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A novel context-sensitive attitude entropy-based multiclass segmentation method for brain MR images using enhanced flow directional algorithm

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Multimedia Tools and Applications 2025年第15期84卷 14759-14803页

作者： Naik, Manoj Kumar Jena, Bibekananda Panda, Rutuparna Wunnava, Aneesh Abraham, Ajith Faculty of Engineering and Technology Siksha O Anusandhan Odisha Bhubaneswar751030 India Department of Electronics and Communication Engineering Anil Neerukonda Institute of Technology & Sciences Andhra Pradesh Sangivalasa Visakhapatnam531162 India Department of Electronics and Telecommunication Engineering Veer Surendra Sai University of Technology Odisha Burla768018 India Faculty of Computing and Mathematical Sciences Bennett University Greater Noida201310 India

Computer vision techniques, aided by artificial intelligence, in medical image analysis, are currently encouraged for precise and speedy diagnosis of brain disorders. The brain MR image shows an intensity variation, which attracts multiclass segmentation for a meaningful representation. The existing entropy-based methods use the image histogram. Nevertheless, the 1D histogram-based technique has a low complexity. However, it lacks the contextual information. Further, these entropy-based methods suffer low accuracy when the gray value distribution is non-uniform. To facilitate a reliable and efficient thresholding process, a novel multiclass segmentation objective function is investigated along with an efficient optimizer to obtain the optimal threshold values. To bridge this research gap, we explore a context-sensitive attitude entropy (CSAE) based multiclass segmentation objective function, an energy curve is used together with the human decision-making ability. Context-sensitive attitude entropy (CSAE), in contrast to classical entropy, which considers each pixel separately, is a measure of uncertainty or randomness in an image considering the spatial relationships and context between adjacent pixels. This can be achieved by calculating the attitude entropy from the energy curve instead of the image’s histogram. To maximize the CSAE, we suggest a new enhanced flow directional algorithm (EFDA), which improves the flow directional algorithm (FDA) by adding randomization to both the velocity and the non-performer agents. The experimental effort begins with the EFDA performance validation utilising 23 well-known classical and 10 CEC-C06 2019 benchmark functions, which produce better results than cutting-edge optimizers. The EFDA’s supremacy over other optimizers is ensured by Friedman’s mean rank and Wilcoxon’s mean rank test. The suggested CSAE-EFDA method is tested using T2-weighted brain images from Harvard Medical School’s AANLIB database and Brain MR images for t

关键词： Decision making

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An Enhanced Framework for Churn Prediction using Stratifed Bagging and Stacked Multi-Output Random Forests 3

An Enhanced Framework for Churn Prediction using Stratifed B...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Bhardwaj, Anay Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Customer churn prediction is an important task in customer relationship management because it helps businesses know who is at risk of leaving and retain such at-risk *** and time-efficient churn prediction is essential to identify the factors driving churn, enabling businesses to implement timely and effective retention strategies.A framework is proposed which combines stratified bagging with multi-output Random Forest and stacked feature transformation, to enhance prediction accuracy. This approach balances the dataset, trains a custom Random Forest using stratified bagging, and applies stacked learning using Logistic Regression on transformed features. Tested on real-world data, the model demonstrated 99% accuracy, showing improvements over the classic Random Forest in precision, recall, and prediction time. © 2025 IEEE.

关键词： Sales

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Optimizing Hiring Practices: A Machine Learning Approach for Candidate Selection 3

Optimizing Hiring Practices: A Machine Learning Approach for...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dogiparthy, Naga Datta Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita school of computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Recruitment is one of the most crucial factors in shaping efficient and high-performing teams within organizations. However, the traditional recruitment processes are often plagued by biases and subjective judgments that lead to inconsistent and suboptimal hiring decisions. This project uses machine learning(ML) to make recruitment more objective, efficient, and data-driven. By taking the age, gender, education level, experience, interview scores, and personality traits of the applicants as key features, the model predicts candidate suitability without bias. The methodology uses robust preprocessing, Chi-Square feature selection, and the SMOTE technique to overcome class imbalance in the dataset. The proposed system uses various different machine learning algorithms like CatBoost, KD-Trees, Random Forest, and Logistic Regression and many more. All these models are trained and fine-tuned with GridSearchCV for hyperparameter optimization. Among them, the best model was CatBoost with 95% accuracy. This work shows how machine learning can change the recruitment game for organizations to make unbiased, data-driven hiring decisions and ultimately increase workforce efficiency and organizational success. © 2025 IEEE.

关键词： Adversarial machine learning

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Covert Cryptography in Pixel Realms: Unveiling Nonlinear-Encrypted Generative Adversarial Networks for Stealthy Image Steganography

Covert Cryptography in Pixel Realms: Unveiling Nonlinear-Enc...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Gahan, A.V. Devanagavi, Geetha D Reva University School of Electronics and Communication Engineering Bengaluru India Reva University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350309140

This article introduces an innovative grayscale image steganography approach competent of concealing confidential data within a carrier image of the corresponding dimensions. The method involves encrypting the confidential image using nonlinear encryption technology before embedding it. A convolutional neural network is then employed to transform the encrypted and carrier images into a concealed image. Furthermore, a GAN-generative adversarial network is utilized to generate a closer to reality concealed image, making it challenging to differentiate from the original carrier image. The steganography framework is constructed using three Convolutional Neural Networks acting as the concealment network, differentiative network, and retrieving network, correspondingly. Furthermore, a novel weight assignment process is initiated to verify a balanced development process for the concealment-retrieving networks. The empirical findings demonstrate that this suggested framework not just resolves issues related to covert data compromise and manipulation found in present dominant approaches as well maintains a ample concealing capability. Comparative analysis in conjunction with present methods shows that the mean Embedding rate, PSNR and Structural Similarity Index of the concealed images generated using this scheme reach 9.03, 30.982 and 0.844, respectively. The precision of the restoration of confidential images is notably superior. © 2024 IEEE.

关键词： Convolutional neural networks

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Credit Card Fraud Detection(CFD) using GNN and Ensemble Techniques 3

Credit Card Fraud Detection(CFD) using GNN and Ensemble Tech...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vinay, B.V.S.N.S. Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Credit card fraud is an essential problem in the economic industry;thus, its detection is solved with the help of the developed methods in order to minimize the overall loses and to improve the confidence of clients. Towards this end, this paper presents a novel framework that applies GNNs in combination with ensemble learning to accurately identify credit card fraud. GNNs are used for cardinality, representing various relationships between transactions, cardholders, and merchants where complicated types of forgery are identified and are beyond the capability of conventional approaches. Ensemble learning further improves the accuracy of the system in order to reduce high false positive rates by using multiple models to solve the problem of imbalanced classes that are characteristic of the COVID-19 data. The effectiveness of the proposed framework is thus compared with several state-of-the-art malicious account detection techniques and various highly relevant real credit card transaction datasets show the proposed framework attains better precision, recall, F1-score, scalability. Thus, this strategy is much more suitable for ongoing and unspecific problem addressing in an unobstructed and ambiguous environment, such as credit card fraud. © 2025 IEEE.

关键词： Crime

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Predicting Customer Lifetime Value in E-commerce: A Data-Driven Approach to Enhance Customer Retention Strategies 3

Predicting Customer Lifetime Value in E-commerce: A Data-Dri...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dey, Sonali Radha, D. Kirthika Devi, V.S. Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Predicting Customer Lifetime Value (CLV) is one of the most critical tasks that businesses undertake in order to improve customer retention and optimize marketing strategies. The present paper proposes a predictive model for CLV using several machine learning techniques. The features considered include customer demographics, purchasing behavior, and transaction history. All these features were preprocessed using feature engineering and missing value handling. Several regression models, including Linear Regression, Decision Tree, and Random Forest Regressor, were compared in terms of predictive performance. The study includes the decision trees,hash tables as advance data structures such that they can handle large datasets effectively. Of the ones tested, Random Forest Regressor proved to be superior to the others, having the lowest Mean Squared Error (MSE) and highest R2 score, thereby validating its robustness in identifying the complex patterns within the data. The work highlights the time and space complexities of different models. This study contributes to the application of machine learning in customer analytics and provides a foundation for future research to integrate more diverse data sources and enhance model performance through hyperparameter optimization. Future work will also explore real-time prediction capabilities and the integration of multimodal data to further refine CLV forecasting. © 2025 IEEE.

关键词： Sales

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DDoS Detection in SDN Environments: Leveraging Machine Learning Algorithms with Local Interpretable Model-Agnostic Explanations (LIME) for Enhanced Security

DDoS Detection in SDN Environments: Leveraging Machine Lear...

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International Conference on Business Intelligence and Data Analytics, BIDA 2024

作者： Karna, Siwani Jahanve, Poluru Reddy Yadukul, S. Bhaskaran, Sreebha Vekkot, Susmitha Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9789819777167

SDN, short for Software Defined Networking, represents a network architecture controlled by software applications. Distributed Denial-of-Service (DDoS) attacks create a challenge to these SDN environments and lead to many issues like loss of data and network instability, and takes a lot of time to address requests made by human users. It is very crucial to detect such attacks, for users to seamlessly use the network and take advantage of what is available online. This study demonstrates the significance of Machine Learning algorithms in identifying malicious traffic. Employing algorithms Gradient Boosting Classifier (GBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression (LR) proves effective in detecting complex and dynamic attacks. The study utilizes Local Interpretable Model-agnostic Explanation (LIME) to analyze the strength of each feature that contributes towards the prediction of classification. The results highlight the superior performance of the Gradient Boosting Classifier, achieving an accuracy of 99.75% and an F1-score of 99.68% in detecting network anomalies and threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Application programs

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Voltage Mode Sinusoidal Oscillator with Non-Interacting Adjustment of Parameters Employing VCII

Voltage Mode Sinusoidal Oscillator with Non-Interacting Adju...

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2024 International Conference on Computer, electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Arora, Tajinder Singh Tiwari, Sachin ZHCET Aligarh Muslim University Department of Electronics Engineering Aligarh India School of Computing MIT-ADT University Pune India

ISBN: (纸本)9798350388534

This article proposed a novel sinusoidal oscillator with quadrature voltage mode response employing a recently explored active element second-generation voltage conveyor (VCII). The derived oscillator configuration utilized three units of active device and four grounded/virtually grounded resistances along with two grounded capacitances. The adjustment between frequency of oscillation and condition of oscillation enjoys non-interactive controlling feature. Two distinct quadrature responses are available at the buffer ports of the device. The routine mathematical analysis includes the ideal analysis along with non-ideal, sensitivity, and parasitic parameters. The CMOS simulations of the proposed structure are carried out in a PSPICE environment with a TSMC 0.18μm parameter. Experimental verification for the same was performed by the commercially available chip AD844. © 2024 IEEE.

关键词： Electric grounding

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CGFSSO: the co-operative guidance factor based Salp Swarm Optimization algorithm for MPPT under partial shading conditions in photovoltaic systems

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International Journal of Information Technology (Singapore) 2024年 1-16页

作者： Raj, S. Antony Elakkiya, E. Rajmohan, Shathanaa Samuel, G. Giftson Electrical and Electronics Engineering St Ann’s College of Engineering and Technology Andhra Pradesh Chirala India Computer Science and Engineering SRM University Andhra Pradesh Amaravati India Ramanujan Computing Centre College of Engineering Guindy Campus Anna University Chennai India Electrical and Electronics Engineering Arifa Institute of Technology Nagapattinam India

The increasing adoption of solar photovoltaic (PV) power generation stems from its renewable and eco-friendly attributes. However, conventional Maximum Power Point Tracking (MPPT) methods encounter difficulties in efficiently harnessing power from PV systems under Partial Shading Conditions (PSC). During PSC, these systems exhibit fluctuating power outputs due to shading, leading to challenges in identifying the Global Maximum Power Point (GMPP). The presented research introduces a pioneering Co-Operative Guidance factor based Salp Swarm Optimization algorithm (CGFSSO) tailored for MPPT in PSC scenarios within PV systems. The CGFSSO method focuses on precise GMPP localization with minimized oscillations by enhancing the update mechanism and effectively exploring the expansive search space. To assess its efficacy, the proposed CGFSSO approach undergoes comparison against conventional MPPT techniques, Fuzzy logic and Optimization based MPPT methods through rigorous simulation studies. The results underscore the CGFSSO method's exceptional performance in precisely tracking the GMPP and improving MPPT power efficiency when contrasted with established methodologies. This study signifies a promising stride towards optimizing power extraction from PV systems operating under demanding partial shading conditions. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Complex partial shaded conditions Maximum Power Point Tracking Photovoltaic systems Renewable energy sources Salp Swarm Optimization

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A Hybrid Binary Image Encryption Strategy Integrating Phase Shifted Holography, Fourier Transform, and Arnold Cat Map

A Hybrid Binary Image Encryption Strategy Integrating Phase ...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Shrivastava, Shriyanshu Kumar, Nagendra Swami, Pragya Chatterjee, Amit ABV-IIITM Gwalior Department of Electrical and Electronics Engineering Gwalior474015 India NIT Jamshedpur Department of Electronics and Communication Engineering Jamshedpur831014 India School of Computing DIT University Dehradun248009 India

ISBN: (纸本)9798350379525

This work presents a hybrid binary image encryption strategy using virtual optics, phase shifted holography, Fourier transform based spectral conversion, and Arnold Cat Map based key hiding. The confidential information needed to be sent was converted to a binary image and encrypted using Mach-Zehnder configuration of phase shifted virtual holography, followed by Fourier based spectral conversion. Decryption was performed to recover the phase information from the frequency response of the deformed hologram. Towards this, all the fringe patterns had gone through inverse Fourier transform. Next, four step phase shifting algorithm was used for wrapped phase recovery, and line scan based strategy was used for unwrapping. Finally, by subtracting the reference phase map from unwrapped object phasemap, binary image was recovered. For transmitting the keys securely, Arnold Cat Map based scrambling was used. The simulation results conclusively establish the efficiency of the proposed strategy and safety against hack attacks. To analyze the overall information loss, mean square error was calculated. © 2024 IEEE.

关键词： Binary images

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