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A CNN and Image-Based Approach for Malware Analysis

A CNN and Image-Based Approach for Malware Analysis

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2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022

作者： Migdady, Aya Smadi, Lara Yaseen, Qussai Jordan University of Science and Technology Dept. of Computer Information Systems Irbid22110 Jordan Ajman University CEIT College AIRC Center United Arab Emirates

ISBN: (纸本)9781665477093

Malware attacks have various types, patterns, and volumes and have become more sophisticated and severe. Using machine learning to classify and detect malware is one of the approaches to mitigate malware attacks. However, malware classification suffers from some challenges such as the time required in manipulating a huge number of malware files. This paper proposes a Convolutional Neural Network (CNN) model and a pre-processing approach to solve the aforementioned issue. The contribution of this paper is based on converting the dataset into RGB images followed by scale maximization step. In addition, the paper proposes a preprocessing approach for the input datum of images. The results prove that the proposed preprocessing methods have a strong impact on enhancing the overall accuracy by increasing the accuracy from 92.5% to 98%. © 2022 IEEE.

关键词： Malware

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Smart Health Monitoring Wristband with Auto-Alert Function 5

Smart Health Monitoring Wristband with Auto-Alert Function

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Idris, Syahril Anuar Lazam, Nor Azlinah Md Izhar, Lila Iznita Azman, Dharwisyah Bt Way, Lim Jin Aida, Intan University Malaysia of Computer Science and Engineering Faculty of Engineering and Frontier Technology Selangor Cyberjaya Malaysia University Teknologi Petronas Electrical and Electronic Engineering Dept. Bandar Seri Iskandar Perak Malaysia Bachelor of Computer Engineering Selangor Cyberjaya Malaysia

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

Smart healthcare uses technology such as wearable devices and the Internet of Things to dynamically retrieve/access information, which is important for people who require continuous monitoring, that cannot be provided outside of medical facilities. The paper presents a smart health monitoring wristband that uses Arduino Nano 33 IoT with a built-in gyroscope and accelerometer module as the microcontroller, biomedical sensors like temperature sensor, pulse oximeter, heart rate sensor, and GSM/GPRS module. This prototype is developed for all age communities but can be especially useful to the elderly, people with special needs, and those with chronic illnesses. The hardware system is connected to the Blynk application using the microcontroller's built-in WiFi module. The biomedical sensors measure the readings, and the data are uploaded onto the Blynk application interface for viewing. The Smart Health Monitoring Wristband enables real-time health monitoring to be done remotely and helps in improving emergency response time. © 2022 IEEE.

关键词： Microcontrollers

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Utilizing Machine Learning Techniques for Crime Classification Under the Indian Penal Code (IPC) Based on Crime Reporting Data

Utilizing Machine Learning Techniques for Crime Classificati...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Ponnusamy Veeramalai Sankaradass HarshivKrishna M Sre Sandhya K Bharath Sampath Aashika B Dept. of Artificial Intelligence and Data Science Madha Engineering College Chennai Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Crime classification using the Indian Penal Code (IPC) is a common task at each police station. It involves complex decision-making related to crimes and respective IPC sections for different cases. The IPC is the foundational criminal code in India, containing a list of all substantive penal laws with definitions and punishments for each offense. Automating the crime classification is helpful due to the complexity and sheer volume of crimes reported daily. This work uses machine learning techniques to automate crime classification based on IPC. To serve this purpose, this paper utilizes the K-Nearest Neighbors algorithm. The proposed system accepts an input crime report and textually corroborates the report to an IPC section. For this intent, the system relies on the bag-of-words technique for identifying sections under IPC by scrutinizing repetition and availability of key terms in the crime to 98% accuracy in classifying crimes. Hence, the automated method is quite effective for mapping crime reports into the corresponding sections of the IPC. Also, because the system can easily be deployed online, it will be easy to integrate with existing workflows at the police stations. So, case filing and management would become more effective. This reduces the amount of manual effort and also brings consistency in classification; for this reason, the tool is of immense value to law enforcement agencies nationwide. The innovation in the paper is the development of a machine learning-based system using the K-Nearest Neighbors (KNN) algorithm to automate the classification of crimes under the Indian Penal Code (IPC) based on textual crime reports.

关键词： Technological innovation Codes Accuracy Machine learning algorithms Automation Law enforcement Machine learning Nearest neighbor methods Classification algorithms System implementation

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QoS Management in Multi-access Edge Compute 5

QoS Management in Multi-access Edge Compute

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5th International Conference on Computing Methodologies and Communication, ICCMC 2021

作者： Velrajan, Saravanan Sharmila, V. Ceronmani Hindustan Institute of Technology and Science Dept. of Computer Science Engineering Chennai India Hindustan Institute of Technology and Science Dept. of Information Technology Chennai India

ISBN: (纸本)9781665403603

Multi-access Edge Computing (MEC) is an integral part of 5G wireless networks by supporting higher speed and lower latency for users. 5G wireless network supports heterogenous applications such as Internet of Things (IoT), Private Wireless Networks, online gaming and remote healthcare, with well-defined Service Level Agreements (SLAs). QoS Management in MEC is critical for improving the user experience and for meeting the SLA targets of 5G services. While QoS management practices are well-established for applications hosted in the cloud, there is dearth of studies on QoS management in *** paper presents the survey of techniques used in QoS Management in MEC, reviewing the factors influencing QoS and the various performance indicators for QoS. This research work analyzes the existing QoS management approaches and discuss their advantages and limitations. The proposed research work highlights the need for further research to study QoS management models that leverage application characteristics and radio signal quality. The proposed study emphasizes the need for developing QoS management models, which can scale to large deployments of MEC nodes and support federated MEC clouds. © 2021 IEEE.

关键词： 5G mobile communication systems

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Developing a Python-Based QR Code Generation System

Developing a Python-Based QR Code Generation System

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Priyanka Kaushik Shamna Parveen S Shalu Sucheta Panda Swati Agarwal P. R. Sukanya Sridevi Dept. of CSE Chandigarh University Chandigarh India Dept. of CSE Dhanlaxmi Srinivasan College Of Engineering Coimbatore India Dept. of Computer S. & Technology Manav Rachna University Faridabad India Dept. of Computing and IT Usha Martin University Jharkhand India Dept. of Mathematics Usha Martin University Jharkhand India Dept. of Computer Science Meenakshi A.H.E.R Chennai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

This project entails importing necessary modules, creating the main window, and obtaining user inputs such as text or URL, the location to store the QR code, the name of the QR code, and its size. The paper delves into the underlying principles of QR code technology, covering aspects such as its structure, error correction capabilities, and applications across various industries. It highlights how Python serves as a powerful, flexible, and user-friendly tool for generating QR codes. This project is designed to be utilized by software developers, researchers, and enthusiasts, enabling them to gain a comprehensive understanding of QR code generation and to create their own customized QR code generation applications. By providing a detailed technical guide for QR code generation using Python, this research paper makes a significant contribution to the field of QR code technology.

关键词： Industries Uniform resource locators QR codes User interfaces Programming Generators Software Reliability Standards Python

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Design of Power Amplifier with Bandpass Filter

Design of Power Amplifier with Bandpass Filter

引用

2023 IET International Conference on Engineering Technologies and Applications, ICETA 2023

作者： Islam, Tanvir Yu, Zhe-Yu Chen, Rui-Peng Ting, Yi-Cheng Lai, Wen-Cheng Dept. of Electrical & Computer Eng. University of Houston HoustonTX77204 United States Dept. of Electronic Eng. Nat'l Formosa Univį of Sci. and Tech. Taiwan Dept. of Electronic Eng. Nat'l Taiwan Univ. of Sci. and Tech. Taipei Taiwan Dept. of Electrical Eng. Ming Chi University of Technology New Taipei City Taiwan

ISBN: (纸本)9781839539848

This article presents a 3.5 GHz power amplifier (PA) with 3th-order bandpass loop filter development that uses two stage cascade architecture. The two stages partition power amplifier with Gm-C band-pass filter is promulgated by the tsmc-0.18 um CMOS-processing with its the P1dB of -22.95 dBm, and conversion gain of 22.57 dB, respectively. Come out chipset arranges 20.72 W with a sourcing voltage of 1.8 V. © The Institution of Engineering & technology 2023.

关键词： Power amplifiers

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Enhancing Cabbage Plant Protection: CNN Algorithm for Insect Identification

Enhancing Cabbage Plant Protection: CNN Algorithm for Insect...

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Information technology Research and Innovation (ICITRI), International Conference on

作者： Remi Senjaya Purnawarman Musa Puji Sularsih Novrina Novrina Witari Aryunani Suhartini Suhartini dept. of Informatics Engineering Gunadarma University Depok Indonesia dept. of Electrical Engineering Gunadarma University Depok Indonesia dept. of Information Technology Gunadarma University Depok Indonesia dept. of Computer Science Gunadarma University Depok Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

People frequently find it straightforward to identify insects that feed on cabbage plants based on the insects’ characteristics. However, challenges arise when employing a computer for precise bug categorization and identification. The implementation of Deep Learning (DL) utilizes the Convolutional Neural Network (CNN) technique, renowned for its effectiveness in categorizing and recognizing objects in digital photo data. The CNN algorithm delivers notable results in photo identification, simulating the human visual brain’s image recognition through artificial neural networks in the Python programming language. To enrich the user experience, frameworks like Keras and TensorFlow are employed in neural network development. Our research will use the CNN algorithm to determine insect species in cabbage images. Achieving high accuracy involves coupling the CNN algorithm with the VGGNet architectural model. Notably, utilizing an epoch value of 150 resulted in a 97.75% accuracy rate during the training model technique; however, the validation accuracy was slightly lower at 94.25%. The corresponding loss and validation rates were 0.0613% and 0.2945%, respectively. Moreover, based on the model (150 epochs), the testing outcomes in the study, involving 48 images beyond the dataset and incorporating additional information, resulted in an accuracy rate of 91.66%.

关键词： Training Visualization Technological innovation Accuracy Insects Prediction algorithms User experience Classification algorithms Convolutional neural networks Testing

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Cascaded Deep CNN Network for Breast Cancer Screening from Ultrasound Images Using Differential Evolution Algorithm

Cascaded Deep CNN Network for Breast Cancer Screening from U...

引用

Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Wahidur Rahman Md Shadman Sakib Md Solaiman Hosen Mir Mohammad Azad Hani Alshahrani Adel Sulaiman Mana Saleh Al Reshan Asadullah Shaikh Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of Computer Science and Engineering University of Information Technology & Sciences Dhaka Bangladesh Department of Computer Science Iowa State University USA Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science College of Computer Science and Information Systems Najran University Saudi Arabia Emerging Technologies Research Lab (ETRL) College of Computer Science and Information Systems Najran University Saudi Arabia Department of Information Systems College of Computer Science and Information Systems Najran University Saudi Arabia

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

Because of poor blood flow and susceptibility to infections, breast cancer can be challenging to heal and, if not treated, might cause major problems, including limb amputation and a lowered quality of life. Though several systems exist to identify breast cancer, few combine machine learning (ML), deep learning (DL), and optimization strategies. This work presents a novel method leveraging complex algorithms to precisely identify breast cancer from ultrasound images. The study utilizes a dataset from a secondary source, categorizing it into three identical classes: normal (Class 0), benign (Class 1), and malignant (Class 2). It extracts features using pre-trained convolutional neural networks (CNNs), optimizes these features using the Differential Evolution Algorithm (DEA), and classifies images using standard machine learning algorithms. To show how well it detects breast cancer, the approach combines seven conventional ML classifiers with ten pre-trained CNN models. Using DenseNet121 + DEA + Xtreme Gradient Boosting (XGB) Classifier, DEA chooses important features derived by CNNs with a high accuracy of 97.76%. However, the proposed method can be very effective for real-time breast cancer screening using ultrasound images.

关键词： Limbs Ultrasonic imaging Machine learning algorithms Feature extraction Breast cancer Real-time systems Classification algorithms Convolutional neural networks Standards Optimization

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A Machine Learning and Data Analytics Approach to Patient Risk Stratification

A Machine Learning and Data Analytics Approach to Patient Ri...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Veeramalai Sankaradass V.K Manindra Manish Rajesh Anandhan Ramprakash Velmurugan Sabarinathan Sakthivel Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India Dhanalakshmi Srinivasan Institute of Medical Sciences and Hospital Perambalur India Dept. of Artificial Intelligence and Data Science Chennai Institute of Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

In this paper, we introduce a robust machine learning (ML) and data analytic solution for risk stratification of patients to promote clinical decision support systems (CDSS) that provide enhanced protection of the patient health interests. Our approach, which we have shown would minimally intrude into peoples' lives, uses predictive models to analyze patient data to identify high-risk patients early in the history of a wide range of medical conditions. Utilizing integrated structured data (e.g., lab results, vital signs) and unstructured data (e.g., clinical notes), our system improves the accuracy of medical decisions made by healthcare providers. We use a combination of supervised learning algorithms and real-time data processing to recalibrate risk scores, which serve as actionable insights for anticipatory interventions. The strategy also comes with extensive validation protocols to verify the efficacy of her model in a healthcare environment. Our results indicate that this practice dramatically increases the accuracy of risk assessments, with important implications for patient care and resource utilization.

关键词： Decision support systems Data analysis Accuracy Protocols Supervised learning Real-time systems Risk management Resource management Protection Random forests

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Machine Learnig Approach for Prediction of Employee Salary using Demographic Information with Experience

Machine Learnig Approach for Prediction of Employee Salary u...

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Global Conference for Advancement in technology (GCAT)

作者： Babasaheb S. Satpute Raghav Yadav Pramod K. Yadav Dept. of Computer Science and Engg School of Engg. & Technology JAIN (Deemed to be University) Bangalore India Dept. of Computer Science & IT FET VIAET SHUATS Prayagraj India Dept. of Computational Biology and Bioinofrmatics JIBBSHUATS Prayagraj India

In the realm of workforce dynamics and income inequality, the accurate prediction of employee salaries serves as a vital endeavor to foster understanding and address disparities. This research paper delves into the development and implementation of a machine learning system aimed at predicting employee salaries. The dataset employed in this study encompasses a comprehensive collection of salary and demographic information, intricately intertwined with years of *** dataset’s heterogeneous attributes encompass a spectrum of socio-demographic factors, including age, gender, education, country, and race. This confluence of variables provides an expansive landscape for analysis, enabling researchers to unearth intricate patterns and trends in income distribution across diverse demographic categories. Such exploration facilitates the identification of potential inequalities or variations in earning potential, fostering insights into the interplay between socio-demographic factors and *** distinct feature of incorporating years of experience within the dataset introduces an additional dimension to the analysis. This dynamic facet empowers researchers to investigate the interrelation between professional tenure and salary levels. By evaluating how income evolves with both demographic characteristics and accumulated work experience, the study offers a holistic perspective on income diversity within today’s *** core objective of this research paper is to devise and implement machine learning algorithms capable of accurately predicting employee salaries based on the amalgamation of socio-demographic attributes and professional experience. Through rigorous analysis and model development, the paper endeavors to unveil the intricate web of factors influencing earning potential. The outcomes of this study hold significant implications for policy makers, businesses, and stakeholders by shedding light on the multifaceted determinants of income dispa

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