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Movie Recommendation System Using Euclidean Distance

Movie Recommendation System Using Euclidean Distance

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Muneeb Sami Khan Zunnurain Hussain Muhammad Irtaza Amaad Muhammad Zulkifl Hasan Aqsa Khalid Saad Hussain Chuhan Rimsha Awan Muhammad Atif Yaqub Farhan Ashraf Department of Computer Engineering Information Technology University Lahore Pakistan Dept. of Computer Science Bahria University Lahore Campus Faculty of Information Technology University of Central Punjab Department of Computer Science National College of Business Administration and Economics Lahore Pakistan

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

In this study, we pivot from the commonly used cosine similarity to the Euclidean distance as the foundation for our film recommendation algorithm. Initially, data pertaining to users, films, and ratings are loaded using the panda’s library. The algorithm then computes normalized ratings, tabulates the number of films in each genre, and visualizes this distribution. The frequency of ratings for each genre is similarly plotted. A dictionary is maintained to store movie IDs for each genre. These values are then integrated into the normalized rating matrix. For each genre, the normalized ratings of movies are cataloged in a lexicon. Using the normalized ratings matrix and movie IDs, a Data Frame is generated. Subsequently, the movie and ratings datasets are merged. A pivot table captures the number of films each user has watched per genre. The study also be happy with their purchase if these recommendations are tailored to their needs. As a result, the client would utilize this application once more. Because of the significant money gained determines the average number of films a user watches in each genre. This pivot table is refined to display movies in each genre that a user has watched, but only those exceeding the genre’s average viewership. Instead of cosine similarity, the Euclidean distance is computed to gauge the similarity between movies, offering a fresh perspective on recommendation systems.

关键词： Technological innovation Dictionaries Data visualization Euclidean distance Filtering algorithms Motion pictures Libraries Fourth Industrial Revolution Recommender systems

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PCB Based Modulated Scatter with Enhanced Modulation dept.

PCB Based Modulated Scatter with Enhanced Modulation Depth

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Crocker, Dylan A. Donnell, Kristen M. Amazon Web Services Inc. SeattleWA98109 United States Missouri University of Science and Technology Microwave Sensing Lab. Dept. of Electrical and Computer Engineering RollaMO65409 United States

ISBN: (纸本)9781665442282

The modulated scatterer technique (MST) has shown promise for applications in microwave imaging, electric field mapping, and materials characterization. Traditionally, MST scatterers are dipoles centrally loaded with an element capable of modulation (e.g., a PIN diode). By modulating the load element state, the scattered fields are also modulated. However, due to the small size of such scatterers, it can be difficult to reliably detect the response. Increasing the modulation dept. (MD) of the scattered signal may improve detectability. This paper presents simulations and measurements of PCB-based MST elements that, through reactive loading, are designed to be electrically invisible during the reverse bias state of the modulated element (a PIN diode in this case) while producing detectable scattering during the forward bias state. The results show a significant (> 90%) improvement in the MD of the scattered signal when compared to a traditional MST scatterer. © 2023 IEEE.

关键词： Polychlorinated biphenyls

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Highly Efficient Switched Capacitor Converters for Rechargeable Hearing Aids

Highly Efficient Switched Capacitor Converters for Rechargea...

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Communication, Control and Intelligent Systems (CCIS)

作者： V. Anandi Ramesh Muthuswamy A Pranav Dept. Of Electronics and Communication Ramaiah Institute of Technology Bengaluru India School of Management Presidency University Bengaluru India Dept. Of Computer Science Engineering VIT Chennai Campus Chennai India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

The paper deals with switched capacitor-based designs for power-efficient rechargeable hearing aids. Switched-capacitor DC-DC power converters are a subset of DC-DC power converters that use only switches and capacitors to convert one voltage to another efficiently. By using switched-capacitor circuits, the aim is to increase the efficiency of hearing aids. To achieve long battery life, a high conversion efficiency is required. At the same time, the physical size should be low as it is desirable for the end-user to have small hearing aids. The voltage levels thus have to be stepped down from the voltage provided by a Li-ion (rechargeable) to levels closer to that of Zn-Air (non-rechargeable).

关键词： Capacitors Switches DC-DC power converters Hearing aids Control systems Switched capacitor circuits Topology Batteries Intelligent systems

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Analysis on Quantum Cryptographic Approaches for Image Encryption

Analysis on Quantum Cryptographic Approaches for Image Encry...

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Intelligent Technologies (CONIT), International Conference on

作者： Shilpa M. Satre Bharti Joshi Dept. of Computer Engineering Dept. of Information Technology Ramrao Adik Institute of Technology Deemed to be University Bharati Vidyapeeth College of Engineering University of Mumbai Navi-Mumbai India Dept. of Computer Engineering Ramrao Adik Institute of Technology deemed to be University Navi-Mumbai India

To secure and protect the photos from unfair tactics, the encryption process is used. the advent of the digital era has made it exceedingly easy to hack any data kept on the cloud. Encryption is critical to ensuring communication security, which is a crucial issue. There are numerous photo encryption methods that mostly rely on chaotic logistic maps; however, they have downsides such a small key space and low security. Current analysis introduced experimental results of quantum chaotic cryptosystem,3D-Chaotic map and quantum chaotic logistic map. This paper we measure image encryption parameters are Entropy, structural similarity index measure, combined average changing intensity and net pixel change rate which defines utilization of suitable image encryption approaches. For the cypher and the original image, we quantify the correlation coefficient value in both the horizontal and vertical positions with comparison of all three existing Approach-es performance. The scientific and practical economic results reveal that the quantum chaotic cryptosystem has outstanding performance and in future with utilization of advanced Approaches we can resist various cryptographic attacks.

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Kidnapped Vehicle Using Particle Filters-Self-Driving Car Nanodegree

Kidnapped Vehicle Using Particle Filters-Self-Driving Car Na...

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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Sunil Dhull Payal Thakur Yashwant Singh Sangwan Navjot Singh Talwandi Rajender Kumar Surender Kumar Panipat Institute of Engineering & Technology Dept of Computer Science Engineering Chandigarh University Punjab India Department of Computer Science Govt. College Hisar Haryana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Guru Teg Bahadur College Bhawanigarh Sangrur Punjab India

ISBN: (数字)9798350306446

ISBN: (纸本)9798350306453

Detecting and tracking stolen vehicles is an important task with self-driving cars. This study focuses on the use of particle filters to detect and track stolen vehicles. Particle filters are a probabilistic estimation method that can effectively handle the uncertainties and nonlinearities associated with vehicle tracking. The study examines the application of particle filters in the Nanodegree project to self-driving cars, specifically for the detection and tracking of stolen vehicles. The results demonstrate the efficiency of particle filters to accurately estimate the location and routes of hijacked vehicles, enabling effective recovery operations Vehicle occupied scenarios refer to situations in which a self-driving vehicle loses its location awareness The vehicle can measure its position observations based on a sensor. Using small- scale filters, this research aims to improve the ability of self- driving vehicles to reconstruct reality, accurately restoring its position in cases where they are unpredictable, ultimately contributing to the robustness and reliability of autonomous vehicle systems.

关键词： Uncertainty Accuracy Tracking Estimation Particle filters Robustness Public security

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Code Smell Detection using Hybrid Machine Learning Algorithms

Code Smell Detection using Hybrid Machine Learning Algorithm...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： D Mahalakshmi Prabakaran Kasinathan D. Elangovan C. Rohith Bhat M Balamurugan S. Sivakumar Dept. of Information Technology Panimalar Engineering College Dept. of CSE School of Computing Veltech Rangarajan Dr. Sagunthula R & D Institute of Science and Technology Dept. of CSE Panimalar Engineering College Institute of computer science and engineering Saveetha School of engineering (SIMATS) Chennai Dept. of CSE Sri Sairam Engineering College Affiliated to Anna University Dept. of Computer Applications Lakshmi Bangaru Arts and Science College melmaruvathur

Code smells frequently leads to the discovery of decreased code quality, drains on application resources, or even critical security vulnerabilities embedded within the application's code. While code smells may not always indicate a particularly serious problem, it do often lead to the discovery of these issues. Software's structural characteristics lead to a design issue that makes it challenging to manage and maintain code refactoring. The goal of the current research is to create methods for identifying code smells. The machine learning algorithm is a reliable method for individualized smell detection, but there aren't many studies on how well it works for different developers. In this proposed work used two different deep learning algorithms and five different machine learning ensembles to detect suspicious code. Investigation of the Data class, God class, Feature-envy, and Long-method datasets revealed that each contained various levels of code smells. Although there is room for improvement, the outcomes of prior publications' applications of machine learning and stacking ensemble learning methods to this dataset were satisfactory. A class balancing method (SMOTE) was implemented to address the problem of class imbalance within the datasets. While the Feature- envy dataset with the selected dozen metrics produced the lowest accuracy (91.45%) for the Max voting method, the Long-method dataset with the various chosen metrics produced the highest accuracy (100%) for all five methods.

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Financial Viability of PV Grid for an Intelligent System of Residential Buildings in sub-urban Areas of Punjab, Pakistan

Financial Viability of PV Grid for an Intelligent System of ...

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Power Electronics and Control (SEPOC), Seminar on

作者： Moazzam Shehzad Nasir Jamal Hafiz Abdul Muqeet Noman Sohaib Qureshi Dept. of Electrical Engineering Lincoln University College Petaling Jaya Malaysia Dept. of Electrical Engineering Punjab Tianjin University of Technology Lahore Pakistan Dept. of Computer Science University of Engineering and Technology (RCET Campus) Lahore Pakistan

ISBN: (数字)9798350379419

ISBN: (纸本)9798350379426

With fuel prices rising and environmental concerns intensifying, the financial burden on consumers and businesses has shown an upward trajectory, prompting a search for sustainable alternatives sources. This study investigates the financial feasibility of installing a grid-tied solar photovoltaic (PV) system in residential buildings in suburban areas of Punjab, Pakistan. The research uses a complete intelligent financial model to assess the feasibility of a grid-connected solar PV system, incorporating parameters such as solar irradiance levels, system size, capital costs, operating expenses, energy rates, its incentives, and payback periods in a systematic computational optimization methodology. The shift to solar photovoltaic systems provides an affordable way to lower energy bills that can also lessen the dependency on fossil fuels. Additionally, solar PV systems help significantly to reduce greenhouse gas emissions and environmental damages by utilizing the clean energy of the sun. This is in line with worldwide efforts to battle climate change and protect the planet from environmental concerns. This research uses RET Screen software to evaluate the financial feasibility with GHG reduction analysis as well. The study’s findings are useful for homeowners, policymakers, and stakeholders interested in encouraging renewable energy adoption and sustainable development in Punjab, Pakistan’s suburban region.

关键词： Climate change Meters Computational modeling Optimization methods Fuels Pricing Power grids Photovoltaic systems Financial management Energy management Buildings Solar power generation Economics

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Advancing Low Resource Natural Language Processing: Techniques, Applications, and Future Directions

Advancing Low Resource Natural Language Processing: Techniqu...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Parth Middha Harshit Agarwal Vaibhav Rajput Ayush Thakur Sofia Singh Shipra Saraswat Dept. of Computer Science & Engineering Amity University Noida India Amity Institute of Information Technology Amity University Noida India Dept. of Artificial Intelligence Amity University Noida India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This paper explores the development and application of low resource Natural Language Processing (NLP) modules, addressing the challenges of processing underrepresented languages and domains with limited linguistic resources. It discusses key methodologies such as transfer learning, unsupervised and semi-supervised learning, and data augmentation techniques that enable effective NLP in resource-constrained environments. The paper presents case studies in machine translation, named entity recognition, and sentiment analysis, demonstrating the practical impact of these approaches. Additionally, it outlines persistent challenges in the field and proposes future research directions, emphasizing the importance of enhancing data acces-sibility, model robustness, computational efficiency, and ethical considerations in advancing low resource NLP.

关键词： Sentiment analysis Ethics Computational modeling Transfer learning Named entity recognition Data augmentation Data models Robustness Computational efficiency Machine translation

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Safeguarding Authenticity in Text with BERT-Powered Detection of AI-Generated Content 7

Safeguarding Authenticity in Text with BERT-Powered Detectio...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Chakraborty, Utsho Gheewala, Jaydeep Degadwala, Sheshang Vyas, Dhairya Soni, Mukesh Gujarat Technological University Computer Engineering India Sarvajanik College of Engineering And Technology Gujarat Surat India Sigma University Dept. of Comm. Engg. Gujarat India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India Dr. D. Y. Patil School of Science & Technology Tathawade Pune India

ISBN: (纸本)9798350359299

This research study explores the crucial domain of upholding textual authenticity by introducing a comprehensive method for identifying AI-generated content, employing BERT (Bidirectional Encoder Representations from Transformers). In a time when Artificial Intelligence (AI) significantly shapes written communication, it becomes imperative to differentiate between text produced by humans and that generated by machines. The proposed approach utilizes the capabilities of BERT by delving into contextual embedding, revealing complex patterns that serve as indicators of AI origin. Through meticulous experimentation and evaluation, we substantiate the effectiveness of our method in precisely discerning AI-generated text. This contribution adds to the ongoing endeavors to safeguard the integrity of human-authored content in the ever-evolving digital landscape. © 2024 IEEE.

关键词： Embeddings

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Adaptive Model Predictive Control-Driven Approach for Visual Detection of Micro- UAVs

Adaptive Model Predictive Control-Driven Approach for Visual...

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IEEE International Conference on Communications (ICC)

作者： Gunasekaran Raja Selvam Essaky Deepak Suresh Rajendran Sai Ganesh Senthivel Sebastian Knorr Kapal Dev Department of Computer Technology NGNLab Anna University Chennai India Information Networking Institute Carnegie Mellon University Pennsylvania USA Dept. of Electrical Engineering & Information Technology Ernst Abbe University Jena Germany Dept. of Computer Science Munster Technological University Ireland

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Unmanned Aerial Vehicles (UAVs) provide a good base platform for dynamic vision-based target detection. The detection process can be enhanced by utilizing multiple UAVs. In such scenarios, precise trajectory planning is essential to achieve objectives while avoiding collisions with obstacles. Furthermore, in most cases, Air-to-Air (A2A) detection of micro-UAVs in large-scale environments is challenging due to their dynamic movements and other complex parameters, such as poor light, motion blur, and occlusion. To solve these challenges, developing an algorithm that can adapt the control strategy based on changing system and environmental parameters is essential. This paper proposes an Adaptive Model Predictive Control (AMPC)-driven hybrid GANYOLOX framework to mitigate these navigation and detection challenges. The proposed AMPC allows model updates during multi-UAV operations, which enables estimation techniques to predict changes in the system model and helps to compute the target UAV position. Alternatively, the framework constructs a hybrid GAN-YOLOX model to overcome various A2A detection challenges. Extensive comparative analysis of the hybrid GANYOLOX framework achieved 94% detection accuracy and out-performed existing DL frameworks by 8%.

关键词： Adaptation models Accuracy Trajectory planning Computational modeling Neural networks Object detection Predictive models

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