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A fully flexible key delegation mechanism with efficient fine-grained access control in CP-ABE

Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12837-12856页

作者： Porwal, Shardha Mittal, Sangeeta Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India

Ciphertext Policy-Attribute Based Encryption (CP-ABE) is a secure one-to-many asymmetric encryption schemes where access control of a shared resource is defined in terms of a set of attributes possessed by a user. Key delegation service, where a user is able to temporally transfer its full or partial access rights, is an important requirement to make CP-ABE based systems practically useful. In existing CP-ABE delegation schemes, a delegator can delegate its entire or partial set of attributes such that all content accessible using those attributes becomes available to the delegatee also. Flexibility to delegate some specific content, which is a practical requirement, is not feasible with these schemes. Further, for a delegated resource, flexibility of fine grained delegation like delegating only a specific subset of read, write and update permissions is not yet available. In this work, CP-ABE key delegation has been enhanced to support specific content and specific permission delegation. The proposed scheme is fully flexible as it additionally supports limits on the number of times delegation can be done, delegatee revocation and traceability of forbidden delegation. Theoretical and practical analysis show that the proposed scheme achieves its objectives and is computationally efficient as well as secure against chosen plaintext and key abuse attacks. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Access control

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A new electricity theft detection method using hybrid adaptive sampling and pipeline machine learning

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Multimedia Tools and Applications 2024年第18期83卷 54521-54544页

作者： Tripathi, Ashish Kumar Pandey, Avinash Chandra Sharma, Nikhil Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India Discipline of CSE PDPM Indian Institute of Information Technology Design and Manufacturing Madhya Pradesh Jabalpur482005 India

Electricity theft not only results in higher electricity costs for regular paying customers but is also a safety threat to the public due to illegal power connections made for cheating. Many governments and private companies have suffered substantial losses from electricity theft or fraud. Several approaches have been presented in the literature for electricity fraud detection. The existing methods for electricity fraud detection show poor performance as the electricity datasets are generally unbalanced or skewed. To handle skewness issues, SMOTE and ADASYN-based oversampling techniques are employed in some electricity fraud detection methods. However, existing oversampling methods typically increase the overlapping of classes and introduce additional noise. Therefore, in this paper, a new oversampling method named ADASYN-SGWO has been presented. The proposed ADASYN-SGWO employs ADASYN for oversampling and SSO-GWO for intelligent undersampling, which mitigates class imbalance issues in electricity datasets. After reducing the issue of class imbalance, random forest pipeline has been used to detect electricity fraud. To assess the efficiency of the proposed approach, precision, recall, F1 score, accuracy, Matthews correlation coefficient, and balance classification rate of the proposed and other state-of-the-art, including XG-boost classifier, Logistic regression, Gaussian naive Bayes, Random Forest classifier, Ada Boost classifier, LGBM classifier, and Quadrant discriminative analysis, have been computed and compared. Further, the proposed approach is also statistically validated using Friedman and post hoc analysis. The statistical analysis and experimental outcomes witnessed that the proposed ADASYN-SGWO-PRF method has outperformed all the other considered methods with 98.2% accuracy, which is significant for real-time applications of electricity theft detection problems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Spr

关键词： Classification (of information)

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Performance Evaluation of Natural Language Processing Algorithms for Sentiment Analysis

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SN computer science 2024年第6期5卷 724页

作者： Silviya, S. H. Annie Faith, S. Julia Seetha, R. Hemalatha, M. Department of Computer Science and Engineering Rajalakshmi Institute of Technology (RIT) Department of Information Technology S.A.Engineering College Department of Information Technology Vellore Institute of Technology Department of Computer Science and Engineering RMD Engineering College

Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, computer science, and artificial intelligence called “natural language processing” studies how computers and human language interact, with a focus on how to teach computers to process and analyze vast volumes of natural language data. Sentiment analysis, sometimes referred to as opinion mining or emotion artificial intelligence, is the methodical identification, extraction, quantification, and study of affective states and subjective data using natural language processing, text analysis, computational linguistics, and biometrics. Sentiment analysis is frequently used in online and social media platforms, voice of the customer materials (such as reviews and survey replies), and healthcare materials for marketing, customer service, and clinical medicine purposes. This paper discusses different sentiment analysis methods and selects the best approach for a certain task. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics Computational linguistics Natural language processing Sentiment analysis Text analysis

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Designing Of Hybrid Intrusion Detection System With Combined Approach Of LightGBM-AE And Double DQN 27

Designing Of Hybrid Intrusion Detection System With Combined...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Sinha, Ditipriya Srivastava, Arpita Singh, Om National Institute of Technology Patna Department of Computer Science and Engineering Patna India Indian Institute of Engineering Science and Technology Shibpur Department of Information Technology Shibpur India

ISBN: (纸本)9798350392319

Cyber-attacks are a growing threat to individuals, businesses, and governments. Data science helps improve security by analyzing network traffic and system activities. Intrusion detection systems (IDSs) are essential for enhancing security and resilience, but IDS datasets face challenges in obtaining quality training data and accurately predicting cyberattacks. To tackle these issues, this paper designs a novel hybrid intrusion detection system that detects malicious behavior based on signature and anomaly. It utilizes advanced techniques like one hot encoding, min-max normalization for data preprocessing, modified GAMO for data oversampling, information gain, and modified grey wolf optimizer (GWO)based bi-phase approach for feature selection, stacking of LightGBM with autoencoder for signature-based detection, and finally double deep Q-network (Double DQN) for anomaly-based detection to create robust and efficient hybrid IDS model. The proposed model achieves 94.33% and 88.90% accuracy utilizing signature-based and anomaly-based IDS, respectively, outperforming state-of-the-art 'MTH-HIDS' in accuracy, precision, recall, and f1-score. © 2024 IEEE.

关键词： Network intrusion

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Identification of Corn Leaf Disease Using Deep Learning 16

Identification of Corn Leaf Disease Using Deep Learning

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Kaushik, Rekha Yagnik, Suryansh Verma, Aman Yadav, Nitin Indian Institute of Information Technology Department of Information Technology Bhopal India Indian Institute of Information Technology Department of Electronics and Communication Bhopal India Indian Institute of Information Technology Department of Computer Science Bhopal India

ISBN: (纸本)9798331505264

The agricultural production can be enhanced effectively by detecting corp diseases more efficiently thus, preventing crop failure due to contamination. This study utilized deep convolutional neural network (CNN) models to detect plant leaf diseases from leaf images. CNNs have demonstrated promising results for machine vision tasks. The models achieved accuracy rates ranging from 90.25% to 95.56% to classify the disease, surpassing traditional methods that rely on manually crafted features. Key CNN architectures explored include VGG16, ResNet50, and AlexNet, with optimizations such as batch size ad-justments, dropout incorporation, and varying training epochs. The high accuracy levels indicate that deep CNNs can reliably detect diseases, suggesting their potential for enabling automated crop disease detection in real-time agricultural systems. Moving forward, additional testing with expanded datasets of plant diseases could further validate the robustness of the models. Deep learning methods can be used to precisely predict the crop diseases, this approach may significantly aid efforts in early disease intervention and treatment, leading to improved crop health, yields, and ultimately, farm productivity. © 2024 IEEE.

关键词： Convolutional neural networks

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Weighted Ensemble Learning for Accident Severity Classification Using Social Media Data

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SN computer science 2024年第5期5卷 528页

作者： Raul, Sanjib Kumar Rout, Rashmi Ranjan Somayajulu, D.V.L.N. Department of Computer Science and Engineering National Institute of Technology Telangana Warangal 506004 India Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing (IIITDM) Andhra Pradesh Kurnool 518002 India

Due to the heavy increase in social media usage, like Twitter, there is a growing interest in the research community in developing automation tools like accident severity-based tweet classification models. These tools aid in automatically extracting severity information from the accident tweet content. Moreover, prediction models are essential for predicting the severity of an accident to increase the safety efficacy of the road traffic system. However, the difficulty lies in the collection of sufficient labeled data. We propose a model called weighted ensemble-based self-training with decision tree (WESTDT), a semi-supervised methodology with a dynamic data labeling strategy. The base classifier in this model is a homogeneous weighted ensemble of decision tree classifiers for better prediction of pseudo labels. We also propose a novel performance measure called risk factor to estimate the amount of risk present in the application using the prediction model. The proposed model outperformed the state-of-the-art model, namely reliable semi-supervised ensemble learning (RESSEL), and the baseline models, namely decision tree (DT) and self-training with decision tree (STDT), in terms of both the traditional and the proposed measures. The results indicate that the proposed framework outperforms all other models on all datasets in terms of precision, recall, and accuracy by a range of 5–18.3%, 3.9–9.3%, and 6.6%, respectively. These findings can be helpful for the development of efficient and sustainable systems for traffic management and safety. It is also crucial for assisting government authorities in devising prompt, proactive strategies to prevent traffic accidents and enhance road safety. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Accident severity classification Decision tree Self-training Semi-supervised learning Weighted ensemble learning

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Custom ANN: An Approach for Efficient Modulation Classification

Custom ANN: An Approach for Efficient Modulation Classificat...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Kumar, Varun Gupta, Deepika Indian Institute of Information Technology Vadodara Department of Computer Science and Engineering International Campus Diu India

ISBN: (纸本)9798350350821

Modulation is a fundamental technique for transmitting signals between a transmitter and receiver in wired and wireless scenarios. In a non-cooperative communication scenario, the receiver should be capable of classifying the modulation type efficiently. Automatic Modulation Classification (AMC) is designed to monitor the radio-frequency spectrum and for making effective transmission decisions. Earlier AMC methods relied on feature extraction and likelihood-based approaches. However, with the rise of Deep Learning (DL), AMC has gained momentum due to its superior predictive capabilities. In this work, we propose a custom artificial neural network (ANN) for modulation classification. We analyze the impact of a deep learning model on AMC and how different modulation techniques influence its accuracy. © 2024 IEEE.

关键词： Signal modulation

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Exploring Explainable AI for Enhanced Depression Prediction in Mental Health 1

Exploring Explainable AI for Enhanced Depression Prediction ...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Jo, Ashly Ann Raj, Ebin Deni Vino, Angel Susan Menon, Parvathy V. Indian Institute of Information Technology FACTS-H Lab Department of Computer Science & Engineering Kottayam India

ISBN: (纸本)9798350376517

Depression is a prevalent mental health condition affecting millions of individuals worldwide. This paper presents a comprehensive analysis using machine learning to predict depression, a prevalent mental health issue affecting millions worldwide. The dataset employed for this study includes a range of mental health conditions such as schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders, and alcohol use disorders, investigating their correlations with depression. We explore these relationships and develop predictive models to forecast the likelihood of depression. The study evaluates the performance of various machine learning algorithms, with the Random Forest algorithm demonstrating the highest accuracy rate of 99.30%. Furthermore, we delve into model interpretability by applying techniques like LIME and SHAP to explain the predictions. Our findings contribute significantly to the understanding and prediction of depression, offering valuable insights that could assist healthcare professionals in the early detection and proactive management of this condition. © 2024 IEEE.

关键词： Adversarial machine learning

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CNN-LSTM based classification of polo like kinase family of Proteins: An emerging cancer drug target

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Materials Today: Proceedings 2022年 58卷 445-450页

作者： John, Chinju Mathew, Oommen K. Sahoo, Jayakrushna Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam686635 India

Artificial intelligence and deep learning are becoming an inevitable part of our life. Deep learning models are giving contributions to the identification of genetic causes behind various diseases affecting the human community. The prognosis and diagnosis of such diseases can be recommended with the help of artificial intelligence. Here we are proposing a novel deep learning model that employs the legendary deep learning architectures such as convolutional neural network and the variant of recurrent neural network known as long short-term memory network to classify the protein sequences belonging to the five-member polo like kinase family, a subclass of Serine-Threonine kinases, which is considered as an active anti-cancer drug target these days. The proposed deep learning model was trained and tested on sequences collected from biological sequence databases and classified the new sequences to their corresponding classes with an accuracy of 97.6%. Furthermore, this model could untangle the efforts associated with sequence annotation and classification, which is always a tedious and exigent task. © 2022

关键词： Diseases

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IoT based automated Wheel Chair for Physically Challenged

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Materials Today: Proceedings 2022年 56卷 533-541页

作者： Mishra, Priyanka Shrivastava, Sanatan Department of Computer Science and Engineering Indian Institute of Information Technology Kota Jaipur India

The purpose of an automatic wheelchair (AWC) is to assist and support paraplegics. The proposed chair is particularly suited for people with a higher level of impairment, such as quadriplegics who are unable to move any of their body parts except their head due to their advanced age or infirmity. We created a microcontroller-based wheelchair that moves with the patient's head motion. Electronic and mechanical components make up the system. An accelerometer monitors the patient's head nodes in all four dimensions for head motion recognition. The paper goes over the Bluetooth technology on the wheelchair, as well as the accelerometer that powers the motors and controls the chair's motions inside the house. Our wheelchair is designed with the patient's health in mind as well as the device's maneuverability. The pulse rate and blood oxygen levels are monitored by sensors using an Internet of Things approach. The ultrasonic sensors aid with obstacle recognition, making it easier to move the chair in a certain direction. By observing the directions of head movement along the x and y axes, the implanted accelerometer aids the chair in determining which movement the patient wishes to make. The signal is then sent to the microcontroller. The wheelchair is controlled by a microprocessor-based on the direction of acceleration, and it moves in LEFT, RIGHT, FORWARD, and BACKWARD with the help of viper motors. © 2022

关键词： Microcontrollers

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