Diabetes is a long-term disease caused by the human body's inability to make enough insulin or to use it properly. This is one of the curses of the present world. Although it is not very severe in the initial stage, over time, it takes a deadly shape and gradually affects a variety of human organs, such as the heart, kidney, liver, eyes, and brain, leading to death. Many researchers focus on the machine and in-depth learning strategies to efficiently predict diabetes based on numerous risk variables such as insulin, BMI, and glucose in this healthcare issue. We proposed a robust approach based on the stacked ensemble method for predicting diabetes using several machine learning (ML) methods. The stacked ensemble comprises two models: the base model and the meta-model. Base models use a variety of models of ML, such as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF), which make different assumptions about predictions, and meta-models make final predictions using Logistic Regression from predictive outputs from base models. To assess the efficiency of the proposed model, we have considered the PIMA Indian Diabetes Dataset (PIMA-IDD). We used linear and stratified sampling to ensure dataset consistency and K-fold cross-validation to prevent model overfitting. Experiments revealed that the proposed stacked ensemble model outperformed the model specified in the base classifier as well as the comprehensive methods, with an accuracy of 94.17%.

Handwriting is controlled by neurons in the brain’s nervous system, reflecting an individual’s personality and psychology. This unique characteristic can be used for various applications, including user authentication, assessment of neurodegenerative disorders, and classification of handedness, gender, and age groups. Traditional authentication systems require memorization, information leakage, and fingerprints, making them vulnerable to security breaches. The majority of researchers have studied the limitations of image quality, camera frames, and light effects on text and image-dependent performance. Therefore, this paper mainly focused on real-time, text-independent handwriting fine-motor data and proposed an efficient authentication system with low cost using efficient feature extraction and optimal feature selection approaches. This research utilizes two benchmark databases, including the handwriting data of 48 (24+24) participants collected via a sensor-based pen tablet. Each participant wrote the 10 words five times repeatedly, making it a total of 2400 samples. The handwriting classification of the different individuals is in 3 phases: feature extraction, feature selection, and classification. A total of 91 features (statistical, kinematic, spatial, and composite) were extracted from more accurate, real-time numerical handwriting data. The efficient and optimal features have been selected using four feature selection approaches, namely, Pearson’s r correlation, ANOVA-F, Mutual Information Gain, and PCA, among which the ANOVA-F test and PCA perform well for handwriting-extracted data. Then, 14 machine learning (ML) models and 7 deep learning (DL) models were applied to handle the problem of individual classification, with both no- and full-feature-selection scenarios considered. The experimental analysis has been conducted with different angles and perspectives, such as K-Fold cross-validation, testing system efficiency considering 5/10/15/24/48 individuals, and in the case of individual tasks. It shows that ML-based algorithms, namely, CATBOOST (99.07%) with ANOVA-F and DL-based models, namely, BiLSTM (98.31%) with PCA-selected features, provide the highest accuracy with dataset 2, among others that advocate the practicality and reliability of choosing this system for user identification.

In this paper, we design and implement a multiantenna configured secure millimeter-wave (mmWave) CP-less multi-user orthogonal chirp division multiplexing (OCDM) transceiver. Our proposed simulated system emphasizes more applicable performance matrices for a typically assumed case of four users and a passive eavesdropper for audio data transmission. It introduces a four-dimensional hyperchaotic system-based encryption algorithm to enhance physical layer security (PLS). In addition, low density parity check (LDPC), TURBO, (3, 2) single parity check (SPC), and repeat and accumulate (RA) channel coding with Cholesky decomposition based zero-forcing (CD-ZF) and minimum mean square error (MMSE) signal detection techniques for a better bit error rate (BER) are also implemented. The simulation results signify the effectiveness of the proposed system in terms of PLS enhancement with low correlation coefficients (14.62%, 7.61%, 13.61%, and 15.39% for users 1, 2, 3, and 4, respectively), an achievable secrecy rate with a low signal-to-interference and noise ratio (SINR) of the passive eavesdropper, an achievable out-of-band (OOB) power emission of 341 dB, an estimated average short-time Fourier transform (STFT) spectral power difference of 7.68 dB and estimated peak-to-average power ratios (PAPRs) ranging from 7 to 7.5 dB at a CCDF of 1×10 ^-3 for different ground transmitting channels. At an identical signal-to-noise ratio of 17 dB, all four users achieve a BER of 1×10 ^-4 under RA channel coding, CD-ZF, and 16-QAM digital modulation.

In this article, we design and implement a multiantenna configured secure multiuser discrete Fourier transform (DFT)-Spread orthogonal frequency division multiplexing (OFDM) system based on frequency-domain spectrum shaping (FDSS) for reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicle (UAV)-assisted terahertz (THz) communications. Our proposed simulated system highlights more suitable performance matrices for a typical case of three users for color image transmission. We introduced a six-dimensional hyperchaotic system-based encryption algorithm to enhance the physical layer security (PLS) of a UAV-to-ground communication network. In addition, the block diagonalization (BD) precoding technique reduces multiuser interference (MUI). Furthermore, we included repeat and accumulate (RA) channel coding with Cholesky decomposition-based zero-forcing (CD-ZF) and minimum mean square error (MMSE) signal detection schemes to improve the bit error rate (BER). We adopted the FDSS scheme and considered null carriers to reduce the out-of-band (OOB) spectrum power. The simulation results demonstrate the effectiveness of the proposed system in terms of PLS enhancement for color image transmission, with a low image structural similarity index of 0.65%, 1.60%, and 0.70% for users 1, 2, and 3, respectively; an achievable OOB power emission of 337 dB; and estimated peak-to-average power ratios (PAPRs) ranging from 7.10 to 7.85 dB at a complementary cumulative distribution function (CCDF) of 1×10-4 for different ground-transmitting channels. At signal-to-noise ratios of 13.7, 9.4, and 7.5 dB, users 1, 2, and 3 achieve a BER of 1×10-3 under RA channel coding, MMSE, and binary phase shift keying (BPSK) digital modulation.

Enhancing spectral efficiency (SE), reducing out-of-band (OOB) power emission, minimizing multiuser interference (MUI), and ensuring secure data communication are key challenges in the development of future-generation unmanned aerial vehicle (UAV)-enabled wireless communication systems. To address these challenges and emphasize effective solutions, we propose a novel and secure reconfigurable intelligent surface (RIS)-assistedUAV-enabled multiuser frequency-domain spectrum shaping (FDSS)-based discrete cosine transform (DCT)-Spread massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) system. The system incorporates physical layer security (PLS) encryption based on two-dimensional cosine-sine interleaved chaotic map (2D-CSIM), block diagonalization (BD) precoding, FDSS with null carriers, turbo and repeat-and-accumulate (RA) channel coding, Cholesky decomposition-based zero-forcing (CD-ZF), and lattice reduction-based minimum mean square error (LR-MMSE) signal detection schemes. The 2D-CSIM enhances confidentiality in audio signal transmission, while the BD precoding reduces MUI. In addition, the FDSS with null carriers reduces OOB emissions and enhances the system’s SE. The turbo and RA channel coding with CD-ZF and LR-MMSE signal detection schemes improve the bit error rate (BER) performance. Simulation results show the effectiveness of the proposed system. It achieves significant PLS for audio transmission, with low correlation coefficients of < 2% for four users between the original and encrypted signals. We also quantify the effectiveness of the 2D-CSIM-based encryption technique using the National Institute of Standards and Technology (NIST) Statistical Test Suite. The system achieves an OOB reduction of 334 dB, SE of 33.30 bps/Hz, and peak-to-average power ratio (PAPR) of 8.89 dB at a complementary cumulative distribution function (CCDF) of 1 × 10⁻³, which are comparable to state-of-the-art systems. At signal-to-noise ratios (SNRs) of 27 − 28.5 dB, the four users achieved a BER of 1 × 10⁻⁴ with RA coding, LR-MMSE detection, and lower-order quadrature amplitude modulation (QAM) constellation.