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Malware image classification: comparative analysis of a fine-tuned CNN and pre-trained models

International Journal of computers and Applications 2023年第11期45卷 709-721页

作者： Majhi, Santosh Kumar Panda, Abhipsa Srichandan, Suresh Kumar Desai, Usha Acharya, Biswaranjan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Burla India Department of Information Technology Veer Surendra Sai University of Technology Burla India Department of Electronics and Communication Engineering SR University Telangana Warangal India Department of Computer Engineering-AI & BDA Marwadi University Gujarat Rajkot India Department of Electronics and Communication Engineering S.E.A. College of Engineering & Technology Bengaluru India

A crucial part is played by malware detection and classification in ensuring the safety and security of computer systems. In this work, a comprehensive study has been presented for the classification of harmful or malware images that uses a Convolutional Neural Network (CNN) which has been finely tuned and its performance has been compared with five pre-trained models: ResNet50, InceptionResNetV2, VGG16, Xception and InceptionV3. The suggested CNN framework has been trained using the dataset MalImg_9010, consisting of 9,376 grayscale images resized to 128 × 128 pixels. The models have been evaluated based on their F1 score, recall, precision, and accuracy. The experiments that were conducted demonstrate that the fine-tuned CNN model achieves an impressive 0.965 as the F1 score and a 95.57% accuracy. Furthermore, the comparison with pre-trained models reveals the dominance of the presented framework concerning the F1 score and accuracy. The output of the conducted simulation suggests that the fine-tuned CNN approach shows promise for accurate malware image classification. Additionally, the paper discusses potential improvements, such as increasing the number of training epochs and incorporating larger and more diverse malware datasets, including RGB images and a broader range of malware families. The current research article gives valuable observations on various models’ effectiveness for classifying malware images and highlights the future scopes for research incorporating this domain. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image classification

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Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

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computer Systems Science & engineering 2023年第1期44卷 1-17页

作者： R.Bhaskaran P.S.Sujith Kumar G.Shanthi L.Raja Gyanendra Prasad Joshi Woong Cho Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Computer Science and Engineering MEA Engineering CollegeMallapuram679325KeralaIndia Department of Electronics and Communication Engineering Rajalakshmi Institute of TechnologyKuthambakkamChennai600124India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore641202India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,*** the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of *** study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless *** proposed IM-EECNL technique involves two major processes namely node localization and ***,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the ***,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the ***,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and *** performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

关键词： Wireless networks real time applications clustering node localization energy efficiency metaheuristics

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A Smart Bin with Real-Time Monitoring and Garbage Level Tracking Using IoT 11

A Smart Bin with Real-Time Monitoring and Garbage Level Trac...

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

作者： Ayyanagoudar, Anand Mohankar, Akash Gharte, Jayesh Pasnsare, Pooja Ainapure, Bharati Appasani, Bhargav Vishwakarma University Department of Computer Engineering India School of Electronics Engineering Kalinga Institute of Industrial Technology India

ISBN: (纸本)9798350350357

This paper presents a novel system for smart waste management using IoT technology. With the rapid growth of urban areas and population, proper waste management has become a crucial issue for maintaining a healthy environment. The proposed system utilizes weight sensors and ultrasonic transducers to monitor the fill levels of dust bins in real-time. The system provides a user-friendly web page that displays a visual representation of the garbage levels in each bin, allowing users to take appropriate actions based on the current status. The system also sorts trash according to their weight, providing a precise idea for transportation of products. The proposed system offers an innovative and advantageous approach to waste management, promoting efficient and sustainable practices. © 2024 IEEE.

关键词： Sustainable development

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Data Driven Approach for Analysing the Relationship between Weather Patterns and Energy Consumption in Residential Buildings for Improved Energy Management 5

Data Driven Approach for Analysing the Relationship between ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kowshik, S.B. Ramakrishnan, R. Amutha, S. Dhanush, R. Akshay, P. Preetam Kalyaan, P. School of Electronics Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331518097

Residential buildings are the most significant providers of global energy consumption. Hence, studying the weather patterns and energy use in residential buildings remains vital for effective energy management strategies. This work explores a data-driven approach to analyse the complex interplay between weather and energy consumption in residential settings. Traditional methods for understanding building energy use often rely on physical models or occupant surveys, which can be time-consuming and expensive. This research addresses the need for a more efficient and comprehensive approach to analyse the effect of weather patterns on energy consumption. Following deep learning sequence models were used to most accurately predict the energy consumption - LSTM (Long Short Term Memory), Bi-LSTM (Bidirectional LSTM) and Attention Based LSTM (ABLSTM) and find the ABLSTM model most accurate and suited for our requirement with RMAE value as 0.3086, R2 as 0.9990. This research provides individuals and communities with the knowledge to make informed decisions about their energy usage, potentially leading to reduced energy costs through optimized consumption and can contribute to improved energy security. © 2024 IEEE.

关键词： Building energy prediction Data driven Deep learning Energy shortage LSTM Sustainability Sustainable energy

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Deep Learning-based Text News Classification using Bi-directional LSTM Model 3

Deep Learning-based Text News Classification using Bi-direct...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Anand, Shreyas Prakasam, P. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

The advancement of technology has resulted in increased news consumption through various online platforms and social media channels. Hence, there is a requirement for intelligent systems that may categorize the news that exists on the social media/online platform. Hence, this paper explores creating a news classifier utilizing a Bidirectional Long ShortTerm Memory (Bi-LSTM) deep learning model, which is a type of recurrent neural network (RNN) specifically designed to overcome the challenge of vanishing gradient often faced in deep learning. The machine learning was performed on a Kaggle dataset with over 400,000 records of news stories from various publishers. Natural language processing techniques such as stemming, one-hot encoding, vectorization, and embedding are used to prepare the data for machine learning. The multiclass classification is achieved and the performance of the model is analyzed. © 2024 IEEE.

关键词： Classification (of information)

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Predictive Insights into Electric Vehicle Market Dynamics: An Exploration through Machine Learning 4

Predictive Insights into Electric Vehicle Market Dynamics: A...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Aaditya, S. B. Abrish Kurmi, Aditya Karandikar, P.B. Army Institute of Technology Department of Electronics and Telecommunication Engineering Pune India Army Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350354218

The burgeoning electric vehicle (EV) sector epitomizes a crucial transition towards more sustainable modes of transportation, underscored by the global imperative to mitigate carbon emissions. However, despite the technological strides made in this arena, the pace of EV adoption is tempered by a confluence of factors, including but not limited to the initial purchase cost, the driving range per charge, and prevailing consumer perceptions surrounding EV utility and feasibility. This investigation harnesses the analytical prowess of machine learning (ML) methodologies to delve into the nuanced dynamics of the EV market. By examining a comprehensive array of EV attributes, this study endeavors to distill the determinants that significantly sway consumer adoption patterns. Preliminary findings underscore the paramount influence of variables such as top speed, operational range, and rapid charging capability on the EV's market price, delineated by their pronounced positive correlations. These insights not only illuminate the attributes that consumers value most but also furnish automobile manufacturers with the empirical evidence needed to tailor their development and marketing strategies, thereby catalyzing the transition towards electric mobility. © 2024 IEEE.

关键词： Self-supervised learning

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An Advanced Surveillance Motorized Car for Real-Time Inspection with Sensing Capabilities

An Advanced Surveillance Motorized Car for Real-Time Inspect...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： Ingale, Kiran Tekade, Om Thakare, Prathamesh Wankhade, Abhishek Wankhade, Purva Department of Electronics and Telecommunication Engineering Vishwakarma Institute of Technology Pune India Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819746491

In recent years, the Internet of things (IoT) has emerged as a promising technology for improving quality of life and addressing social challenges. The development of a proposed car is done with integrated sensors and advanced motor control methods. The ESP8266 module in the car is connected to sensors for measuring distance, temperature, humidity, and air quality. Precise movements in various directions are made possible by servomotors and a motor driver. Programming the ESP8266 and ESP32 CAM modules as well as integrating the Blynk interface for remote control are also included in the software. The applications in surveillance and environmental monitoring show successful integration and functionality. The proposed system offers a comprehensive system for real-time remote data collection and control, advancing robotics and the Internet of things. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Air quality

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Deep Fish: An Approach to Fish Species Identification Through Deep Learning Techniques 3rd

Deep Fish: An Approach to Fish Species Identification Throug...

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3rd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Reddy, Penumalli Jithya Malathi, M. Julaiha, AG. Noorul Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department Computer Science Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9789819739905

This analysis introduces a system for automated identification of fish species and classification based on deep learning. This system can provide valuable insights to marine biologists, with better understanding on fish species habitats. The developed system relies on deep learning neural networks (DNNs) to extract features from underwater images and classify fish into different species. Classifying underwater images of fish species is crucial for conducting fish surveys, maintaining ecological balance, monitoring populations, and conserving endangered species. Yet, it proves challenging due to optical obstacles like smattering and raptness in ocean water, leading to dark and low images. This research delves into the application of deep learning in diverse aquatic tasks, encompassing fish identification, classification, feeding decisions and behavior analysis, estimation of its size, and quality of water prediction. Using a sizable labeled dataset from Kaggle, the proposed system employs a hierarchical CNN classification approach, achieving notable recognition rates and accuracy. The study highlights the efficacy of deep learning in fish species classification, emphasizing its automatic feature extraction capability, and removing the necessity for labor-intensive manual work when engineering features. Furthermore, the advantage of deep learning models lies in their capacity to attain high accuracy with extensive datasets, exemplified by the successful training on the Kaggle dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Eco-friendly Smart Sensor Adjustment using Deep Reinforcement Learning Approach 2

Eco-friendly Smart Sensor Adjustment using Deep Reinforcemen...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Reethika, A. Lavanya, S. Sheetal, Attar Mahay Vanitha, C.N. Priyadharsini, S. Jenolin rex, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Mahendra College of Engineering Salem India

ISBN: (纸本)9798331541217

Today's networking and telecommunications industries greatly benefit from the growth of highly sturdy and energy-effective Internet of Things through communication sensors. Device efficiency may be increased while sensor energy consumption is reduced by calibrating sensors to use less power. Reinforcement learning (RL), which has garnered significant interest from researchers, is being widely applied to achieve intelligent automation in several academic disciplines today. Resource optimization has not received much attention in research, despite the Internet of Things using a large variety of sensors. By investigating a revolutionary approach to power conservation with the use of reinforcement learning, the creative study aims to produce a novel generation of Internet of Things with standardized power sources to optimize source consumption. A closed-grid, numerous power source controlling system for sensor consumption has been revealed. Our recommended strategy, which uses Deep Q learning, can help Internet of Things sensors optimize their operation. This study focused only on energy-effective sensor and simulation results show that the planned approach works well. © 2024 IEEE.

关键词： Telecommunication industry

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Sparse Rev-Shift Coded Modulation with Novel Overhead Bound

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China Communications 2023年第10期20卷 17-29页

作者： Mingjun Dai Wanru Li Chanting Zhang Xiaohui Lin Bin Chen School of Computer Science and Technology Kashi UniversityKashi 844000China College of Electronics and Information Engineering Shenzhen UniversityShenzhen 518000China

To provide reliability in distributed systems,combination property(CP)is desired,where k original packets are encoded into n≥k packets and arbitrary k are sufficient to reconstruct all the original ***-and-add(SA)encoding combined with zigzag decoding(ZD)obtains the CP-ZD,which is promising to reap low computational complexity in the encoding/decoding process of these *** densely coded modulation is difficult to achieve CP-ZD,research attentions are paid to sparse coded *** drawback of existing sparse CP-ZD coded modulation lies in high overhead,especially in widely deployed setting m

关键词： distributed system shift-and-add zigzag decoding sparse coded modulation

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