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.

The rapid advancement of the Internet of Things (IoT) has heightened the importance of security, with a notable increase in Distributed Denial-of-Service (DDoS) attacks targeting IoT devices. Network security specialists face the challenge of producing systems to identify and offset these attacks. This research manages IoT security through the emerging Software-Defined Networking (SDN) standard by developing a unified framework (RNN-RYU). We thoroughly assess multiple deep learning frameworks, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Feed-Forward Convolutional Neural Network (FFCNN), and Recurrent Neural Network (RNN), and present the novel usage of Synthetic Minority Over-Sampling Technique (SMOTE) tailored for IoT-SDN contexts to manage class imbalance during training and enhance performance metrics. Our research has significant practical implications as we authenticate the approache using both the self-generated SD_IoT_Smart_City dataset and the publicly available CICIoT23 dataset. The system utilizes only eleven features to identify DDoS attacks efficiently. Results indicate that the RNN can reliably and precisely differentiate between DDoS traffic and benign traffic by easily identifying temporal relationships and sequences in the data.

Speech emotion detection is driven by complex neurological mechanisms within the brain's nervous system, reflecting an individual's emotional state and psychological makeup. This unique characteristic serves as a foundation for many applications, including personalized user experiences, mental health diagnostics, and demographic analysis based on emotional responses. Traditional approaches to emotion recognition frequently face challenges such as variability in speech patterns, environmental noise, and speaker diversity. Within the evolving landscape of human-computer interaction, this research introduces a novel synthesis of Convolutional Neural Networks (CNNs) with the domain of speech emotion recognition, thus surpassing the boundaries of traditional methodologies. This research analyzed 957 audio samples from 5 individuals using benchmark datasets, extracting Mel Frequency Cepstral Coefficients (MFCC) to classify emotions accurately-including ‘neutral’, ‘happy’, ‘angry’, ‘fear’, ‘surprise’, ‘calm’, ‘disgust’, and ‘sad’. Departing from traditional approaches reliant on manual feature engineering, our end-to-end model autonomously learns from raw speech, with our CNNs achieving an exceptional 96.7% accuracy. This marks a significant advancement in speech emotion recognition through deep learning, paving the way for more intuitive, emotion-aware human-computer interactions.