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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Maurya, Sudhanshu
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Towards an automated weather forecasting and classification using deep learning, fully convolutional network, and long short-term memory Shelke, Nilesh; Maurya, Sudhanshu; Ithape, Rupali; Shaikh, Zarina; Somkunwar, Rachna; Pimpalkar, Amit
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1868-1879

Abstract

Historically, weather forecasting was unreliable and imprecise, relying on intuition and local knowledge. Inaccurate weather forecasts can cause severe impacts on agriculture, construction, and daily life. Existing methods struggle with rural and urban weather prediction, requiring faster and more accurate solutions. This research proposes a deep learning system using real- time images to address this challenge. This research employs a deep learning model fully convolutional network-long short-term memory (FCN-LSTM) to analyze images and predict weather conditions. In this case, the model forecasts a sunny and cloudy environment, which facilitates defining the ideal conditions for every given climatic zone in the weather classification model. The model is trained on a dataset of weather images obtained from Kaggle. The performance of the proposed model FCN-LSTM achieves an accuracy of 96.88% and a validation accuracy of 91.22%. Also, the mean squared error (MSE) is 7.11, which is significantly lower and supports efficient enhancement in weather forecasting. This significant improvement demonstrates the potential of deep learning for real-time weather forecasting. The model provides efficient weather classification, enabling informed decision-making across various sectors. This research lays the foundation for automated weather analysis using deep learning, eliminating human bias and improving accuracy.
Securing post-quantum cryptography: side-channel resilience in CRYSTALS-Kyber key encapsulation mechanism Kasture, Shreyas; Maurya, Sudhanshu; Singh, Alakshendra Pratap; Shukla, Amit; Kotiyal, Arnav; Mirza, Kashish
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5251-5267

Abstract

This study evaluates side-channel vulnerabilities in hardware implementations of the cryptographic suite for Algebraic lattices (CRYSTALS)-Kyber key encapsulation mechanism (KEM) using correlation and differential power analysis (DPA) techniques. Unprotected field-programmable gate array (FPGA) implementations across all Kyber parameter sets were successfully compromised, revealing significant information leakage. Attack complexity scaled linearly with key size. Additive Boolean masking provided varying protection levels, with 4-bit masking offering a 100× security increase at notable performance cost. Performance characterization showed increased slice utilization and reduced maximum frequency for higher-order masking. A novel hybrid countermeasure combining higher-order masking with controlled time randomization enhanced protection against machine learning-based attacks. Comprehensive power trace analysis using 12-bit precision at 500 MS/s sampling rates was conducted. Statistical evaluation utilized Pearson's correlation and Welch's t-tests with a 0.8 threshold for key recovery. Real world validation in IoT, financial, and satellite scenarios highlighted practical post-quantum cryptography (PQC) deployment challenges. The study provides concrete design guidance for efficiently securing hardware Kyber implementations against side-channel attacks.
Deep learning for early detection of cardiovascular diseases via auscultation sound classification Kasture, Shreyas; Maurya, Sudhanshu; Kumar Sharma, Amit; Chitraju Gopal Varma, Santhosh; Mirza, Kashish; Sadaf Mohammad Ismail, Firdous
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1746-1761

Abstract

Heart diseases are one of the most prominent causes of death globally, which requires immediate and accurate diagnosis. The auscultation methods used in conventional medical practice, where the doctor listens to the sounds produced by the body without intervention is very ineffective because of the limitations in the actual skills and perception of the doctor. The main goal of this project will be designing a mobile-based system for the early detection of cardiovascular disease (CVD) by utilizing deep learning for auscultation sound classification. The approach involves the use of deep learning structures to classify cardiac sounds into normal and abnormal patterns on its own. Wavelet transformations, time-frequency representations, and Mel frequency cepstral coefficients (MFCC) have been used in feature extraction. The ResNet152V2 model showed high classification performance with area under the receiver operating characteristic curve (AUROC) of 0.9797 and 0.9636 on two datasets. Contrary to that, data augmentation, hyperparameter optimization, attention mechanisms, as well as input-output residual connections, led to better functionality and interpretability. This research seeks to overcome the limitations of traditional stethoscope use through the incorporation of sophisticated algorithms and the availability of mobile technology that could result in early diagnosis and prevention of CVDs, especially in underprivileged areas.
Co-Authors Chitraju Gopal Varma, Santhosh Ithape, Rupali Kasture, Shreyas Kotiyal, Arnav Kumar Sharma, Amit Mirza, Kashish Pimpalkar, Amit Sadaf Mohammad Ismail, Firdous Shaikh, Zarina Shelke, Nilesh Shukla, Amit Singh, Alakshendra Pratap Somkunwar, Rachna