patient to choose the most appropriate treatment options. This article introduces the use of convolutional neural networks (CNN) to detect brain tumors and evaluate their consequences. The main goal is to use neural networks, especially deep learning techniques, to identify brain tumors. From the results obtained in the training and testing phase, the pre-trained model achieved an accuracy of 0.9664 and a loss of 0.0934. Obtained are indicators for the classification of glioma, meningioma, non-neoplastic and pituitary tumors. Precision, recall, and F1 scores for glioma were 0.9781, 0.9477, and 0.9627, respectively. In the absence of tumor, precision, recall, and F1 scores were 0.9790, 0.9943, and 0.9866, respectively. The precision, sensitivity and harmonic mean values of the pituitary gland are 0.9589, 0.9951 and 0.9767, respectively. After analyzing the dataset, it is clear that convolutional neural networks (CNN) are the most reliable method for predicting the presence of brain tumors.s © 2024 IEEE.

关键词： Convolutional neural networks

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