Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
23,722
result(s) for
"Kumar, N. S."
Sort by:
Extraction of bioactive compounds from Psidium guajava leaves and its utilization in preparation of jellies
Psidium guajava L. (guava) is predominantly grown throughout the world and known for its medicinal properties in treating various diseases and disorders. The present work focuses on aqueous extraction of bioactive compounds from the guava leaf and its utilization in the formulation of jelly to improve the public health. The guava leaf extract has been used in the preparation of jelly with pectin (1.5 g), sugar (28 g) and lemon juice (2 mL). The prepared guava leaf extract jelly (GJ) and the control jelly (CJ, without extract) were subjected to proximate, nutritional and textural analyses besides determination of antioxidant and antimicrobial activities. GJ was found to contain carbohydrate (45.78 g/100 g), protein (3.0 g/100 g), vitamin C (6.15 mg/100 g), vitamin B3 (2.90 mg/100 g) and energy (120.6 kcal). Further, the texture analysis of CJ and GJ indicated that both the jellies showed similar properties emphasizing that the addition of guava leaf extract does not bring any change in the texture properties of jelly. GJ exhibited antimicrobial activity against various bacteria ranging from 11.4 to 13.6 mm. Similarly, GJ showed antioxidant activity of 42.38% against DPPH radical and 33.45% against hydroxyl radical. Mass spectroscopic analysis of aqueous extract confirmed the presence of esculin, quercetin, gallocatechin, 3-sinapoylquinic acid, gallic acid, citric acid and ellagic acid which are responsible for antioxidant and antimicrobial properties.
Journal Article
An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection
Detection and classification of epileptic seizures from the EEG signals have gained significant attention in recent decades. Among other signals, EEG signals are extensively used by medical experts for diagnosing purposes. So, most of the existing research works developed automated mechanisms for designing an EEG-based epileptic seizure detection system. Machine learning techniques are highly used for reduced time consumption, high accuracy, and optimal performance. Still, it limits by the issues of high complexity in algorithm design, increased error value, and reduced detection efficacy. Thus, the proposed work intends to develop an automated epileptic seizure detection system with an improved performance rate. Here, the Finite Linear Haar wavelet-based Filtering (FLHF) technique is used to filter the input signals and the relevant set of features are extracted from the normalized output with the help of Fractal Dimension (FD) analysis. Then, the Grasshopper Bio-Inspired Swarm Optimization (GBSO) technique is employed to select the optimal features by computing the best fitness value and the Temporal Activation Expansive Neural Network (TAENN) mechanism is used for classifying the EEG signals to determine whether normal or seizure affected. Numerous intelligence algorithms, such as preprocessing, optimization, and classification, are used in the literature to identify epileptic seizures based on EEG signals. The primary issues facing the majority of optimization approaches are reduced convergence rates and higher computational complexity. Furthermore, the problems with machine learning approaches include a significant method complexity, intricate mathematical calculations, and a decreased training speed. Therefore, the goal of the proposed work is to put into practice efficient algorithms for the recognition and categorization of epileptic seizures based on EEG signals. The combined effect of the proposed FLHF, FD, GBSO, and TAENN models might dramatically improve disease detection accuracy while decreasing complexity of system along with time consumption as compared to the prior techniques. By using the proposed methodology, the overall average epileptic seizure detection performance is increased to 99.6% with f-measure of 99% and G-mean of 98.9% values.
Journal Article
Purification and identification of antioxidant peptides from the skin protein hydrolysate of two marine fishes, horse mackerel (Magalaspis cordyla) and croaker (Otolithes ruber)
In the current study, two peptides with antioxidant properties were purified from skin protein hydrolysates of horse mackerel (
Magalaspis cordyla
) and croaker (
Otolithes ruber
) by consecutive chromatographic fractionations including ion exchange chromatography and gel filtration chromatography. By electron spray ionization double mass spectrometry (ESI-MS/MS), the sequence of the peptide from the skin protein hydrolysate of horse mackerel was identified to be Asn-His-Arg-Tyr-Asp-Arg (856 Da) and that of croaker to be Gly-Asn-Arg-Gly-Phe-Ala-Cys-Arg-His-Ala (1101.5 Da). The antioxidant activity of these peptides was tested by electron spin resonance (ESR) spectrometry using 1-diphenyl-2-picryl hydrazyl (DPPH
·
) and hydroxyl (OH
·
) radical scavenging assays. Both peptides exhibited higher activity against polyunsaturated fatty acid (PUFA) peroxidation than the natural antioxidant α-tocopherol. These results suggest that the two peptides isolated from the skin protein hydrolysates of horse mackerel and croaker are potent antioxidants and may be effectively used as food additives and as pharmaceutical agents.
Journal Article
Deep learning steganography for big data security using squeeze and excitation with inception architectures
With the exponential growth of big data in domains such as telemedicine and digital forensics, the secure transmission of sensitive medical information has become a critical concern. Conventional steganographic methods often fail to maintain diagnostic integrity or exhibit robustness against noise and transformations. In this study, we propose a novel deep learning-based steganographic framework that combines Squeeze-and-Excitation (SE) blocks, Inception modules, and residual connections to address these challenges. The encoder integrates dilated convolutions and SE attention to embed secret medical images within natural cover images, while the decoder employs residual and multi-scale Inception-based feature extraction for accurate reconstruction. Designed for deployment on NVIDIA Jetson TX2, the model ensures real-time, low-power operation suitable for edge healthcare applications. Experimental evaluation on MRI and OCT datasets demonstrates the model’s efficacy, achieving Peak Signal-to-Noise Ratio (PSNR) values of 39.02 and 38.75, and Structural Similarity Index (SSIM) values of 0.9757, confirming minimal visual distortion. This research contributes to advancing secure, high-capacity steganographic systems for practical use in privacy-sensitive environments.
Journal Article
Extraction of bioactive compounds from Psidium guajava and their application in dentistry
Guava is considered as poor man’s apple rich in phytochemicals with medicinal value and hence it is highly consumed. Gas chromatography–mass spectroscopy (GC–MS) analysis of guava leaf extract revealed the presence of various bioactive compounds with antimicrobial, antioxidant, anticancer, and antitumor properties. Hence, it is used in tooth paste formulations along with other ingredients such as Acacia arabica gum powder, stevia herb powder, sea salt, extra virgin coconut oil, peppermint oil in the present study. Three formulations F1, F2 and F3 have been made by varying the concentration of these ingredients and the prepared formulations were studied for their antimicrobial activity and physico-chemical parameters such as pH, abrasiveness, foaming activity, spreading and cleaning ability. Among these, F3 showed significant antioxidant and antimicrobial properties, minimal cytotoxicity, maximum spreadability and very high cleaning ability. This study surmises that the herbal toothpaste formulation is greener, rich in medicinal values and imparts oral hygiene.
Journal Article
Soil test crop response nutrient prescription equations for improving soil health and yield sustainability—a long-term study under Alfisols of southern India
Enhancing soil health and nutrient levels through fertilizers boosts agricultural productivity and global food security. However, careful fertilizer use is essential to prevent environmental damage and improve crop yields. The soil test crop response (STCR) is a scientific approach to fertilizer recommendation that ensures efficient use, supporting higher crop production while protecting the environment and preserving resources.
A long-term field experiment on the STCR approach was initiated in 2017 at the Zonal Agriculture Research Station, University of Agricultural Sciences, Bangalore, India. The experiment aimed to study the impact of STCR-based nutrient prescription along with farmyard manure (FYM) for a targeted yield of soybean (
), sunflower (
), dry chili (
), aerobic rice (
L.), foxtail millet (
), okra (
), and kodo millet (
) on yield and changes in soil health in comparison with other approaches of fertilizer recommendation.
The results showed a significant and positive impact of the integrated use of fertilizer with FYM based on the STCR approach on the productivity of all the crops and soil fertility. Significantly higher yields of soybean (23.91 q ha
), sunflower (27.13 q ha
), dry chili (16.67 q ha
), aerobic rice (65.46 q ha
), foxtail millet (14.07 q ha
), okra (26.82 t ha
), and kodo millet (17.10 q ha
) were observed in the STCR NPK + FYM approach at yield level 1 compared to the general recommended dose and soil fertility rating approach. This approach outperformed the standard recommendations, enhancing nutrient uptake and efficiency across various crops. Utilizing the principal component analysis, the soil quality index effectively reflected the impact of nutrient management on soil properties, with the STCR NPK + FYM treatment at yield level 1 showing the highest correlation with improved soil physical and chemical parameters.
The STCR approach led to improved yield, nutrient uptake, utilization efficiency, and soil health, thanks to a balanced fertilization strategy. This strategy was informed by soil tests and included factors like crop-induced nutrient depletion, baseline soil fertility, the efficiency of inherent and added nutrients through fertilizers and farmyard manure, and the success of yield-targeting techniques in meeting the nutritional needs of crops.
Journal Article
Adaptive and scalable protection framework for virtual machines leveraging deep learning and dynamic defense
Virtual Machines (VMs) serve as dynamic execution environments that trade-off workload isolation, performance, and elastic scalability in the cloud. However, the flexibility of VMs which allows for efficiency also makes them susceptible to stealthy and adaptive cyber threats such as resource exhaustion, privilege escalation, and lateral movement. In such environments, the traditional signature- and heuristic-based defenses often encounter difficulties, resulting in high false-positive rates and low-rank under changing attack conditions. To mitigate these limitations, we present a flexible defense system which combines feature extraction, anomaly detection, classification and mitigation in a single pipeline. The system consists of an Adaptive Feature Encoder for concise behavior representation, a Density-Aware Clustering for anomaly detection, a Transformer–Boosting Classifier for timely threat identification, and a Dynamic Mitigation Controller for prompt decision making at runtime, and with low overhead. Experiments on benchmark VM telemetry datasets (ToN-IoT and CSE-CIC-IDS2018) indicate that VMShield provides 99.8% accuracy, 99.7% precision, 99.6% F1-score, and reduces false positives by 35% compared to state-of-the-art baselines. Stress testing ensures scalability, keeping detection latency at ~ 240 ms and overhead under 7%. By integrating the accuracy with operational resilience, proposed adaptive and scalable protection framework offers a practical defense to protect the cloud-hosted VMs from the emerging adversarial threats.
Journal Article
Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes
Maternal mental health during pregnancy is a crucial area of research due to its profound impact on both maternal and child well-being. This paper proposes a comprehensive approach to predicting and monitoring psychological health risks in pregnant women using advanced machine learning techniques. The study employs a systematic methodology including data collection, preprocessing, feature selection, and model implementation. Data collection was conducted at Majidia Hospital, involving a diverse sample of 70,000 pregnant women recruited through antenatal clinics, online health platforms, community outreach programs, and telephone surveys using structured questionnaires. Participants were selected across all pregnancy trimesters to ensure a representative demographic, capturing variations in age, educational background, occupational status, and parity. A diverse set of machine learning models, including Random Forest, Decision Tree, Support Vector Machine (SVM), Logistic Regression, Gaussian Naive Bayes, and Multilayer Perceptron (MLP), were evaluated alongside ensemble methods to achieve robust and reliable predictions. The experimental results demonstrate that the Random Forest model consistently outperforms other classifiers with an accuracy of 97.82% ± 0.03%, precision of 97.82% ± 0.03%, recall of 100.00% ± 0.00%, and an F1 score of 96.81% ± 0.02%. SVM and Decision Tree classifiers also showed strong performance, with accuracy scores of 93.79% ± 0.01% and 91.82% ± 0.03%, respectively. Furthermore, ensemble methods enhanced predictive performance, highlighting their ability to balance accuracy, precision, recall, and F1 score. In regression tasks, the Random Forest Regressor achieved near-perfect predictions with a Mean Squared Error (MSE) of 4.5767 × 10
−8
and an R
2
score of 1.000, underscoring its superior predictive capabilities. Additionally, a custom loss function integrating Cross-Entropy Loss and an F1 Score Penalty was introduced to address class imbalance and enhance model performance. The training process, conducted over 10 epochs, demonstrated consistent loss reduction, with the lowest recorded loss at epoch 8 (2.4382), reflecting effective learning and parameter tuning. This study envisions the development of an intelligent, web-based tool aimed at revolutionizing psychological health assessment and support for pregnant women. This tool will not only provide early diagnosis and intervention but also recommend personalized yoga practices and natural remedies to improve maternal mental health and overall wellbeing. These findings highlight the potential of AI-driven innovations in enhancing maternal care through holistic and accessible technological solutions.
Journal Article
A Dickson polynomial based group key agreement authentication scheme for ensuring conditional privacy preservation and traceability in VANETs
VANETs exchange data in highly dynamic open wireless access environments which are prone to security and privacy attacks. In order to safeguard the transmitted data, group key agreement authentication (GKAA) technique is used. Utilization of group key allows entities to corroborate a group key for secure VANET communication in an unsecure wireless communication channels. The traditional GKAA consumes a considerable amount of resources, verification delay is very high. Since the group key is computed and administered solely by TTA, it leads to central tendency. Additionally the communication delay soars high. To alleviate the problems of computational cost, communication cost, security, conditional privacy, central tendency, a Dickson polynomial based conditional privacy preservation authentication based on group key authentication (GKA) for VANETs has been proposed. The proposed work involves the use of Dickson polynomial to improve the security strength of TTA while authentication vehicles. Since it is based on chaotic mapping algorithm, wherein the chaotic map provides a one-way hash function; Dickson polynomial is used to corroborate a publicly distributed group key; it alleviates the complex modular or scalar multiplication performed using Elliptic curves. The group key gets computed in a distributed fashion by using the Chinese Remainder Theorem (CRT) and gets updated dynamically without the aid of TTA. Conditional Privacy has been ensured by the tracing back the pseudonyms in case of any illicit behavior exhibited by the vehicles. The proposed scheme is lightweight and lowers the communication, computation cost involved during authentication and verification. Performance analysis has been carried out by using BAN logic and ROR model thereby ensuring the security and efficiency. Thus the proposed authentication technique outperforms the traditional certificate-less and group key authentication schemes in terms of improvement in computation cost of 39%, communication cost of 672 bits for a single message with a less verification delay.
Journal Article