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Predicting Mobile Prices with Machine Learning Techniques

Predicting Mobile Prices with Machine Learning Techniques

引用

2024 International Conference on Computational Intelligence and Computing applications, ICCICA 2024

作者： Rana, Deepak Singh Dhondiyal, Shiv Ashish Singh, Sumeshwar Kukreti, Sanjeev Dhyani, Ashish Graphic Era Hill University Dept. Of Computer Science And Engineering Dehradun India Graphic Era Deemed To Be University Dept. Of Computer Science And Engineering Dehradun India Jaypee Institute Of Information Technology Dept. Of Computer Science And Engineering India Graphic Era Deemed To Be University Department Of Hospitality Management Uttrakahnd Dehradun India

ISBN: (纸本)9798350306446

Smartphones are increasingly vital to people on a daily basis. Telephones are utilized in all aspects of life, ranging from personal to professional, due to technological advancements. It serves a function beyond making phone calls. It enables internet connectivity and email reading. when not using the computer. The characteristics of a mobile phone are a crucial consideration when buying *** overall objective of this research is to find the best way to apply machine learning to estimate the retail pricing of smartphones based on their individual specs. Individuals who frequently use their phone are more attentive to selecting features. When purchasing a cell phone, a comparison is done based on the price-performance ratio. Phone features are regarded as performance. This research aims to forecast if mobile phones with certain features are considered economical or *** work is capable of being utilized in various marketing and business contexts to assist in making informed purchasing decisions by maximizing features while minimizing costs. © 2024 IEEE.

关键词： Decision trees

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Network Intrusion Detection System Using Decision Tree and KNN Algorithm 3

Network Intrusion Detection System Using Decision Tree and K...

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3rd International Conference on Power Electronics and IoT applications in Renewable Energy and its Control, PARC 2024

作者： Singh, Km Shivani Singh, Khundrapam Johnson National Institute of Technology Dept. of Computer Science and Engineering Manipur Imphal India

ISBN: (纸本)9798350309409

The complexity of attacks is increasing, making it increasingly difficult to effectively discover breaches. A network intrusion detection system is needed to handle the aforementioned problem. An automated software program called a network intrusion detection system alerts administrators when someone attempts to compromise the system by participating in hazardous behaviors. A system or network is protected from harmful invasions by hardware and software firewalls. They function like filters, removing any data that could put the system or network at risk. We aimed to design the best intrusion detection system possible so that it could distinguish between 'normal' and 'attacked' categories of network data with high accuracy. The best accuracy can be obtained by using soft computing techniques like Decision Trees and KNN. We tested a variety of techniques, such as preprocessing the data, feature selection, principal component analysis (PCA) reduction, standardization, and normalization, in order to increase our model's accuracy scores. We also assess the two approaches' results according to their accuracy in order to decide which way is more effective. © 2024 IEEE.

关键词： Decision trees

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Malware Detection across Clusters

Malware Detection across Clusters

引用

2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Manjushree, C.V. Nandakumar, A.N. Vemana Institute of Technology Dept. of Information Science & Engineering India City Engineering College Dept. of Computer Science & Engineering India

ISBN: (纸本)9781665462631

Due to diverse software vulnerabilities and hardware attacks, user credentials are vulnerable or could land in demilitarized zones. An attempt is made to explore and finally proposed a trust based malware detection based on optimal data classification. An improved glowworm swarm optimization (IGWSO) algorithm is proposed for clustering in the input dataset into different groups. After clustering, recurrent deep neural network based computation is used for deriving different trust levels for cloud information that classify the suspected intrusion. The proposed TMD system implemented in java with cloudsim tool evidently discovers the effectiveness of the algorithm over existing state of art systems on the parameters of high detection, precision, recall and F-measures. © 2023 IEEE.

关键词： Deep neural networks

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Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix

Arrhythmia Classification Using Graph Neural Networks Based ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Han, Seungwoo Tokyo University of Agriculture and Technology Dept. of Electrical Engineering and Computer Science Japan

ISBN: (纸本)9798350386226

With the advancements in graph neural network (GNN), there has been increasing interest in applying GNN to electrocardiogram (ECG) analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted ECG features and applied a GNN to classify arrhythmias. Our work was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that proposed method can be considered an approach for arrhythmia classification. © 2024 IEEE.

关键词： Graph neural networks

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Optimizing Satellite Constellations for Global Internet Coverage: A Comparative Analysis of Communication Protocols and Orbital Configuration

Optimizing Satellite Constellations for Global Internet Cove...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Banerjee, Pallab Raj, Vivek Kumar, Biresh Thakur, Kanika Mitra, Dipra Kumar, Purushottam Amity University Jharkhand Dept. of Computer Science Engineering Jharkhand Ranchi India Amity University Jharkhand Amity School of Information Technology Jharkhand Ranchi India Galgotia University Dept. of Computer Science Engineering Greater Noida campus India Amity University Dept. of Computer Science Engineering Jharkhand Ranchi India

ISBN: (纸本)9798350385199

As a way to provide ubiquitous connectivity, satellite constellations are becoming more and more popular due to the growing need for worldwide internet access. In order to provide dependable and seamless worldwide internet coverage, this study investigates how satellite constellations might be optimized by comparing and contrasting communication protocols and orbital topologies. This ambitious aim involves a number of problems and complications, which are explored in the research along with the trade-offs and design considerations that must be made in order for the implementation to be effective. Two main elements form the framework of the comparative analysis. First, by considering factors like latency, data throughput, and coverage efficiency, it evaluates a range of communication protocols, including traditional geostationary satellites, Low Earth Orbit (LEO) constellations, and Medium Earth Orbit (MEO) satellites. Second, to ascertain the effect of various orbital configurations on worldwide internet coverage, the study considers satellite dispersion, orbital altitudes, and coverage patterns. By means of this study, the research endeavors to provide a valuable contribution to the current discussion on satellite constellations for worldwide internet coverage, therefore facilitating well-informed decisions about the design and implementation of these systems. © 2024 IEEE.

关键词： Geostationary satellites

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Design and Fabrication of an IoT based Air Purifier using HEPA Filter 11

Design and Fabrication of an IoT based Air Purifier using HE...

引用

11th International Conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2023

作者： Choudhary, Arpita Saini, Lakhan Ahmad, Afaq Banerjee, Hriday Gazi, Firoj University of Engineering and Management Jaipur Dept. of Computer Science Jaipur India UEM Jaipur Dept. of Computer Science Jaipur India University of Alberta Dept. of Computer Science Edmonton Canada

ISBN: (纸本)9781665475129

Air pollution has become one of the major problems for human health across the globe. Indoor air pollution poses more risk than outdoor air pollution, since the human body is more exposed to the inside. It refers to the contamination of indoor air that causes harmful health problems. To purify the polluted air, the air purifier is a main necessity. In this Internet of Things (IoT) project, we have created an air purifier that purifies the indoor air by passing it through the High Efficiency Particulate Air (HEPA) filter and Ultraviolet light. The microcontroller used is the NodeMCU based on the ESP8266 WiFi enabled chip. Two MQ135 air quality sensors used to monitor the change and calculate the efficiency of the purified air. IoT customization eases the manual work of changing the purifier manually every time by providing a platform that operates on the mobile phone via Wi-Fi where the user can operate it accordingly. It provides an equivalent grade of services at a very moderate cost that is affordable even for the average person and can provide real time analytics. The proposed system is capable for providing hands-on practical operations, which enables more exciting possibilities in IoT based automation systems. © 2023 IEEE.

关键词： Internet of things

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Slang Word Detection in the Context of User Profiling in the Social Media Platformes 4

Slang Word Detection in the Context of User Profiling in the...

引用

4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Samanta, Bikram Shil, Ritika Pal, Alok Ranjan Pal, Antara College of Engg. and Management Dept. of Computer Science and Engg. Kolaghat India Atharva College of Engineering Dept. of Computer Science Engg. Mumbai India

ISBN: (纸本)9798350343670

On social media platforms, people express their views in various ways, sometimes using formal words and sometimes in informal form. The informal conversations may contain jargon words i.e. slang words which might be improper for all kinds of audience. In this work, an attempt has been made to identify slang words in a text, posted on the social media platform. The overall experiment has been carried out in 4 modules. In module-1, the suspicious words from the posted text have been identified using minimum edit distance. This experiment has been implemented by the help of a dataset of printable slang words collected from Kaggle. In module-2, the topic of discussion has been analyzed by using a knowledge based approach. This part of the experiment has been carried out by creating a newspaper archive from the online repository of the "Times of India"newspaper. But, this knowledge based strategy could not produce an appreciable accuracy at its baseline. Therefore, as a modification of this technique, in module-3, the context analysis task has been implemented combining knowledge based approach and machine learning based approach. In module-4, the module-1 and module-3 are merged to tag a suspicious word with its level of abusiveness. © 2024 IEEE.

关键词： Context Analysis Slang Word Social Media Transformer User Profiling

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IoT Enabled Water Flow Management and Monitoring System for Hydroelectric Power Generation

IoT Enabled Water Flow Management and Monitoring System for ...

引用

2024 International Conference on Decision Aid sciences and applications, DASA 2024

作者： Shaikat, Abu Salman Tasnim, Rumana Hasan, Jahid Zonayed, Md Hussein, Molla Rashied Shithil, Shaekh Mohammad Al Amin, Abdullah Laurentian University of Sudbury Dept. of Bharti School of Engineering and Computer Science Ontario Canada World University of Bangladesh Dept. of Mechatronics Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Dept. of Mechanical Engineering Dhaka Bangladesh University of Asia Pacific Dept. of Computer Science & Engineering Dhaka Bangladesh University Technology Malaysia Dept. of Mechanical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350369106

Water energy sources play a crucial role, particularly in providing electrical energy through hydroelectric power. Both the water flow rate and the water head determine the potential energy available from water. The hydroelectric system's energy generation directly depends on the water flow rate. A lower flow rate means less water is available to generate energy, even if the head is high. It is essential to increase the potential of the water flow so that it can generate enough electricity. Traditionally, the flow rate is restricted due to the limited capacity of single penstock. This work addresses this issue by introducing multiple penstock-based water flow management systems. Also, conventional water flow monitoring systems often face limitations, like relying on manual measurement, as it only provides periodic data, making it difficult to respond quickly to changes in flow conditions. IoT-based flow sensors leverage IoT technology to monitor water flow and transmit flow data in real-time, enabling operators to detect fluctuating flow conditions and implement timely adjustments to processes, therefore enhancing the overall efficiency. This work aims to design and develop an IoT-enabled prototype hydroelectric power generation system using multiple water flows for enhancing power generation efficiency. The prototype indicates the use of multiple pipes as penstocks in a dam. Additionally, if generating a significant amount of electricity is not required, the water flow can be discontinued, thereby automatically closing each pipeline. This work uses flow sensors, Kitsware software, Arduino IoT Cloud, and an LCD display to generate flow measurement data every ten seconds from multiple pipelines. Moreover, the system automatically generates Excel data and graphically represents multiple water flows, enabling the user to compare the water flow data. The completed work resulted in a hydropower system demonstrating enhanced output and effective compatibility with IoT for

关键词： Penstocks

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Generating High-Quality Distributed Representations of Words Using Wiktionary and Sentence-BERT 16

Generating High-Quality Distributed Representations of Words...

引用

16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Nishiura, Ryota Tsuchiya, Seiji Watabe, Hirokazu Graduate School of Science and Engineering Doshisha University Dept. of Information and Computer Science Kyotanabe Kyoto Japan Doshisha University Faculty of Science and Engineering Dept. of Intelligent Information Engineering and Science Kyotanabe Kyoto Japan

ISBN: (纸本)9798350377903

We propose an improved method to generate high-quality distributed representations of words. In our previous paper, we introduced a novel method to obtain distributed representations of words with BERT using Wiktionary as semantic sources of words. The previous method outperformed a Wrd2Vec model in a word similarity task, while there still had to be some room to improve in term of semantic accuracy. In this paper, our new method introduces one of Sentence-BERT models to text-embed the semantic sources instead of the original BERT model. We demonstrate that our proposed method outperforms both our previous method and the Word2Vec model in the tasks. © 2024 IEEE.

关键词： Embeddings

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ElHa Net: A Pattern Recognition Neural Network 8

ElHa Net: A Pattern Recognition Neural Network

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Babiker, Elsadig Ahmed Mohamed Adlan, Hanan Hassan Ali Prince Sattam Bin Abdulaziz University College of Computer Engineering and Science Dept. of Computer Science Al-Kharj Saudi Arabia Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Dept. of Computer Science Riyadh Saudi Arabia Dept. of Computer Science Faculty of Mathematical Sciences United Kingdom

ISBN: (纸本)9798331540104

Neural Networks are the state-of-the-art models that derive intelligent systems. Generalization of the neural network model is a judge of how well an architecture mimic human intelligence. Self-extraction neural networks become the dominant neural networks, especially in autonomous systems such as intelligent robotics, autonomous vehicles, ṫ etc. This paper presents a neural network based on distance measure, ElHaNet Neural Network. ElHaNet network is a self-extraction neural network and is capable of pattern recognition. ElHaNet architecture composed of an extraction phase and a classification phase. Neurons in the extraction network capture patterns in the presented input. The classification network performs on the input patterns to be classified according to the problem in hand. The developed architecture is bench marked against well-known architectures for hand written digit recognition. The performance of the network is found to compete favorably with state-of-the-art models in the literature. © 2024 IEEE.

关键词： Neural network models

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