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An Effective Pre-Owned Car Price Prediction Based on Multi Linear Regression Technique 1

An Effective Pre-Owned Car Price Prediction Based on Multi L...

引用

1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Kumar, Aman Singh, Dheeraj Chauhan, Keshav Guru Prasad, M.S. Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350341737

The market for the purchase and sale of second- hand automobiles has seen a trend towards expansion in recent years. Used vehicle buyers and sellers are able to conduct business at rates that are acceptable and fair thanks to scientific examination of automobile prices. This contributes to a reduction in the risks associated with conducting transactions in the used car market and encourages the market to continue to grow in a sustainable manner. Even though a lot of effort has been done on predicting the price of used cars, there are still a lot of obstacles standing in the way of an accurate and reliable estimate. Predictions of the prices of used automobiles that are accurate can assist individuals in making wise choices and avoiding the inflated costs of used vehicles on the market to the greatest extent possible. In this work multi linear regression technique is used, which is a type of supervised machine learning algorithm to build a model for predicting the price of pre-owned cars effectively. The dataset used for the prediction was collected from Kaggle webpage. After training and testing the model, the proposed model deployed using flask framework. The accuracy of the proposed model is 89.23% based on testing data. © 2023 IEEE.

关键词： Automobiles

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Image Recognition-Based Indoor Positioning System Using Federated Learning 100

Image Recognition-Based Indoor Positioning System Using Fede...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Yu, Heng-Shao Jhuang, Yu-Chiao Leu, Jenq-Shiou University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan

ISBN: (纸本)9798331517786

With the advancement of artificial intelligence (AI), indoor positioning systems have become increasingly important for various applications, leading to the development of diverse indoor positioning methods. Among various approaches, image-based indoor positioning methods have demonstrated relatively good positioning accuracy. However, maintaining user privacy continues to be a significant challenge. To tackle this issue, we propose a Federated Learning (FL) approach and develop an FL-based indoor image recognition positioning system. We conducted experiments to validate our method using two real-world datasets and compare the results to a Non-FL approach. Furthermore, we evaluated the accuracy of our method across various communication rounds, the number of client devices, and the amount of data per client device. Our experimental results indicate that the proposed method effectively preserves client privacy while achieving accuracy similar to the Non-FL approach. By employing FedAvg and FedOpt algorithms with the MobileNet model, our system ultimately reaches 94% accuracy. © 2024 IEEE.

关键词： Federated learning

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Comparative Design of Low Power Half Adder Using MGDIB and CMOS Techniques 3

Comparative Design of Low Power Half Adder Using MGDIB and C...

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3rd FORTEI-International Conference on electrical engineering, FORTEI-ICEE 2024

作者： Harahap, Robby Kurniawan Sukowati, Antonius Irianto Widyastuti Nur’ainingsih, Dyah Kristyawati, Desy Sekar Ciptaning Anindya, R.A. Dept. Electrical Engineering Gunadarma University Depok Indonesia Dept. Computer System Cendekia Abditama University Tangerang Indonesia

ISBN: (纸本)9798331542207

With the growing demand for portable electronic devices and the Internet of Things (IoT), optimizing power efficiency and speed in digital circuit design has become increasingly critical. Arithmetic components, such as half adders, are fundamental to many digital architectures. This research presents a comparative study between a low-power half adder designed using the Modified Gate Diffusion Input (MGDI) technique is a variation of the Gate Diffusion Input (GDI) method, which reduces power consumption and transistor count and an enhanced version with an isolation buffer, termed MGDIB, against a conventional CMOS-based half adder. Circuit designs were implemented using 0.35µm CMOS technology and simulated in LTspice. The performance metrics evaluated include transistor count, leakage current, static power dissipation, and delay. The MGDI half-adder requires only 13 transistors, significantly fewer than the 18 transistors needed by the CMOS design. Furthermore, the MGDIB configuration achieves a substantial reduction in leakage current (0.33 nA compared to 9.81 nA in CMOS) and a shorter delay (0.051 ns compared to 0.52 ns in CMOS). However, the MGDIB half adder exhibits slightly higher static power dissipation (0.11 nW compared to 3.23 nW in CMOS). These findings demonstrate that the MGDI and MGDIB techniques offer substantial improvements in transistor efficiency, operating speed, and power reduction, making them promising approaches for modern digital circuit design. © 2024 IEEE.

关键词： CMOS integrated circuits

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Prosecutorial Outcome Predication with LoRA and QLoRA 37

Prosecutorial Outcome Predication with LoRA and QLoRA

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37th Annual Conference on Legal Knowledge and Information Systems, JURIX 2024

作者： Chien, Kuo-Chun Chang, Chia-Hui Huang, Huai-Hsuan Kung, Jo-Chi Dept. of Computer Science & Information Engineering National Central University Taiwan

ISBN: (纸本)9781643685625

Our study on Legal Judgment Prediction (LJP) focuses on indictments, designing innovative tasks for prosecutors to predict reasons, imprisonment, fines, and penalty types. We investigated multi-task learning (MTL), Low-Rank Adaptation (LoRA), and its quantized version (QLoRA). Results show that the MTL framework, combined with LoRA and QLoRA, excels in prediction while optimizing resource use. Error analysis in the Reason field of TWPJD dataset was conducted to guide future predictions. Future work will focus on balancing sub-task performance and collecting more investigative data to enhance legal AI applications. © 2024 The Authors.

关键词： Multi-task learning

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SQL Injection Detection using Machine Learning and Convolutional Neural Networks 5

SQL Injection Detection using Machine Learning and Convoluti...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Misquitta, Jason Asha, S. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

SQL Injection Attacks are a concern to databasedriven online applications because they can compromise the security and integrity of information in databases. It gives hackers the potential to read, delete, modify or copy data. Exploiting the flaws in the validation of user inputs provided through login pages is the simplest technique to conduct this attack. Each login page a user accesses can help to reveal more about who they are. Feedbacks sent by the server while running a SQL command can provide details on weaknesses in the database server's validation procedure. This research paper analyses code that focuses on detecting SQL injections. Different supervised Machine Learning algorithms are used to evaluate the model. A CNN model is also created for real-time identification of whether an input is a potential attack or not. © 2023 IEEE.

关键词： Personal computing

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Hybrid Model of Fireworks Algorithm and Deep Learning for Drought Prediction using Satellite Data 9

Hybrid Model of Fireworks Algorithm and Deep Learning for Dr...

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9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Paudel, Ankit Bhavana, B.N. Anchalia, Aniketh Kakati, Anirudh Chaudhari, Shilpa Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334395

Many meta heuristic optimization techniques have been developed to address exceedingly difficult optimization issues in numerous disciplines. Fireworks Algorithm (FWA) was proposed as a Swarm Intelligence Optimization technique, mimicking fire work explosions. This paper proposes using FWA in the field of Agricultural Drought Assessment. Droughts are complex natural disasters that need efficient mitigation techniques. Latest research in existing drought assessment tools suggest utilizing Convolutional Neural Network (CNN) models, often hybridized with other optimization strategies. Considering the four vegetation indices Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI) and Soil Adjusted Vegetation Index (SAVI), a hybridized model that integrates FWA into CNN is proposed. This enhanced model can give up to 94% accuracy with very minimal loss for prediction of future drought. This model improves efficiency significantly. © 2023 IEEE.

关键词： Drought

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Unveiling Predictive Factors in Apple Quality: Leveraging LIME, SHAP, and the Synergy of Machine Learning Models and Artificial Neural Networks 6

Unveiling Predictive Factors in Apple Quality: Leveraging LI...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Chowdhury, Rituparna Das, Rudra Faruk Ananna, Fowzia Binta Saha, Arnob Nawar, Sadia Hosen, Md. Hamid East Delta University Dept. of Computer Science & Engineering Chittagong Bangladesh

ISBN: (纸本)9798350385779

In the realm of fruit quality assessment, the demand for accurate identification methods has grown significantly. Traditional measures, such as physical and chemical analyses, prove impractical due to their time-consuming nature and potential sample damage. To address this imperative need for precise and timely apple quality detection, this research explores the application of machine learning algorithms. Recognizing the drawbacks of traditional methods reliant on physical and chemical measures for predicting fruit maturity, non-destructive techniques, such as hyperspectral imaging and machine learning algorithms, are meticulously investigated. The study goes on to evaluate and compare the performance of various machine learning approaches, including Random Forest Classifier (RF), K-Nearest Neighbors Classifier (KNN), Support Vector Classifier (SVC), Extreme Gradient Boosting Classifier (XGB), Light Gradient Boosting Machine Classifier (LGBM), Artificial Neural Network (ANN), and Voting Classifier. Among these, the Artificial Neural Network emerges as the top performer, achieving an impressive accuracy rate of 92.87%. Following the thorough evaluation of the models, Explainable Artificial Intelligence (XAI) techniques, specifically LIME and SHAP, are employed. These techniques playa crucial role in interpreting and visualizing the intricate decision- making process of the models. The insights gained underscore the models' robustness while providing valuable information on the decision-making processes, contributing significantly to advancements in apple quality detection methodologies. © 2024 IEEE.

关键词： Lime

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Multi-Objective Task Scheduling in Cloud Environments using Dynamic Velocity-Adaptive Particle Swarm Optimization

Multi-Objective Task Scheduling in Cloud Environments using ...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Kumar, Ravi Vardhan, Manu Natinal Institute of Technology Dept. of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350373783

In the realm of cloud computing, tackling the multi-objective task scheduling problem means finding the best way to assign tasks to virtual machines, taking into account conflicting goals like minimizing execution time, load balance and cost. This intricate task demands smart algorithms to strike a balance between these competing objectives, ensuring the most efficient use of resources and meeting user expectations in the cloud. Different optimization techniques, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have been explored to handle this challenge and optimize task scheduling in the cloud environment. In classical PSO, particles can move at varying speeds in each iteration. In this study, we apply Velocity Stagnation Particle Swarm Optimization (VSPSO) variants where velocity stagnation applied during particles movement. This approach provides particles with a third movement option, enabling them to maintain the same velocity as in the previous iteration. VSPSO has a greater capacity to balance the exploration and exploitation. VSPSO adjusts the initial term of the PSO velocity equation to prevent premature convergence. Furthermore, the population of VSPSO is split into two swarms to ensure *** the inherent capabilities of PSO, VSPSO algorithm dynamically updates particle velocities to attain 5-6% total execution time 6-10% reduced cost and optimal balance load. Through meticulous comparisons with other algorithms, including Genetic Algorithm and Ant Colony Optimization, this study scrutinizes the effectiveness of our enhanced PSO approach. The overarching goal is to push the boundaries of multi-objective task scheduling in cloud computing by attaining minimal execution time, reduce cost and optimal balance load on each virtual machines. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Early Prediction of Diabetes using Several Machine Learning Algorithms 7

Early Prediction of Diabetes using Several Machine Learning ...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Rexie, J.A.M. Santhosh, P. Solomon, P. Nikhil Vishnu, P. Atul Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

One of the main issues diabetes poses to the medical profession globally is that its consequences are escalating swiftly. Elevated blood glucose levels cause diabetes, also referred to as diabetes mellitus or simply diabetes. On the basis of physical and chemical exams, a number of standard approaches can be used to diagnose diabetes. But, Doctors face a difficult task in predicting diabetes that affects the kidneys, eyes, heart, nerves, feet and other parts of the *** diagnosis analyses illness prognosis and diagnosis using a doctor's training and expertise, although this might be vulnerable to error. Machine learning and data science approaches have the potential to enrich other scientific disciplines by providing new perspectives on well-known issues. One such initiative is to assist in making predictions based on medical data. 'Machine learning' describes the processes by which computers tend to learn from experience, and is a recent area of data science. The aim of this research is to develop a system to accurately diagnose diabetes in patients at an early stage by comparing the results of several machine learning algorithms. Use four different supervised machine learning techniques: Random Forests RF, Logistic LR Regression,Decision DT Trees and (SVM) Support Vector Machines. The main objective of this research project is to develop a prognostic tool for early detection and prediction of *** model is also deployed into a web application using Python Flask, and the web application is built using HTML and CSS. Anyone can input features into the web application, and the model - which was previously developed using machine learning techniques - will then predict whether or not they will be diagnosed with diabetes. © 2023 IEEE.

关键词： Diagnosis

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Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Networks 16

Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Ne...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Tapadar, Karnish N A Khatua, Manas Tamarapalli, Venkatesh Indian Institute of Technology Guwahati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The 6TiSCH standard uses IEEE 802.15.4 Time Slotted Channel Hopping (TSCH) as a MAC layer protocol. TSCH cell scheduling determines packet transmission schedules in terms of timing and channel allocation. The Minimal Scheduling Function (MSF) and the 6P protocol facilitate schedule management dynamically. MSF involves a decision-making process to add/delete one cell at a time from the current schedule to adapt the schedule to modified traffic rate. The cell deletion is accomplished by randomly selecting one of the scheduled cells between two communicating nodes. However, random cell deletion can elevate end-to-end latency and energy consumption. Therefore, this paper proposes a Latency-aware Cell Deletion (LCD) mechanism to minimize associated average end-to-end latency and energy consumption. Simulations using the 6TiSCH simulator verified that in an 80-node network, compared to the standard MSF and another benchmark scheme IMSF, LCD achieved a substantial 44.40% and 42.39% reduction in end-to-end latency, respectively. © 2024 IEEE.

关键词： 6TiSCH Network Adaptive Link Scheduling Link Scheduling Minimal Scheduling Function

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