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A Survey of Android Malware Detection Based on Deep learning 1

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Wang, Dianxin Chen, Tian Zhang, Zheng Zhang, Nan Beijing Institute of Technology Beijing100081 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Android malware poses a serious threat to cyberspace security, and many researchers are committed to researching fast and effective methods for Android malware detection. However, the latest Android malware usually uses escape techniques such as code obfuscation, so the traditional machine learning methods gradually become invalid. In recent years, deep learning has been gradually applied in the field of Android malware detection due to its powerful data processing and feature representation capabilities, and has achieved convincing performance. this paper conducts a review and research on the related achievements of Android malware detection based on deep learning in recent years, and classifies Android malware detection methods according to different characteristics and different networks. At the same time, a systematic introduction on deep learning model algorithms, such as RNN, CNN, Attention Mechanism, etc., is made. Besides, we analyze most commonly used datasets and evaluation indicators. the outcomes of this paper can provide a more comprehensive overview for researchers in the field, aiming to inspire researchers to make more and better achievements in Android malware detection. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Android malware

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NASDAQ Stock Value Prognosis—A Comparative Scrutiny Using Deep learning and machine learning Models 4th

NASDAQ Stock Value Prognosis—A Comparative Scrutiny Using D...

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and applications, ICMISC 2023

作者： Gundu, Ananthalakshmi Bhaskar, Nuthanakanti Srujan Raju, K. Reddy, Avala Raji Department of Computer Science and Engineering CMR Technical Campus Hyderabad India Department of Mechanical Engineering CMR Technical Campus Hyderabad India

ISBN: (纸本)9789819994410

Predicting stock market trends is challenging due to the uncertainty and multiple factors that influence stock prices. this project uses deep learning and machine learning techniques to predict stock market trends. Based on stock market data from top IT companies listed on the NASDAQ stock exchange, the dataset is made up of 10 years of stock market data. there are five machine learning models considered in the study, including Decision Trees, Random Forests, Adaptive Boosters, Support Vector Classifiers, and Logistic Regression models, and deep learning methods included in the study such as Long Short-Term Memory. To gain insights, the first step is to conduct a data analysis of the past one-year data of the IT companies. Data for the last 60 days of the stock market were used only for testing and predicting, and data for the last 10 years of stock prices were used to evaluate the models. In order to train the LSTM model, the remaining nine years and ten months of data are used. LSTM models can be evaluated based on their accuracy by comparing the predicted values of the stock values over the last 60 days with the actual values of the stock during that period. the developed LSTM model demonstrates high accuracy when predicting stock values with minimal deviation. this project provides a valuable tool for stockholders and venture capitalists to make informed decisions by leveraging deep learning and machine learning techniques for stock market trend prediction. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Enhancing Apple Leaf Diagnosis through Deep learning Techniques 4

Enhancing Apple Leaf Diagnosis Through Deep Learning Techniq...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Rekha, H. Kumaravel, T. Natesan, P. Sangeetha, S. Nathiya, N. Uvetha, V. Shridevi Institute of Engineering and Technology Department of Information Science and Engineering Karnataka Tumkur India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Erode India Kongunadu College of Engineering and Technology Department of Information Technology Tamilnadu Trichy India Paavai College of Engineering Department of Computer Science and Engineering Tamil Nadu Pachal India

ISBN: (纸本)9798350300888

Apple is a type of fruit, which is very nutrient-rich and offers a variety of health advantages. they are also rich in antioxidants and fiber. Consuming them is associated with a reduced chance of developing many chronic conditions, including diabetes, a condition coronary heart disease, as well as cancer. Aside from aiding in weight loss, apples may also enhance digestive and cognitive well-being. Apple Leaf, on the other hand, has a lot of fiber and can be helpful when you're sick to increase your appetite. Apple leaf is thought to have anti-inflammatory qualities and is high in antioxidants. the methods of detecting apple leaf disease are presented in this research. It is dependent on a study that develops a CNN (Convolutional Neural Network) to categorize a completely distinct variety of apple leaf diseases. If a diseased leaf is discovered, this program will detect the type of the disease. the system in question uses cutting-edge deep learning techniques as well as transfer learning and convolutional neural networks. the outcome demonstrates the CNN model that has been retrained performs better when detecting the disease of the leaf. the conventional methods to classify leaf disease have had an accuracy ranging around 95%. Hence, to classify the leaf disease more accurately and preferably, Convolutional neural networks-based deep learning for image recognition is proposed. this is a lightweight PlantifyDr Dataset on Kaggle that serves to identify leaf photos using the DNN. With the help of VGG16, the accuracy achieved in this work is about 97.50% on the Apple Leaf Dataset. It presents good accuracy in classifying the type of disease when it is compared with many other approaches found in the literature. © 2023 IEEE.

关键词： Convolutional neural networks

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Pluggable Compensation Module for Twofold Propagation-based Hologram Generation 4

Pluggable Compensation Module for Twofold Propagation-based ...

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4th international Computational Imaging conference, CITA 2024

作者： Yuan, Ganzhangqin Tsinghua Shenzhen International Graduate School Tsinghua University China

ISBN: (纸本)9781510688834

Recent advancements in learning-based computer-generated holography (CGH) have enabled sophisticated methods for generating phase-only holograms (POHs). However, a key challenge remains unaddressed: ringing artifacts, which arise due to the Gibbs phenomenon during the diffraction propagation process, significantly degrade the quality of reconstructed holographic images. To address this, we introduce a novel pluggable compensation module designed to predict and mitigate these artifacts by adjusting for residual values during propagation. this pluggable module, easily integrated with existing CGH networks, alleviates the network's burden by aligning the diffraction process more closely with ideal conditions. Our results demonstrate that embedding this module in state-of-the-art CGH frameworks, such as HoloNet and CCNN, elevates the PSNR by up to 3.9 dB and 0.6 dB, respectively. Notably, our method achieves considerable artifact reduction in real-world scenarios, thereby enhancing the overall fidelity of holographic displays. this pluggable module represents a substantial advancement, offering versatility and improved performance across diverse CGH algorithms. © 2025 SPIE.

关键词： Holograms

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Estimation of Power Consumption Prediction of Electricity Using machine learning 4th

Estimation of Power Consumption Prediction of Electricity Us...

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Yeleswarpu, Paradhasaradhi Nayak, Rakesh Patidar, R.D. Department of Computer Science and Engineering OP Jindal University Raigarh India Department of Electrical Engineering OP Jindal University Raigarh India

ISBN: (纸本)9783031488870

Electricity consumption has been broadly concentrated on in the PC engineering field since numerous years. While the securing of energy as an action in ML is arising, a large portion of the trial and error is still essentially centered around getting raised degrees of precision with no computational limitations. We accept that one of the reasons for this deficiency of interest is because of their shortfall of straightforwardness with admittance to assess energy utilization. the principal objective of this study is come to assess valuable guidelines to the MLpeople group that grants them the major acknowledgment to utilize and fabricate energy assessment techniques for AI calculations. Utilization of various group models like Linear Regression, and random forest regression and gride search cv, adaboost algorithms to predict the power and to acquire exact outcomes. Notwithstanding, we additionally present the state-of-the-art programming apparatuses that award power assessment standards, along with two use cases that reinforce the request of energy fatigue in ML. Toward the end, we anticipate the future energy which is so useful to the matrix to make exact energy for the network by refreshing with shrewd meters where everyone can know individuals, who are involving more energy in what machines, so it is gigantically useful in which time we want more energy and less energy. © 2024, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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machine learning Models for PCB Defect Detection: A Survey 6

Machine Learning Models for PCB Defect Detection: A Survey

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6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Subbulakshmi, T. Ramachandran, Vishwath Anand, Philip Madhavan, Rohit Revathi, M. Subramanian, Girish H. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Computer Science and Engineering Chennai India Pennsylvania State University School of Business Administration PA United States

ISBN: (纸本)9798350394702

Almost every commonly used electronic device relies on a Printed Circuit Board (PCB) produced through Surface-Mount Technology (SMT). thanks to advancements that have significantly reduced the size of electronic components, PCBs have dramatically decreased in size. Consequently, it is crucial to conduct the application and examination of solder paste during SMT with a strong emphasis on speed and precision. this research survey delves into an analysis of Solder Paste Inspection (SPI) methods, drawing insights from more than 30 research papers spanning several decades. Initially, the survey identifies various PCB defect detection methods and assembles relevant papers for each approach. Subsequently, it provides a brief assessment of these papers, comparing their algorithms, performance, advantages, and challenges. this comprehensive overview aims to offer fresh perspectives to researchers and readers interested in gaining a deeper understanding of the latest developments in solder paste inspection and the detection of defects in PCBs. © 2023 IEEE.

关键词： Printed circuit boards

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11th international conference on Mechatronics and Control Engineering, ICMCE 2023

11th International Conference on Mechatronics and Control En...

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11th international conference on Mechatronics and Control Engineering, ICMCE 2023

ISBN: (纸本)9789819965229

the proceedings contain 13 papers. the special focus in this conference is on Mechatronics and Control Engineering. the topics include: Using Wheel Slippage for Improved Maneuverability of 4 Wheel Steering Vehicles;design and Construction of a Prototype for the Lyophilization Process;three-Dimensional Finite Element Analysis and Experiment of Temperature Rise of Permanent Magnet Linear Synchronous Motor;Modeling and Control of BLDC Motor for Scaled Autonomous Vehicle application;FedMSPC: A Federated Multivariate Statistical Process Control Framework For Privacy-Preserving Process Modeling Across Company Borders;LPV Modeling for Control Scheme Design of a Compound Helicopter;piecewise Modeling Algorithms Using Numerical Data;Scaling of a Modular Production System (MPS) with Manufacturing Execution System (MES) and Multiple Agents;an Effective Methodology for Imbalanced Data Handling in Predictive Maintenance for Offset Printing;Design and Experimental Validation of RL-Based Decision-Making System for Autonomous Vehicles;performance Evaluation of machine learning App Approach to Modular Arrangement of Predetermined Time Standard.

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4th international conference on Advanced Engineering Optimization through Intelligent Techniques, AEOTIT 2023

4th International Conference on Advanced Engineering Optimiz...

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4th international conference on Advanced Engineering Optimization through Intelligent Techniques, AEOTIT 2023

ISBN: (纸本)9789819746538

the proceedings contain 51 papers. the special focus in this conference is on Advanced Engineering Optimization through Intelligent Techniques. the topics include: Solving 2-Domination Problem in Graphs Using Genetic Algorithm;techno-Economic and Voltage Secure Operation of Hybrid Power Systems in Optimal Power Flow Framework;End-To-End Slicing and Devising a Scheduler for a 5G C-RAN Using PROMEthEE Method;LionSense: Redefining Wildlife Monitoring and AI-Powered YOLOv5 Lion Detection and Classification;A Comprehensive Analysis of AC-Decoupling Techniques for Common-Mode Leakage Current Reduction;parameters Optimization of Selected Advanced Manufacturing Processes;Hydraulic Modelling and Flood Impact Assessment in the Periyar River Basin: An Integrated Approach of HEC-RAS, HEC-GeoRAS and Salt Concentration Method;diversified Ensemble learning: Integrating Bagging and Teaching–learning-Based Optimization with a Pairwise Dissimilarity Measure;Evaluating Pareto Solutions in Optimization Problems of thermal Systems Using BHARAT Method;modelling of Harmonic Filter—Improvement of Power Quality Indices Under Non-linear Loads: A Case Study;reduction of Vibrations Using Variable Damping Torsional Dampers for Smart Vehicles Using Real-Time Intelligent Smart Fluid Controls;Mathematical Modelling and Numerical Simulation, FSW of Dissimilar Magnesium Alloys Using Eccentric Pins Configurations;material Flow Optimization of a machine Loading Problem;a Comparative Analysis of Resampling Techniques to Address Imbalanced Datasets;parameters Optimization of Selected Advanced Welding Processes;green Logistics of Crude Oil Transportation: A Multi-objective Optimization;optimized Lateral Motion Control of Boeing 747 Using Particle Swarm Optimization;performance Analysis of Gravitational Search Algorithm During Parametric Optimization of Machining Processes;many-Objective Optimization of a 120-Bar 3D Dome Truss Structure Using three Metaheuristics.

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Design of learning Algorithm Engineering Electrical Control System based on Fuzzy Neural Network 4

Design of Learning Algorithm Engineering Electrical Control ...

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Jiang, Yuxuan Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Zhejiang Province Ningbo315100 China

the traditional electrical control system has the problem of insufficient function control. this paper designs the engineering electrical control system through the learning algorithm based on fuzzy neural network. the control strategy design module combines fuzzy reasoning with neural network to design intelligent control strategy. Develop a variety of control principles and strategies that can meet the needs and characteristics of the system. the user interface module assists users to monitor and adjust the system, and has the functions of real-time data display and setting control parameters. Fuzzy neural network modeling module combines fuzzy logic with neural network to model the system. In this paper, the sliding window technology is used to collect and process data in real time. When the running time is 1 hour, the system output value is 100.2 and the output fluctuation value is 0.6;When the running time is 2 hours, the system output value is 99.8 and the output fluctuation value is 0.7. the learning algorithm based on fuzzy neural network has little fluctuation and stable performance. © 2024 the Authors.

关键词： Fuzzy inference

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machine learning-Based Airline Passenger Satisfaction Evaluation 4

Machine Learning-Based Airline Passenger Satisfaction Evalua...

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4th international conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Yang, Sinan School of Information Science and Technology Hangzhou Normal University Hangzhou China

ISBN: (纸本)9798350341690

As competition intensifies, airlines must identify the key factors affecting passenger satisfaction and take appropriate measures to improve their services. To this end, this study analyses airline satisfaction and proposes strategies to improve the passenger experience in the airline industry. the study employs a data-driven approach and machine learning algorithms to analyse comprehensive data including factors such as flight distance, age, delays, in-flight service, ease of online check-in and customer demographics. Specifically, the study uses Exploratory Data Analysis (EDA) to identify potential patterns and relationships, and then implements the XGBoost algorithm to predict passenger satisfaction based on these factors. In addition, the Bagging algorithm is used to improve the predictive performance of the model. Experimental results on the study dataset demonstrate the effectiveness of the method. the study identifies important factors that contribute to passenger satisfaction, such as inflight WiFi service, online check-in and type of travel. As well as the closeness of the different factors to passenger satisfaction. these findings provide valuable insights for airlines to optimise their services and allocate resources effectively, ultimately improving customer retention and business growth. By implementing the recommended strategies, airlines can improve service quality, foster customer loyalty and maintain a competitive edge in the dynamic airline industry. © 2023 IEEE.

关键词： Air transportation

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