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Penetration Testing: A Cost-Benefit Analysis of Best Practices Implementation for Software Startups 6

Penetration Testing: A Cost-Benefit Analysis of Best Practic...

引用

6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Gaafar, Ahmed Ali Fouad, Khaled M. Sadek, Mohamed A. Nile University Faculty of Information Technology and Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Egypt New Mansoura University Faculty of Computer Science and Engineering Egypt Nile University Egypt

ISBN: (纸本)9798350378511

Despite software startups often not handlingsensitive data, the implementation of robust security measures is crucial to mitigate significant financial and reputational risks. This study investigates the cost-benefit analysis of implementing best practices in penetration testing (Pentest) versus notimplementing them, using Roboost as a case study. It emphasizes that proactive security investments not only protect current assets but also prepare organizations for future growthThe research employs a mixed-methods approach, combining quantitative analysis of financial data with qualitative insights from stakeholder interviews. The findings indicate that while the initial costs of hiring penetration testers and implementing security protocols are substantial, the benefits far outweigh these expenses. Securing new clients and retaining existing ones, which constitute a significant portion of Roboost' s revenue, underscores the financial prudence of these investments. Our analysis demonstrates that the costs of not implementing security practices, such as potential losses from security breaches, are substantially higher than the investments in proactive measures. This underscores the critical importance of embedding security best practices within organizational frameworks to ensure long-term sustainability and success. © 2024 IEEE.

关键词： Decentralized finance

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Towards Intelligent Attendance Monitoring for Scalable Organization with Hybrid Model Using Deep Learning

Towards Intelligent Attendance Monitoring for Scalable Organ...

引用

International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Srivastava, Akhilesh Kumar Mishra, Chandrahas Mishra, Anurag Srivastava, Atul Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science KIET Group of Institutions Ghaziabad India Amity School of Engineering and Technology AUUP Lucknow India

ISBN: (纸本)9789819706402

Face recognition technology using AI has seen paradigm shift in the evolving world. Automatic attendance monitoring using real-time face identification is a solution to handle attendance in any small/large as well as scalable organization. Traditional methods in the organization involve calling names or signing sheets with individuals, which is a very time-consuming process and provides insurance. This is also subjected to manual errors. Automation of attendance recording and monitoring through face recognition is a process of identifying the face for taking attendance by using the image of the human face as biometric parameter captured through a surveillance camera in the premise. This article presents an effective way of attendance monitoring by making use of deep learning technology and compares its results with the state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Synergistic Fusion of Steganography and Cryptography in Medical Imaging: A Comparative Analysis to Enhance Image Security 4th

Synergistic Fusion of Steganography and Cryptography in M...

引用

4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Ghosh, Sharmila Saha, Ashim Pal, Tannistha Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (纸本)9789819751990

Image steganography is a technique for encrypting data, allowing covert communication, and enhancing data security inside image files. This study provides an in-depth analysis of image steganography, covering its core ideas, methods, uses, difficulties, and developments. Numerous embedding algorithms and methodologies, including transform domain methods and LSB-based strategies, are examined in this study. The paper highlights the need for effective defenses against steganalysis attacks, while also discussing the challenges and potential weaknesses in steganography. It examines common steganography techniques and discusses countermeasures such as adaptive embedding, key-based embedding, and algorithmic resistance. Additionally, the paper also highlights the significance of assessing the efficacy and caliber of image steganography techniques. Objective metrics such as PSNR and MSE are discussed, providing insights into the trade-offs between security and image fidelity. For those interested in understanding image steganography, its applications, and its challenges, this study offers a complete resource. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Analysing for Impact of Neutral Sentiments for Enhancing Quality of Services 2

Analysing for Impact of Neutral Sentiments for Enhancing Qua...

引用

2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Ranjan, Roop Singh, Ranjeet Shukla, Roshan Buddha Institute of Technology Department of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Sentiment analysis (SA) is an intellectual process of extracting a user's views. Social media is gaining prominence in public transit agencies for communication strategies and daily management. In recent years, web documents are identified as an important source of reviewing individual opinions. Researchers using these social networking platforms are producing a high rate of increase in interest in methods for automatically extracting and analysing opinions from web documents such as for weblogs, customer reviews, movies and public services etc. This paper is an attempt to perform an evaluation of public opinion for the Quality of Services of two very popular public transportations in North India. Opinions of public have been collected in form of tweets for a specific time. An approach based on fuzzy logic and machine learning is introduced to solve the tweets extraction that is used for classification. The impact of neutral tweets is discussed. Results show that the performance of road transport is better than the transport through railway services and neutral tweets play a significant role in emotion analysis of the system. © 2024 IEEE.

关键词： Tweets

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Quantum Transfer Learning Algorithm Based on Parameterized Quantum Circuit 5

Quantum Transfer Learning Algorithm Based on Parameterized Q...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Zhang, Shiyue Wang, Aijuan College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9798350385557

This paper proposes a quantum transfer learning (QTL) algorithm that combines a transfer learning (TL) algorithm with parameterized quantum circuits (PQC) to investigate the relationship between PQC and model performance in hybrid quantum classical neural networks (HQCNN). The QTL algorithm effectively improves training speed compared to current quantum algorithms, facilitating a more effective exploration of the relationship between PQC and HQCNN. Experimental results show that the expressibility of PQCs is closely related to circuit structure, overlap depth, and the number of qubits. The all-circuits and annular-circuits (APQC) demonstrate better expressibility. Additionally, the expressibility of annular-circuits strongly correlates with the accuracy of the QTL model. The paper also explores the impact of pre-trained models on QTL performance. Ultimately, the pre-trained model ResNet18 and the quantum classifier based on APQC are chosen to form the QTL model. In terms of training time, QTL completes 20 epochs in 4 hours, saving 90% of time compared to general QCHNN. © 2024 IEEE.

关键词： Electrocardiograms

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Deep Neural Networks for Diagnosis of Parkinson's Disease Using Replicated Acoustic Features 1

Deep Neural Networks for Diagnosis of Parkinson's Disease Us...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Likhitha, Janga Sai Shahid, Afzal Hussain Reddy, Anamareddy Sravann School of Computer Science and Engineering Vellore Institute of Technology Amaravati India

ISBN: (纸本)9798350353648

Parkinson's disease is a neurodegenerative disease that affects a person's non-motor and motor functions causing tremors, stiffness, difficulty in moving, fatigue, anxiety, sleep disorders, etc. This condition happens because of the absence of dopamine-producing cells in the brain, and its symptoms worsen over time. This paper proposes a method for diagnosing Parkinson's disease using replicated acoustic features. This paper uses feature selection algorithms namely Fisher's, mRMR, Boruta, and ANOVA (Analysis of Variance) to find out the most discriminative features, and these features were fed into Random Forest (RF) and Deep neural networks (DNNs) to diagnose Parkinson's disease. 99.17% is the highest accuracy is achieved with the DNNs with Boruta and DNNs with ANOVA models. This is a significant improvement compared to previous studies. This result suggests that the replicated acoustic features can be used to diagnose Parkinson's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Customer Segmentation using Clustering Techniques: Data-Driven Approach to Enhance Marketing Strategy 5

Customer Segmentation using Clustering Techniques: Data-Driv...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Sakthi Priya, G. Vignesh Saravanan, K. Hemalatha, B. Karthika, R. Kavya Srinidhi, B. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

Customer segmentation is a vital strategy for businesses seeking to enhance their marketing efforts and optimize resource allocation. This research focuses on segmenting customers using the Mall Customer Segmentation dataset, which includes demographic and behavioral attributes such as age, gender, annual income, and spending score. The work begins with data preprocessing, involving the handling of missing values, encoding of categorical variables, and scaling of numerical features to prepare the data for analysis. Following preprocessing, exploratory data analysis (EDA) is conducted to visualize feature distributions and relationships, providing insights into the data's structure. The core of the research involves applying various clustering algorithms-K-Means, Hierarchical Clustering, and DBSCAN-to identify distinct customer segments. The optimal number of clusters is determined through methods like the Elbow Method and Silhouette Score. The clusters are then evaluated for quality, and their characteristics are analyzed to understand different customer segments. This segmentation enables businesses to their marketing strategies, enhance customer satisfaction, and make data-driven decisions based on customer behavior insights and demographics. © 2024 IEEE.

关键词： Sales

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Epilepsy Seizure Detection based on EEG Signals using Machine Learning 3

Epilepsy Seizure Detection based on EEG Signals using Machin...

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Goel, Aadya School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798331515911

Seizure detection is a critical aspect of epilepsy management, and accurate identification can significantly improve patient outcomes. This study explores the application of machine learning techniques, specifically the Random Forest classifier, for the detection of seizures using electroencephalogram (EEG) data. By employing a systematic approach that includes feature extraction and channel reduction, we enhanced the model's performance. Relevant features were extracted from EEG signals, including time-domain statistics and frequency-domain characteristics, which are vital for effective classification. The model was evaluated using standard classification metrics such as precision, recall, F1-score, and accuracy, yielding high performance, particularly in identifying non-seizure events. These findings highlight the potential of ensemble learning methods for real-time seizure monitoring and underscore the importance of this research in developing reliable diagnostic tools. Overall, our results demonstrate that machine learning approaches can significantly contribute to improved patient outcomes in epilepsy management, paving the way for future advancements in clinical applicability. © 2025 IEEE.

关键词： Electroencephalography

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SSA-LSTM High-Speed Railway Track Irregularity Prediction Model Based on Dual-Stage Decomposition 2

SSA-LSTM High-Speed Railway Track Irregularity Prediction Mo...

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2nd International Conference on Informatics, Networking, and Computing, ICINC 2023

作者： Wang, Chongke Lu, Xiaofeng Li, Gaoxiang Wu, Chen School of Computer Science and Engineering Xi’an University of Technology Xi'an China

ISBN: (纸本)9781510674745

The smoothness of high-speed railway tracks is an important indicator for judging the quality of railway tracks. Accurately predicting the uneven trend of the track and dealing with it in advance is of great significance to the safe operation of high-speed railways. According to the data characteristics of specific road sections, this paper proposes a SSA-LSTM model based on dual-stage decomposition to predict track irregularity of high-speed railways. First, in order to eliminate the influence of noise in the original data, Singular Spectrum Analysis (SSA) is used to decompose, denoise, and reconstruct the original data. Secondly, in view of the existence of multiple modal mixing effects in the denoised data, the data is decomposed through the ensemble empirical mode decomposition (EEMD) method. Then, to solve the problem that the long short-term memory network (LSTM) hyper-parameters are difficult to effectively determine, the Sparrow Search Algorithm (SSA) is used to search and optimize. Finally, the optimized LSTM model is used to predict and reintegrate the decomposed multiple sequences. Experimental results show that the SSA-LSTM prediction model based on dual-stage decomposition proposed in this article can obtain more accurate prediction results. © 2024 SPIE.

关键词： Empirical mode decomposition

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Broken Rotor Bar Fault Detection in Induction Machines Based on Rectified Three-Phase Stator Currents 10

Broken Rotor Bar Fault Detection in Induction Machines Based...

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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Hajnayeb, Saeed Amerian, Fatemeh Marzebali, Mohammad Hoseintabar Abolghasemi, Vahid Seyyedbarzegar, Seyyed Meysam Shahrood University of Technology Faculty of Electrical Engineering Department Shahrood Iran School of Computer Science and Electronic Engineering Department University of ESSEX Colchester United Kingdom

ISBN: (纸本)9798331532543

Induction machines are widely used in industrial and household applications due to their efficiency in electromechanical energy conversion. However, broken rotor bars can significantly disrupt machine performance, typically manifesting as sideband harmonics near the fundamental frequency which can be utilized for fault detection. Nonetheless, frequency leakage can impede fault detection, concealing fault until substantial damage occurs. The proposed method, based on the rectified three-phase stator current (RTPSC), effectively reduces leakage frequency while maintaining the fault characteristic frequency (FCF) at its original amplitude. In addition, generate the fundamental harmonic at a frequency of 6 times. This approach aims to is to enhance diagnostic capabilities in the maintenance and monitoring of induction machines, improving operational efficiency and it also reduces the chance of catastrophic failures. The proposed methodology is tested on cases involving one, two, and three broken rotor bars, with the simulation results validated through experimental verification. © 2024 IEEE.

关键词： Stators

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