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5th International Ethical Hacking Conference, eHaCON 2024

作者： Chakraborty, Mohuya Palit, Sudip Kumar Cybersecurity Centre of Excellence Institute of Engineering and Management University of Engineering and Management Kolkata India Department of Computer Science and Engineering Institute of Engineering and Management University of Engineering and Management Kolkata India

ISBN: (纸本)9789819784561

This paper focuses on the security aspects of the Three-level Mobility Model (TLMM) in the next-generation networks. Prior research has indicated that hierarchical architectures outperform other network topologies in terms of handoff delay, location update, and signaling overhead in mobile networks. The optimality test of the time-bandwidth product proved the supremacy of the three-tiered architecture of mobile networks in literature. However, earlier works of TLMM have not addressed the security issues. Designing a security protocol for TLMM is therefore the goal of this research. Our solution to the current research gap is a lightweight blockchain-based security protocol that can be used for local, regional, and global mobility. The work’s uniqueness resides in its proposal of a protocol to establish a peer-to-peer network where users can communicate with the data stored in a secure distributed ledger, reducing the amount of duplicate information that users must transfer. The implementation of blockchain technology is done using Hyperledger Composer and Hyperledger Fabric. Burrows-Abadi–Needham (BAN) logic has been used in the formal security verification of the suggested system to provide mutual authentication among the participants. Furthermore, the suggested model performs noticeably better than the other current systems by providing thirteen distinct security characteristics, requiring less processing time (about 8 ms) and communication overhead (around 2832 bits). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile telecommunication systems

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Design of a Machine Learning based Data Analytics Engine to Improve Decision Making Processes 5

Design of a Machine Learning based Data Analytics Engine to ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Pethe, Yoginee Surendra Dandekar, Pranali Rahul Shri Ramdeobaba College of Engineering and Management Department of Computer Application Nagpur India Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9781665474672

Analytical Hierarchy Processing (AHP) assists in estimation of input-specific weights for improving system-level decision making operations. The process initially decides a goal, and then uses different criterion & alternatives to achieve it. But original AHP model is not efficient for large-scale systems, which limits its capability under real-time use cases. To overcome this issue, various Machine Learning based techniques are proposed, and each of them has their own set of limitations. Techniques with higher efficiency also showcase high complexity, while simplistic optimization techniques have limited performance levels. To overcome these limitations, this text proposes design of an efficient AHP model with low complexity via integration of (GWO), and iterative learning (IL) operations. The proposed model initially performs context-based correlation between input parameter sets. These correlation values are used to modify the AHP decisions, which are tuned by GWO process. Results of Grey Wolf optimization this GWO process are optimized via IL based tuning, which incrementally modifies the correlation values for optimum efficiency levels. Due to which the proposed model showcases low complexity, while maintaining higher efficiency when compared with the original AHP process. The proposed model was evaluated on multiple datasets including housing, image processing, and employment scenarios. Based on this evaluation it was observed that the proposed model showcased 8.5% higher accuracy, 6.4% higher precision, with 4.5% 1ower delay when compared with original AHP and standard AHP-optimization techniques. Due to this improvement, the proposed model is capable of deployment for a wide variety of real-time scenarios. © 2023 IEEE.

关键词： Machine learning

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application of hybrid capsule network model for malaria parasite detection on microscopic blood smear images

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Multimedia Tools and applications 2025年第7期84卷 3459-3485页

作者： Kumar, S. Aanjan Muchahari, Monoj Kumar Poonkuntran, S. Kumar, L. Sathish Dhanaraj, Rajesh Kumar Karthikeyan, P. Department of Computer Application and Science Institute of Engineering and Management West Bengal Kolkata India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Department of Electronics & Communication Engineering Velammal College of Engineering & Technology Tamil Nadu Madurai625009 India

Today, malaria is a dangerous disease caused by Plasmodium parasites and transmitted by the bite of Anopheles mosquitoes. It is estimated that more than 200,000 cases of malaria will occur every day worldwide, and more than 400,000 people will die from malaria. Early diagnosis of malaria is important to mitigate the morbidity and mortality of the disease. In this paper, a fully trained capsule neural network combined with a convolutional neural network model is proposed for malaria detection from blood smear microscopic images. In malaria diagnosis, the hybrid CapsNet model can detect malaria in blood samples. The hybrid model is optimized based on malaria data in microscopic images of infected and uninfected blood. Images are processed and enhanced through rotation before being fed into the hybrid CapsNet model. The CapsNet hybrid model is examined for malaria data with a learning rate of 0.07 and a batch size of 20. Hybrid CapsNet model shows improved results in detecting malaria from microscopic images measured against the currently available standard malaria test. Constraints like malaria parasite detection rate, accuracy, F1 index and blood smear classification is used to gauge the attainment of the hybrid CapsNet model. Detection rate of 99%, accuracy of 99.08% and FAR of 0.97% is attained by the hybrid CapsNet model as contrary to the traditional state-of-the-art model, the DSCN-Net, where the detection rate was 98%, the accuracy was 97.2%, and the FAR was 0.99%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diseases

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MLTPDFS: Design of Machine Learning Model for Temporal Analysis of Progressive Diseases via Facial Scans 5th

MLTPDFS: Design of Machine Learning Model for Temporal Analy...

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5th International Conference on Information Systems and management Science, ISMS 2022

作者： Dandekar, Pranali Rahul Pethe, Yoginee Surendra Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Application Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9783031311529

Edge analysis, colour variation analysis, texture change analysis, etc. can identify multiple health conditions in facial scans. Identifying these diseases requires individual image processing models. Temporal facial analysis can identify stress, anxiety, happiness, high blood pressure, diabetes, and other health conditions. Most of these models use independent Machine Learning (ML) components for disease identification, limiting real-time scalability. Some of these models combine ML components, but their clinical prediction and analysis is limited. This text proposes a novel Machine Learning Model for Temporal analysis of progressive diseases via Facial Scans. The proposed model trains CNNs to identify Stress, Anxiety, Heart Conditions, Cancer, and Brain abnormalities. Each disease type requires its own CNN Model. VGGNet-19 predicts stress and anxiety, YoLOV3 identifies brain abnormalities, ResNet-101 predicts heart conditions, and ResNet-152 identifies cancer levels. These models were chosen for their accuracy, precision, and AUC for different diseases. The proposed model combines CNNs using bio-inspired machine learning. The combined model’s hyper-parameters are tuned based on real-time performance metrics. An incremental learning layer improves these metrics by incorporating continuous learning. Due to these operations, the proposed model improves classification accuracy by 5.4%, precision by 6.5%, recall by 8.3%, and AUC by 4.5%. The model performed faster than existing methods due to machine learning, which reduces training and validation delays, making it useful for real-time clinical use cases. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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Unsupervised Bearing Fault Diagnosis Method for Complex Electromechanical Hydraulic System 4

Unsupervised Bearing Fault Diagnosis Method for Complex Elec...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： He, Zizhou Shen, Wenwen Li, Suicheng Yuan, Tianyou Zhou, Xiaowei Ni, Zhigao Institute of Computer Application CAEP Sichuan Province Department of Computer Engineering and Science China

ISBN: (纸本)9798350324303

Finding the optimal combination of feature extraction and classifier has always been a hot issue in intelligent health monitoring. Traditional artificial features are difficult to achieve the ability of deep learning to mine deep abstract features of signals. However, compared with ensemble learning models, deep learning classifiers have the problems of easy overfitting, poor generalization performance, and the need for a large number of labeled samples. Autoencoder has the advantage of unsupervised feature extraction, LightGBM has the advantage of feature selection, so this paper proposes a fault diagnosis method combining deep convolutional sparse autoencoder with LightGBM, which has the advantages of unsupervised feature extraction and distributed gradient boosting. Experiments are carried out on the CWRU bearing dataset to verify the superiority of the proposed method. © 2023 IEEE.

关键词： Feature extraction

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Multimodal Attention CNN for Human Emotion Recognition 1

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

作者： Tiwary, Gyanendra Chauhan, Shivani Goyal, Krishan Kumar Department of Computer Science and Engineering Bhagwant University Ajmer India Department of Computer Application Raja Balwant Singh Management Technical Campus Agra India

ISBN: (数字)9789819706419

ISBN: (纸本)9789819706402

The human face is the mirror of the mind. The face generally tells all that is going on in one’s heart and mind. Just by looking at the faces of our known ones, we may easily guess their mood. But many times, when we meet some unfamiliar person, it’s hard to get his or her mood just by looking at their faces. This is just because the person may have a certain facial structure that makes them by default look angry, happy, or sad. So, we need to spend some time with that person to analyse other parameters before concluding their state of mood. The current work proposed a novel approach that integrates facial images with electroencephalography (EEG) signals for facial expression recognition tasks. When attention-based deep CNN analyses the facial traits of the subject, a parallel Long Short-Term Memory (LSTM) network analyses the EEG signals. A late fusion network combines the features extracted from both networks, and finally, a classification network tells about what is the current mood of the subject. Combining multiple modalities for emotion recognition has shown promising results when compared with other state-of-the-art models. There are multiple real-life applications of emotion recognition models, such as Advertisement Industry, Human–Robot Interaction, Automatic Depression Detection, Mood Audio/Video Players, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion Recognition

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A Privacy-Preserving Quantum Blockchain Technique for Electronic Medical Records

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IEEE engineering management Review 2023年第4期51卷 137-144页

作者： Venkatesh, Ranjitha Hanumantha, Brahmananda Savadatti Gandhi Institute of Technology and Management Department of Computer Science Engineering Bengaluru561203 India

Nowadays, electronic medical records are increasing rapidly. Electronic medical records consist of sensitive and confidential information. These pieces of information need to be protected from attackers during the exchange with patients. Blockchain technology can protect information from attackers during the exchange of information. There are various classical techniques to protect electronic medical records. These techniques can exchange information correctly but cannot resist collective and coherent attacks and have more communication and computation costs. In this article, we introduced a privacy-preserving quantum blockchain technique to protect electronic medical records. This technique can resist the intercept, intercept-resend, entangle-measure, man-in-the-middle, collective, and coherent attacks with less communication and computation costs. © 1973-2011 IEEE.

关键词： Quantum entanglement

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Detecting and recognizing faces from video images using multi-deep CNN based rank-level fusion 2

Detecting and recognizing faces from video images using mult...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Ghosh, Manas Sing, Jamuna Kanta RCC Institute of Information Technology Department of Computer Application Kolkata India Jadavpur University Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350371314

The advent of deep neural networks has provided a competitive edge in facial analysis efforts, especially the detection and recognition of faces from video images. This paper outlines a methodology wherein a face database is created from an outdoor video clip, and a rank-level fusion mechanism has been proposed for face recognition using a database that makes use of the probabilities produced by the softmax functions of multiple deep convolutional neural network (CNN) models. These probabilities are referred to as certainty indices for the potential classes to which a test sample may belong. For each of the CNN models, fuzzy ranks are obtained for a subset of classes with the top certainty indices by applying a Gaussian distribution function followed by a complementation operation. For each class, appearing in one or more subsets corresponding to deep network models, average of the certainty indices is complemented and fused with the sum of complemented fuzzy ranks. While calculating the cumulative sum of the fuzzy ranks, a penalty, is foisted to the class for not being present in any subset. This can avoid the chance of becoming an unexpected winner. The class with the lowest final ranking was identified as the class pertaining to the test sample. In addition to testing with the custom-made outdoor video-based face dataset, the projected method was evaluated with a standard surveillance indoor dataset, ChokePoint. Considering all test sets on ChokePoint dataset, the average recognition accuracy of the proposed method is 99.54%. The same for custom-made dataset is 93.96%. For both datasets, precision and macro-average scores of the proposed method in all cases are higher than the individual deep CNN models. © 2024 IEEE.

关键词： Face recognition

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Heart Disease Predictive Analysis Using Machine Learning Approaches 6

Heart Disease Predictive Analysis Using Machine Learning App...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Rana, Anamika Malik, Sushma Chauhan, Madhu Kumar, Prashant Maharaja Surajmal Institute Department of Computer Application New Delhi India Institute of Innovation in Technology & Management Department of Computer Application New Delhi India Jagannath International Management School New Delhi India

ISBN: (纸本)9798350304480

Machine Learning (ML) has found widespread applications in the healthcare sector worldwide, including the diagnosis and treatment of heart diseases, locomotor disorders, and various other medical conditions. ML methods have revolutionized healthcare by enabling the analysis of large and complex medical datasets, leading to valuable insights and predictions that aid healthcare professionals in providing better patient care. ML's ability to analyze vast amounts of healthcare data, uncover patterns, and make predictions has significant potential to improve patient outcomes, optimize medical workflows, and advance medical research. However, it's essential to address privacy and ethical considerations when using ML in healthcare, ensuring the responsible and secure use of sensitive patient information. Supervised Learning methods like SVM, Random Forest, and Logistic Regression are used for the analysis of the dataset downloaded from Kaggle. The various performance parameters such as Precision, F-1 score, Accuracy, and Recall were used to compare the performance of different ML classification techniques. Among the various methods evaluated, the Random Forest classification algorithm was found to outperform the other methods across the fourteen available parameters. © 2023 IEEE.

关键词： Heart Disease Logistic Regression Machine Learning (ML) Random Forest SVM

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Pancreatic Cancer Diagnosis from CT Scan Images Using Machine Learning Methods 7

Pancreatic Cancer Diagnosis from CT Scan Images Using Machin...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kashikar, Arya Maurya, Sudhanshu Likhar, Tirthak Mirza, Kashish Yadav, Ajay Kumar Asudani, Deepak Suresh Symbiosis Institute of Technology Nagpur Campus Pune India Graphic Era Hill University Department of Computer Science & Engineering Bhimtal Campus Uttarakhand India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798350350067

This research focuses its objectives on the use of machine learning techniques in the detection of images of a CT scan to diagnose Pancreatic cancer. The work used an extensive database containing diverse CT images, more specifically, concerning pancreatic cancer patients. Two primary methodologies were implemented in this study. The first was the Support Vector Machine (SVM) classifier, one of the most used classifier methods in classification problems. The second component is the Convolution Neural Network (CNN), which has ResNet 50 at its base. The architecture of this model is commendably profound, and it can perceive the details of the images. The SVM classifier marked a precision of 83.07% for detecting cancer;such a level of accuracy can give clues, though it is still possible to define additional possibilities for improving the classification of complicated cases. On the other hand, the CNN model based on the ResNet50 learned a more accurate model than the training accuracy of 90. 01%. Such a performance increase is explained quite well by deep learning methodologies' capabilities, especially in medical image analysis. These studies draw attention to the possibility of utilizing more sophisticated CNN models to promote the accuracy and reliability of pancreatic cancer diagnosis. The increased accuracy also has ramifications for the diagnostic phase of pancreatic cancer, but also it emphasizes the necessity of using complex algorithms for early identification of this disease. © 2024 IEEE.

关键词： computerized tomography

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