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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Ashok Kumar Anshita Shukla Vikas Thada Vikrant Chole Jhankar Moolchandani Santosh Sahu Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Gwalior Madhya Pradesh India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

This paper presents an extensive comparison of the IoT and blockchain technologies in the improvement of security, privacy, and interoperability, as well as energy efficiency in healthcare systems. This is because IoT allows for constant tracking and gathering of data from medical devices and this enhances the welfare of the patients as well as the management of the health facilities. However, this has exposed the Internet of Things (loT) technology with multiple risks in data security and privacy since it uses wireless communication and has limited computational power. Such issues can be addressed through the use of blockchain technology because of its distributed and tamper proof ledger. This research work presents a case of how the integration of IoT with blockchain will lead to a middle of the two worlds, where IoT will be useful for handling real time data while blockchain will handle the issue of security. The study concludes that the integration of blockchains with the IoT offers superior results than the individual implementations in several aspects, thus offering a more secure, efficient, and interoperable environment for today's healthcare applications. Possible future work includes improving the integration of these technologies to minimize the energy consumption, improve the scalability of the system and establish feasible implementation models in actual health care systems.

关键词： Measurement Privacy Energy consumption Scalability Medical services Energy efficiency Real-time systems Blockchains Security Interoperability

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Effect of Credit Financing on the Supply Chain for Imperfect Deteriorating Items with Carbon Emission 11

Effect of Credit Financing on the Supply Chain for Imperfect...

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

作者： Mittal, Mandeep Chauhan, Sudhir Kumar Rastogi, Shivangi School of Computer Science Engineering and Technology Bennett University Department of Mathematics Uttar Pradesh Greater Noida India Amity University Uttar Pradesh Aias Department of Mathematics Noida India Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

The sustainable inventory model is a framework designed to ensure manufacturing processes' sustainability while maintaining profitability. During manufacturing, some items may experience degradation, resulting in imperfect goods. Nevertheless, a screening process is implemented to eliminate such items before they reach the consumers. Therefore, it is critical to scrutinize items prone to deterioration thoroughly. Suppliers prioritize trade credit in payments when supplying retailers, necessitating the development of an inventory model that caters to the needs of retailers dealing with imperfect and deteriorating quality items while considering carbon emissions and trade credit as a payment. The study presents numerical illustrations and sensitivity analysis for a better understanding of the model. © 2024 IEEE.

关键词： Supply chains

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Predicting student Academic Performance using Machine Learning: Analyzing Socio-Economic and Personal Factors from Secondary Education in Portugal

Predicting Student Academic Performance using Machine Learni...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Santosh Sahu Ashok Kumar Rahul Kumar Anshita Shukla Vikrant Chole Jhankar Moolchandani Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Gwalior Madhya Pradesh India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

This paper aims to predict student academic performance using machine learning algorithms by analyzing socioeconomic and personal factors from secondary education in Portugal. The study utilizes a dataset containing demographics, family background, educational support, extra-curricular activities, and academic records to evaluate their impact on students' success in Mathematics and Portuguese courses. Machine learning models such as Logistic Regression, Decision Trees, and Random Forests were employed to analyze the data. The Random Forest model outperformed others with an accuracy of 85 %, indicating significant potential in predicting student performance using the selected features.

关键词： Analytical models Logistic regression Machine learning algorithms Soft sensors Education Psychology Predictive models Mathematical models Socioeconomics Random forests

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Machine Learning-Based Prediction of Gold Prices Using Economic Indicators

Machine Learning-Based Prediction of Gold Prices Using Econo...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Rahul Kumar Jhankar Moolchandani Anshita Shukla Santosh Sahu Vikas Thada Vikrant Chole Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Madhya Pradesh Gwalior India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

In furtherance to this, this research work focuses on the role of applying machine learning techniques in predicting the price of Gold using price data along with three macroeconomic variables. Thirteen models were tested in order to determine which of the three models: They also established that among the seven algorithms, namely linear regression, random forest regressor, and gradient boosting regressor, is the most useful in predicting gold price. Gradient Boosting Regressor was the optimized of all to tremendous accuracy with the help of the used algorithm that let it learn complex and non-linear features in the data inputs. They proved that the recent month and year price data, long term moving average and US Dollar Index (DXY) are the variables which affect the gold prices. The results confirm the necessity to include a broad set of economic variables in the model and the information can be reputable and valuable to investors and policy makers. Nevertheless, it could be proposed that future work would try to incorporate more features and even try to apply deep learning as an effort to improve the accuracy of the envisioned predictions.

关键词： Gold Accuracy Machine learning algorithms Biological system modeling Predictive models Prediction algorithms Boosting Market research Macroeconomics Random forests

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Comparative Analysis of Machine Learning Models for Diabetes Prediction

Comparative Analysis of Machine Learning Models for Diabetes...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Anshita Shukla Rahul Kumar Vikrant Chole Jhankar Moolchandani Santosh Sahu Ashok Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Gwalior Madhya Pradesh India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

This paper aims at comparing performance of various machine learning models for the task of predicting diabetes using one of the public datasets. The research assesses the big data analysis in the determination of the possibilities of risk of diabetes and the preferred models of Logistic Regression and KNearest Neighbors (KNN). Performance measures including accuracy, precision and recall were used in assessing the performance of the two models. The study do reveals the high level of potential of both the models yet reveals that Logistic Regression just edges a bit ahead of KNN in terms of accuracy and precision. These results show the potential of using machine learning in hospital diagnosis and specifically in diagnosis of Chronic diseases such as diabetics.

关键词： Logistic regression Analytical models Accuracy Hospitals Machine learning Predictive models Nearest neighbor methods Market research Data models Diabetes

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Analytical Approach Towards Cybersecurity Through AI-Enabled Threat Intelligence

Analytical Approach Towards Cybersecurity Through AI-Enabled...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Anurag Singh Kanishka Sanjay Kumar Dubey Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

A growing variety of cyber hazards are appearing as the world becomes more and more digital. The sophistication and rate of innovation of emerging threats puts organizations of all sizes at danger, and traditional cybersecurity solutions frequently fall short of keeping up. Threat intelligence powered by artificial intelligence, or AI-TI, offers a fresh and cutting-edge method for tackling cybersecurity issues. To find patterns and anomalies that could point to a cyber-attack, AI-TI solutions can examine massive amounts of data from a variety of sources, including as internal networks, security logs, and open threat intelligence feeds. Then, with the use of this information, security teams can be made aware of prospective dangers and assist in the creation of defense measures. Compared to conventional cybersecurity solutions, AI-TI systems have a number of benefits: Automation: AI-TI systems have the potential to automate a number of threat detection and response-related duties, freeing up security staff to concentrate on more strategic projects. Continuous improvement: AI-TI systems have the ability to continuously learn and get better with time, which enables them to stay on top of the most recent threats. Advanced threat detection: AI-TI systems can be used to identify and react to threats that are too sophisticated or subtle for traditional approaches to identify.

关键词： Technological innovation Information security Organizations Network security Market research Threat assessment Hazards

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Enhanced Brain Tumor Diagnosis Using Transfer Learning with Vision Transformers and Topological Data Analysis

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Procedia computer science 2025年 258卷 4048-4059页

作者： Dhananjay Joshi Bhupesh Kumar Singh Kapil Kumar Nagwanshi Nitin S. Choubey Research Scholar at Amity School of Engineering and Technology Amity University Rajasthan Jaipur 303007 India Amity School of Engineering and Technology Amity University Rajasthan Jaipur 303007 India Computer Science & Engineering Guru Ghasidas Vishwavidyalaya (A Central University) Bilaspur India Department of Computer Science Mukesh Patel School of Technology Management & Engineering Shirpur SVKM’s Narsee Monjee Institute of Management Studies (NMIMS) Deemed-To-University Mumbai-400056 India

The development of deep learning in brain tumor diagnosis has evolved due to the demand for effective medical image assessment methods. Early attempts utilizing basic machine learning algorithms and convolutional neural networks (CNNs) faced challenges in accuracy, precision, and stability across various datasets. This paper presents the TransTopoDx system, a novel deep learning architecture designed to enhance brain tumor diagnosis by integrating large datasets with topological data analysis (TDA) and vision transformers (ViTs). ViTs, pre-trained with transfer learning on extensive image databases and fine-tuned on specific brain tumor datasets, serve as feature extractors, significantly improving the model’s capacity to discern tumor characteristics. Additionally, TDA is employed using the Mapper algorithm to identify patterns in imaging data and facilitate classification. The feature extraction process involves two parallel paths for ViTs and TDA, with results merged in a fusion layer to enhance tumor prediction accuracy. To optimize weight tuning and mitigate overfitting, dropout techniques and data augmentation is used. The TransTopoDx system, implemented in Python, achieves an impressive overall accuracy of 99.92%, with a Precision of 99.90% and Recall of 99.87%. The incorporation of transfer learning in ViTs training and TDA is crucial for improving diagnostic efficacy.

关键词： Advanced Deep Learning Regularization Figshare Dataset

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Analyzing the Impact of AI Adoption on Job Market Dynamics and Employment Trends Using a Synthetic Dataset

Analyzing the Impact of AI Adoption on Job Market Dynamics a...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Vikrant Chole Santosh Sahu Jhankar Moolchandani Ashok Kumar Rahul Kumar Anshita Shukla Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Madhya Pradesh Gwalior India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

This paper aims at establishing the effects of adopting artificial intelligence in the job market based on an artificially generated set of 500 jobs. In the following analysis, the impacts of AI Adoption levels on salary distributions, job growth, as well as the risks of job automation are examined with the help of such statistical methods as multiple linear regression, logistic regression, classification algorithms. The results show a positive relationship between AI utilization and job creation in industries where AI augments human work but reveal higher automation threats for industries with repetitive activities. Also, AI adoption corresponds to higher wages particularly in the advanced skill jobs but the jobs at high automation risk enjoy wage premium due to job insecurity. This paper acknowledges the fact that AI adoption is both a benefit and a threat that calls for careful executive actions on best strategies for managing changes in the workforce. Such future work should replicate these effects using actual data as well as religious and ethical effects on a wider scale.

关键词： Industries Logistic regression Ethics Automation Statistical analysis Linear regression Market research Remuneration Artificial intelligence Synthetic data

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Demosaicing of Multispectral Imaging Camera data using Neural Networks

Demosaicing of Multispectral Imaging Camera data using Neura...

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BIT Conference (BITCON), International

作者： Sumita Gupta Sapna Gambhir Saru Dhir Simran Sapui Rana Majumdar Deepsikha Naskar Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP Department of Computer Science College of Engineering and Computing George Mason University USA Dept. of Information Technology Amity School of Engineering & Technology Amity University UP India Department of Computer Science Sister Nivedita University

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

In most digital cameras, the sensor uses a Bayer filter array to capture the image. This array records only one colour, either blue or green or red for every image pixel, resulting in a mosaic image. To retrieve the missing colour information in the captured image making use of cross-channel interpolation is referred to as demosaicing. Demosaicing, especially in the context of colour demosaicing (CDM), plays a vital role as the first step in obtaining high-quality colour images with single-chip cameras. Traditional demosaicing methods, used to reconstruct colour images from raw sensor data in digital cameras, have several drawbacks. They often result in a loss of spatial resolution, introduce artifacts, lack robustness in challenging conditions, and may require manual tuning for optimal performance. In contrast, neural networks offer advantages like end-to-end learning, improved image quality, robustness, flexibility, and state-of-the-art performance. They learn complex mappings directly from raw data, adapt to various conditions, and produce visually pleasing, high-resolution images. However, they require substantial training data and computational resources, making them less suitable for resource-constrained applications. In this paper, conventional interpolation methods are compared to deep-learning based approaches for image demosaicing. To validate the approach to image demosaicing, aerial images captured from the Mars Colour Camera have been used.

关键词： Image quality Interpolation Visualization Image color analysis Neural networks Training data Robustness Digital cameras Spatial resolution Tuning

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Deep Learning Model for Weather Prediction

Deep Learning Model for Weather Prediction

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Computing, sciences and Communications (ICCSC), International Conference on

作者： Shubhanshi Singh Neha Tyagi dept. Computer Science and Engineering Amity School of Engineering and Technology Noida India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Weather forecasting predicts atmospheric conditions at a particular time and place. It is an important task with a wide range of applications, including agriculture, transportation, and disaster prevention. Traditional weather forecasting methods rely on physical models of the atmosphere, which can be complex and computationally expensive. In recent years, deep learning techniques are effective for weather forecasting. Deep learning models can learn complex patterns from data, which can be used to predict future weather conditions accurately. This project aims to create a deep-learning model for forecasting weather. A huge dataset of historical meteorological data, including radar, satellite, and surface measurements, will be used to train the model. Numerous meteorological factors, such as temperature, precipitation, and wind speed, will be predicted by the model. We will compare the deep learning model's performance with conventional weather forecasting techniques. Creating a deep learning model that is more accurate and effective than current approaches is the project's main objective. The initiative may result in improved weather forecasts that are prompt and accurate, better agricultural, transportation, and disaster prevention decision-making, and less money lost because of weather-related incidents. Possible obstacles for the project are gathering and purifying a sizable dataset of meteorological data, using this data to train a deep learning model, and then comparing the model's output with more conventional techniques.

关键词： Deep learning Accuracy Computational modeling Atmospheric modeling Weather forecasting Transportation Predictive models Data models Agriculture Meteorology

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