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A hybrid convolutional neural network and support vector machine classifier for Amharic character recognition

Neural Computing and Applications 2024年第27期36卷 16839-16856页

作者： Tsegaye, Muluken Zemed Shashi, Mogalla Department of Information Technology Andhra University College of Engineering AP Visakhapatnam530003 India Department of Computer Science Andhra University College of Engineering AP Visakhapatnam530003 India

Optical character recognition is a way of converting scanned images of printed or handwritten documents into machine-encoded text, making it easier to store, browse, retrieve, and process electronic data. In this research, a Printed Amharic Characters Recognition dataset is prepared to train and test a model. Images in the dataset only contain 231 basic Amharic characters that are normalized to 32 × 32 pixels. In this work, a hybrid model of the two super classifiers is developed: the convolutional neural network (CNN) and the support vector machine (SVM). In this novel hybrid CNN-SVM model, CNN works as an automatic feature extractor from the raw images, and then, the extracted feature vectors are given as input to SVM for classification and recognition. A 99.84% accuracy was achieved on the own-prepared dataset and 95.59% accuracy on the benchmark Amharic Optical Character Recognition Database in classifying the testing dataset images. The proposed hybrid CNN-SVM model gave better results than the CNN with a fully connected layer. Moreover, the proposed model outperforms previously existing works attempted by others to recognize printed Amharic characters on the same and different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

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HMM-Based Secure Framework for Optical Fog Devices in the Optical Fog/Cloud Network

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Journal of Optical Communications 2023年第4期44卷 475-483页

作者： Sood, Sandeep K. Deep Singh, Kiran Department of Computer Science & Engineering Khalsa College of Engineering & Technology Amritsar India Department of Computer Science & Engineering Guru Nanak Dev University Regional Campus Punjab Gurdaspur India

In the present optical fog/cloud computing environment, optical line terminals and optical network units are used as the most promising optical fog devices (OFDs). The inherent characteristics of fog computing provide certain granted privileges to the attacker to hack devices and make them malicious. Also, existing security solutions generate false alarms that affect the performance of the underlying network. In this paper, a secure framework is proposed that not only predicts the malicious OFDs but also reduces false alarms. Hidden Markov model and intrusion detection system are used to detect the malicious one by computing the probability of shifting (POS) and then shift it to the virtual honeypot which is kept hidden by deploying it at the optical fog layer. In addition, it also reduces the generation of false alarm and logs all malicious activities for further analysis. In the experiment section, Python is used to validate the proposed framework. Further, HMM is simulated and tested in the MATLAB to reduce the false alarm rate. Results show that the proposed framework effectively reduces the false alarms and detects the malicious one and then shifts it onto the virtual honeypot efficiently. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Fog

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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Customer Feedback and Sentiment Analysis for Hotel Services 2

Customer Feedback and Sentiment Analysis for Hotel Services

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Abhyudhay, S.R. Aditya, G.M. Upadya, Achyuta K. Naik, Archana Ushashree, P. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India

ISBN: (纸本)9798350382952

In the era of digital transformation, the hospitality industry faces unique challenges and opportunities. With travellers increasingly relying on online reviews to make informed decisions, the role of sentiment analysis in the hotel sector has never been more critical. This comprehensive research aims to provide a profound understanding of sentiment analysis in the context of hotel reviews and delves into preprocessing and the application of state-of-the-art sentiment analysis techniques. The sentiment analysis model, based on SVM, achieved promising results in categorizing hotel reviews into positive, negative sentiments. The evaluation metrics indicate an accuracy of 82%. Visualizations such as the confusion matrix and ROC curve further demonstrate the model's effectiveness in distinguishing between positive and negative sentiments. The findings of this research shed light on the distribution of sentiments in hotel reviews, providing actionable insights for the hospitality industry. © 2024 IEEE.

关键词： Sentiment analysis

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Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

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Intelligent Automation & Soft Computing 2023年第3期35卷 3845-3860页

作者： P.N.Ramesh S.Kannimuthu Computer Science and Engineering Karpagam Institute of TechnologyCoimbatore641105India Information Technology Karpagam College of EngineeringCoimbatore641032India

The use of programming online judges(POJs)has risen dramatically in recent years,owing to the fact that the auto-evaluation of codes during practice motivates students to learn *** POJs have greater number of pro-gramming problems in their repository,learners experience information *** systems are a common solution to information *** recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’current context,like learning goals and current skill level(topic knowledge and difﬁculty level).To overcome the issue,we propose a context-aware practice problem recommender system based on learners’skill level navigation *** system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and toﬁnd learners’learning ***ﬁltering(CF)and con-tent-basedﬁltering approaches are employed to recommend problems in the cur-rent and next skill levels based on frequent skill level navigation *** sequence similarity measure is used toﬁnd the top k neighbors based on the sequence of problems solved by the *** experiment results based on the real-world POJ dataset show that our approach considering the learners’cur-rent skill level and learning goals outperforms the other approaches in practice problem recommender systems.

关键词： Recommender systems skill level navigation pattern programming online judge collaborativeﬁltering content-basedﬁltering

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MindCare: Machine Learning based Personalized Mental Health Monitor 4

MindCare: Machine Learning based Personalized Mental Health ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ushashree, P. Naik, Archana Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331540364

Mental health challenges are growing in recent years, emphasizing the need for effective monitoring and inter- vention systems. This paper presents a comprehensive approach to detect mental unstabilities by analyzing the text entered by the user. Utilizing a combination of sentiment analysis and machine learning techniques, the proposed system preprocesses text data to extract sentiment scores and other key features, which are then used to train models for accurate mental health assessments. The system also offers personalized recommendations based on the user's mental state. Experimental results demonstrate the effectiveness of the approach, with the model achieving accuracy of 82.31% for negative text classification. These results underscore the potential of the system to serve as a valuable tool in the early detection and management of mental health issues. © 2024 IEEE.

关键词： Electronic health record

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Synthetic Data Generation for Document Text Recognition 1

Synthetic Data Generation for Document Text Recognition

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Ashlin Deepa, N.R. Srija, Boda Jabeen, Maria Kankar, Kushi Reddy Lakshmi, Bollepalli Jhansi Vijayalata, Y. Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Hyderabad India Vardhaman College of Engineering Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350361780

Handwritten text recognition software is used to recognize and extract text from scanned documents. The fun-damental goal of this technology is to transform printed or handwritten text into an easily readable electronic format. The numerous characters and enormous quantity of the information for Indian languages, however, causes explicit preprocessing to take time. This necessity for explicit preprocessing has been replaced by synthetic data creation techniques, which overcome several difficulties and speed up the process. Artificially produced data that closely mimics actual observations is referred to as synthetic data. In situations where getting real data is difficult or expensive, it offers a workable substitute for training machine learning models. In this work, we propose a data preprocessing technique that creates synthetic data files from the already-existing collection of Indian languages. We use a pre-trained language model called FastText model, which is capable of creating word embeddings, to generate synthetic dataset from the real-time dataset. With the aid of the generated synthetic datasets, document text recognition systems can undergo extensive training and testing, allowing them to grasp the complexities of Indian languages and perform accurate text extraction from scanned documents. © 2024 IEEE.

关键词： Embeddings

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Voice Controlled Smart Home System 2

Voice Controlled Smart Home System

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Manjula, K. Dhananjaya, V. Computer Science & Engineering Nitte Meenakshi Institute of Technology Bangalore India Computer Science & Engineering Impact College of Engineering Bangalore India

ISBN: (纸本)9798350372892

The Internet of Things (IoT) is a significant technological advancement that uses the internet to enable seamless communication between various devices, allowing people and objects to connect anytime and from anywhere. In this research, we present an intelligent smart automation model that can remotely operate household appliances and water the garden plants from anywhere in the world. 'Home automation' is a quickly emerging technology that minimizes the work that humans must perform. Certain home automation systems are offered to people seeking luxurious, advanced home automation systems. This method is safe because it works best for the elderly and disabled people. However, certain people could find it more challenging to use certain systems because of their special demands. The system's primary features are voice recognition and intelligent interaction. Our main objective is to develop an advanced home automation system that has combined Alexa with the ESP 8266 module to improve security, reduce power consumption and minimize user effort. The model includes two essential sensors, a soil moisture sensor, a temperature and humidity sensor. All hardware components in this model are cost-effective, readily available, and easily replaceable. © 2024 IEEE.

关键词： Soil moisture

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Classification of System Failures in Cyber-Physical Systems Using Heterogeneous Edge Enhanced Graph Attention Networks 3

Classification of System Failures in Cyber-Physical Systems ...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Obidhusin, Saef Chandra, N. Subhash Balamurugan, D. Premalatha, J. Kumararaja, K. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Cvr College of Engineering Department of Computer Science and Engineering Hyderabad India Sona College of Technology Department of Computer Science and Engineering Salem India Dhanalakshmi Srinivasan College Of Engineering Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798331508456

From the past few years, Cyber-Physical Systems (CPS) have rapidly evolved in various sectors by integrating computing, networking, and physical processes. CPS facilitates the management of critical infrastructure such as transportation, healthcare, and energy. Even though there are advanced technologies, which are vulnerable to failures led to severe consequences including unpredictable interactions between cyber and physical components. Therefore, to address these issues a robust model is proposed by using Heterogeneous Edge-Enhanced Graph Attention Network (HEE-GAT). Initially, the data is collected from BoT-IoT database which consists of huge traffic data. Then, the collected data is preprocessed using Z-score normalization is applied to standardize feature distributions while preserving network traffic characteristics. Furthermore, for selection of significant features Skill Optimization Algorithm (SOA) is utilized. Finally, HEE-GAT is utilized for effective classification of system failures in CPS. Unlike existing failure detection models such as LSTM-GNN hybrids, which struggle with capturing heterogeneous dependencies and dynamic edge relationships in CPS data, HEE-GAT enhances graph-based learning by assigning dynamic edge weights and utilizing attention mechanisms to distinguish between different failure types with higher accuracy. The proposed HEE-GAT model acquired accuracy of 95% when compared to the existing Fractional Artificial Protozoa Optimization enabled Spiking VGG-16 (FAPO Spiking VGG-16). © 2025 IEEE.

关键词： Service oriented architecture (SOA)

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Features enhancement of micro-service based cab booking system using block-chain 4th

Features enhancement of micro-service based cab booking syst...

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Rohit Singh, Neelaksh Shukla, Riya Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj India

ISBN: (纸本)9781032911571

Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, March 18, 2021). It has a micro-service based architecture (Li et al. 2021). Load balancing (Al-Jaroodi et al. 2012) and stream processing (Besnard et al. 2013) are used to handle a large number of requests. The workload is divided among servers using Dispatch Optimization (Abid et al. 2022). It ensures fast responses, easy connection minimal downtime, and efficient use of resources. However, the use of servers increases the cost of service. Rider and driver depend on the server to connect. They don’t know if the system is charging them fairly. It reduces transparency and trust. The paper proposes a blockchain-based cab booking application to solve some of the problems. Trust, transparency, and decentralization can be ensured using blockchain. Transparent and immutable ledgers of blockchain ensure trust and reduce reliance on intermediaries. Smart contracts govern ride management, optimizing efficiency. Blockchain capabilities are used to ensure fair rides, direct interaction of passengers with drivers, and automated payments. The proposed system aims at combining existing technology with blockchain solution to ensure security and transparency along with scalability and quick response time. © 2025 the Author(s).

关键词： Reservation systems

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