This study proposes a hybrid quantum-classical framework for depression detection from social media text, integrating a frozen DistilBERT encoder with a variational quantum circuit (VQC)-based classification layer. The motivation stems from challenges in clinical NLP, including overfitting on limited datasets and high parameter overhead in conventional deep learning classifiers. Experiments are conducted on a balanced subset of the Reddit Self-Reported Depression Diagnosis (RSDD) dataset comprising 6,000 users. The proposed model is evaluated against classical baselines, including TF-IDF with logistic regression and a fine-tuned DistilBERT model. Results indicate that the hybrid approach achieves competitive performance, with an F1-score of 0.925 (±0.009) and improved recall (0.942 ± 0.015) compared to the classical DistilBERT baseline. Additionally, the quantum classification layer requires significantly fewer trainable parameters (72) compared to the classical dense head, demonstrating improved parameter efficiency at the classification stage. While the results suggest that variational quantum circuits can serve as an alternative non-linear classifier in low-data settings, the findings are based on simulation and require further validation on real quantum hardware. This work contributes to the emerging area of quantum natural language processing by providing an empirical evaluation of hybrid architectures on a real-world clinical text dataset.

This study introduces a unique framework that combines machine learning, and dosha profiling from Ayurveda, to improve precision, reliability, and interpretability of traditional diagnostic assessments. Four machine learning algorithms (Logistic Regression, Support Vector Machine (SVM), Random Forest, and XGBoost) were systematically investigated with a rigorous, quiz-based dataset that contained demographic, lifestyle, and physiological characteristics to classify six dosha categories (Vata, Pitta, Kapha, and their pairs). The experimental results showed a stark difference between linear and ensemble approaches. With an accuracy of 30% (F1 = 0.288), Logistic Regression provided marginal performance, suggesting there is limited separability in the overlapping patterns of health, while SVM came with an accuracy of 97.3% (F1 = 0.972) with kernel optimization. However, tree-based ensemble approaches improved predictive utility; Random Forest showed the highest overall performance (accuracy = 98.1%, F1-Macro = 0.982), with XGBoost and a Stacked Ensemble model behind (both ≈ 98%). This confirms ensemble approaches can represent the complex and nonlinear interdependencies associated with holistic wellness datasets. Interpretability analysis through feature importance ranking identified lifestyle and physiological variables—including sleep quality, appetite, emotional stability, skin texture, and digestion pattern—as the most important predictors, demonstrating a strong correlation to established Ayurvedic theory. Additionally, a desktop-based, interactive visualization was built to allow dosha prediction and wellness insights in real time. In conclusion, this work provides justification for Random Forest and XGBoost models as benchmarks for dosha classification and achieves a scientific syntheses of ancient Ayurvedic practice with contemporary machine learning. The results have important implications for the portfolio of digital Ayurvedic practice; to support data-informed personalized medicine; to foster new cross-disciplinary collaborations among ancient medicine, modern artificial intelligence and machine learning. This study compares Support Vector Machines (SVM), Random Forest (RF), and XGBoost for Ayurvedic dosha classification (Vata, Pitta, Kapha).

Clinically, ECG is a potential tool used in the applications evaluating electrical activities of heart, functioning and providing solution to problems associated with it. Among such, the relationship (correlation) between respiration and electrocardiographic signal has attracted attention in past decades. In this research, a Welch spectrum estimation approach was utilized for normalizing cross spectrum analysis between these two signals. This approach can be useful while diagnosing diseases like pulmonary embolism, coronary lung diseases, Deep vein thrombosis and other diseases related to heart, from the knowledge of existing coherence bonding between these signals. This research applies the above approach to human subjects, whose ECG and respiratory signal annotations has been evaluated and were sampled at 100 samples/ second (sampling rate). The different respiratory signals are taken from Chest (CRSP), abdominal (ARSP) and oronasal regions (NRSP). The annotated signals for all the four subjects, discussed in this paper were obtained through a non-invasive test, which is medically well known as impedance phlebography, or impedance plethysmography. The numbers of samples, under the analysis were 6000 for each signal. The data was acquired from recording database of physionet. For this examination the mean square coherence (MSC) was chosen as an excellent candidate. The results imply that the mean of MSCs is found continuously decreasing in chest respiration. Secondly, the results showed maximum coherence between ECG and corresponding respiratory signal in three subjects is in Abdominal (ARSP) region (i.e. having maximum value greater than 0.5). Lastly, above analysis was analyzed over the fourth subject's data and under observation it was found, exceptionally that, the value of coherence for all respiratory patterns showed a poor functional association or simply coherence between the signal i.e.Coh2 below 0.5 in the abdominal region (ref.Fig.5) and the reason suggested could be chronic lung disease while the results show higher values, that is between (0.5 < coherence <1) in other two. Further, we show that the coherence peak reflects that the one physiological signal is synchronized with another signal of same nature at a particular frequency, here it is 0 to 35 Hz frequency band and combined analysis is shown through a Boxplot, from three regions showing maximum value of coherence upper quartile in abdominal region for three healthy subjects with maximum value of peak in the same region. This paper also presents a platform to dissolve the problem pertaining in an individual related to deep vein thrombosis, hypoxemia (blood level <90%) [16] and related diseases by estimating the coupling associated between saturated oxygen content (SO2) with respiratory patterns, in order to detect dysfunctioning clinically, also for efficient heart working. Thus, the research shows successful attempt to investigate the interaction of the PS of ECG signal and respiratory signals. The work presented in this paper can further be extended by adopting different method and either by defining a vector array element for maximum number of coherence value that could be beneficial for detecting diseases like sleep apnea on basis of minimum or maximum occurrence of peaks.

In this paper, an efficient control algorithm for an Intelligent Controller Induction Motor Drive system using Fuzzy Logic Approach has been proposed. The Indirect Vector Control principle has been employed to control the Induction Motor. Next, a two-degree-of freedom controller is proposed to improve the system performance. The controller design algorithm can be applied in an adjustable speed control system to obtain good transient responses and good load disturbance rejection abilities. The proposed controller has been analyzed using computer simulation and compared with a simple conventional Controller strategy. The simulated controller performances have been finally verified experimentally using TMS320C6711 Digital Signal Processor. The results obtained substantiate the robustness and effectiveness of Intelligent Controller for high performance of Induction Motor.

In this paper THD (Total Harmonic Distortion) is analysed and compared by using UPFC in a multi-line transmission system of 500 KV having 5-buses in two different arrangements. The UPFC converters are arranged as a Diode Clamped multilevel Converter (DCMLC) that leads to the cost reduction as compared with other multi-level converters. The comparison has been done by both series zig-zag/2Y-2Δ and series zig-zag/4Y transformer configuration for 48-pulses GTO based diode clamped converter. The THD is reduced to 42.59% and 58.82% of input waveform at bus B2 by using series zig-zag/4Y transformer configuration. This transformer converter configuration also reduces the difficulty of designing the transformer winding ratio. For calculation of THD, FFT analysis is carried out using MATLAB.

The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Hence, this paper presents Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of the following stages namely, feature extraction, dimensionality reduction, and classification. The features extracted from the magnetic resonance images (MRI) have been reduced using principles component analysis (PCA) to the more essential features such as mean, median, variance, correlation, values of maximum and minimum intensity. In the classification stage, classifier based on Back-Propagation Neural Network has been developed. This classifier has been used to classify subjects as normal, benign and malignant brain tumor images. The results show that BPN classifier gives fast and accurate classification than the other neural networks and can be effectively used for classifying brain tumor with high level of accuracy.

Carry free arithmetic using higher radix number system such as Redundant Binary Signed Digit can be used to meet the demand for computers operating at much higher speeds. The computation speed can also be increased by using the suitable design of adder and multiplier circuits. Fast RBSD adder cells suggested by Neelam Sharma in 2006 using universal logic are modified in the proposed design by reducing the number of gates. Due to reduction in gate count, number of gate levels and hence the circuit complexity is also reduced. As multiplication is repetitive addition, the implementation time of the multiplier circuit will also be reduced to a great extent by using modified design of adder cell to add the partial products. These partial products are added using pipelined units to reduce implementation time further. Thus with the use of proposed RBSD adder, other arithmetic operations such as subtraction, division, square root etc. can be performed much faster. It is concluded that efficiency of the proposed RBSD adder and multiplier is improved as compared to the techniques conventionally used in high speed machines. Thus the proposed modified RBSD adder cell using universal gates can be used to design fast ALU with many additional advantages.