Galgotias University

Heart disease is one of the leading causes of death worldwide. Predicting and detecting heart disease early is crucial, as it allows medical professionals to take appropriate and necessary actions at earlier stages. Healthcare professionals can diagnose cardiac conditions more accurately by applying machine learning technology. This study aimed to enhance heart disease prediction using stacking and voting ensemble methods. Fifteen base models were trained on two different heart disease datasets. After evaluating various combinations, six base models were pipelined to develop ensemble models employing a meta-model (stacking) and a majority vote (voting). The performance of the stacking and voting models was compared to that of the individual base models. To ensure the robustness of the performance evaluation, we conducted a statistical analysis using the Friedman aligned ranks test and Holm post-hoc pairwise comparisons. The results indicated that the developed ensemble models, particularly stacking, consistently outperformed the other models, achieving higher accuracy and improved predictive outcomes. This rigorous statistical validation emphasised the reliability of the proposed methods. Furthermore, we incorporated explainable AI (XAI) through SHAP analysis to interpret the model predictions, providing transparency and insight into how individual features influence heart disease prediction. These findings suggest that combining the predictions of multiple models through stacking or voting may enhance the performance of heart disease prediction and serve as a valuable tool in clinical decision-making.

Background Polycystic Ovary Syndrome is well known to cause various metabolic changes in the body; however, changes in the ocular surface are not fully understood or well-described in the existing literature. Hormonal disturbances resulting from PCOS may affect multiple ocular tissues, including the posterior segment, lacrimal and meibomian glands, cornea, and conjunctiva. Objective This paper aims to summarize the current knowledge and research regarding ocular alterations related to PCOS. Method A comprehensive review of the existing literature was conducted by searching multiple databases, including Scopus, PubMed, and Google Scholar. Keywords such as “Polycystic Ovary Syndrome,” “PCOS,” “ocular surface,” “dry eye,” “meibomian gland dysfunction,” and “ocular changes” were used. Relevant case reports and clinical studies were included to provide a comprehensive understanding of the ocular implications of PCOS. Results Among the ocular changes associated with PCOS, dry eyes are the most common source of irritation and discomfort in affected individuals. Recognizing this association is crucial for eye care practitioners. Conclusion Identifying the link between PCOS and dry eyes enables practitioners to develop personalized management plans for individuals with PCOS, potentially improving their eye health and comfort in longer run. When necessary, further evaluation or referral may be required for patients with PCOS-related ocular symptoms.

Crop diversification and its associated practices support natural agroecosystem processes in local, diverse agricultural systems. The widespread appraisal of crop diversification is a fundamental alternative to simplified production systems for enhancing biodiversity in agricultural landscapes. Despite substantial evidence of crop diversification benefits for agroecology, the adoption of these practices lags behind. Despite strong scientific evidence of the benefits, the adoption of these practices remains significantly slower than necessary to address ecological and societal needs. Its adoption largely depends on farmers attitudes, intentions, perceived motivations, governance policies, incentives, and market acceptance. In contrast, successful agronomic interventions require redirecting subsidies toward incentives that promote biodiversity, achieve sustained high yields, and support soil-centric green revolutions. Achieving landscape-level mosaics of natural biotic populations and fine-grained crop diversification is key to restoring biodiversity lost during the Green Revolution era. Through this review, we discuss the benefits of crop diversification in promoting natural ecological processes. These outcomes arise from the complex interplay between structural elements—such as governance, cultural norms, and resource availability—and individual or collective agency, including priorities, collaboration, and strategic choices. This dynamic relationship shapes the pathways of crop diversification, influencing its role in promoting natural ecological processes and its adoption in diverse agricultural contexts. When individuals and communities collaborate through robust networks and well-supported institutions, the dissemination of sustainable practices across farms and landscapes becomes significantly more effective. This collective action, fueled by shared knowledge, innovation, and access to resources, enhances the capacity to integrate ecological principles that build resilient and sustainable agroecosystems. So, crop diversification practices contribute to a “win–win” outcome by supporting yield stability, enhancing environmental adaptability, and promoting long-term profitability. The upscaling adoption of diversification practices is crucial for conserving biodiversity, mitigating climate change, and transforming food systems on local to global scales. This needs to be urgently acknowledged by scientist to enable better governance and evidence-based policymaking for an agricultural paradigm shift rooted in natural ecologies.

In order to inhibit growth factor-mediated cell signalling and cell proliferation in various cancers, Trametinib (TM) binds preferentially to Mitogen-Activated Protein Kinase Kinase 1 and 2 (MEK). This study marks a first impression in assessing the impact of bilosomes (BS) of TM on oral bioavailability. A thin-film hydration technique, and the optimisation was done using Box-Behnken Design. Quantities of sodium taurocholate (STC), cremophor EL and Span 60 were taken as independent variables. The optimized formulation depicted a low hydrodynamic diameter (158.32±10.24nm) and high entrapment efficiency (94.27±2.16%), zetapotential (-46.57±1.06 mV) and PDI (0.132±0.03). The TM-BS showed a biphasic release pattern and obtained significantly high penetration compared to the pure TM solution (p<.05). The modified formulation, which is an optimised TM-BS, demonstrated lower IC50 in comparison to A549 cells, according to the cytotoxic evaluation. Additionally, enhanced TM-BS dramatically raised cellular caspase-3 protein expression. The TM-BS killed A549 cells better than TM solution.

This paper presents a comparative analysis of intelligent reflecting surface (IRS) technology versus conventional amplify-and-forward (AF) and decode-and-forward (DF) relay schemes in wireless communication. By focusing on transmit power, energy efficiency, and the minimum IRS elements required to outperform traditional relaying, we explore IRS’s potential as an energy efficient alternative. Our analysis shows that IRS configurations, especially with optimal phase shifts, achieve substantial power savings and superior energy efficiency over to AF and DF relays. Moreover, IRS requires fewer elements to meet or exceed relaying performance under higher data rate demands, making it an ideal choice for energy conscious, high performance network designs. These results highlight IRS technology as a promising solution for sustainable, next-generation communication networks.

Background Traumatic brain injury (TBI) is a significant concern that often goes overlooked, resulting from various factors such as traffic accidents, violence, military services, and medical conditions. It is a major health issue affecting people of all age groups across the world, causing significant morbidity and mortality. TBI is a highly intricate disease process that causes both structural damage and functional deficits. These effects result from a combination of primary and secondary injury mechanisms. It is responsible for causing a range of negative effects, such as impairments in cognitive function, changes in social and behavioural patterns, difficulties with motor skills, feelings of anxiety, and symptoms of depression. Methods TBI associated various animal models were reviewed in databases including PubMed, Web of Science, and Google scholar etc. The current study provides a comprehensive overview of commonly utilized animal models for TBI and examines their potential usefulness in a clinical context. Results Despite the notable advancements in TBI outcomes over the past two decades, there remain challenges in evaluating, treating, and addressing the long‐term effects and prevention of this condition. Utilizing experimental animal models is crucial for gaining insight into the development and progression of TBI, as it allows us to examine the biochemical impacts of TBI on brain mechanisms. Conclusion This exploration can assist scientists in unraveling the intricate mechanisms involved in TBI and ultimately contribute to the advancement of successful treatments and interventions aimed at enhancing outcomes for TBI patients.

Alzheimer is a progressive neurodegenerative disease characterized by change in brain that led to the buildup of specific proteins, ultimately causing brain shrinkage and the death of brain cells. It is the leading cause of dementia, manifesting as a gradual decline in memory, cognitive abilities, behavior, and social functioning, which severely impairs a person’s ability to carry out daily activities. The complexity of Alzheimer’s poses significant challenges to modern medicine, making the development of new therapeutic strategies crucial. Indole derivatives, with their broad spectrum of pharmacological activities, have garnered attention for their potential in treating Alzheimer’s disease. This review provides a detailed summary of recent progress in developing indole derivatives as therapeutic agents for Alzheimer's disease. It thoroughly examines the pharmacological properties of various indole derivatives, including their mechanisms of action. These compounds have been shown to influence several processes, such as amyloid-beta aggregation, MAO inhibition, AChE and BuChE inhibition. Furthermore, this review discusses the structural modifications of indole derivatives designed to improve their therapeutic effectiveness

Cloud computing (CC) facilitates online computing resources on a pay-per-use basis. Recently, due to advancements in internet technologies, the popularity of CC has surged. Various users and organizations are adopting it for application, infrastructure, and platform as a service. Cloud storage is one of the major services that is widely being used by both organizations and individual users. Organizations like healthcare, governance, finance, defense etc., are also using the cloud infrastructure to store their sensitive data and thus making it more vulnerable to security attacks. This work proposes a secure cloud-based storage system called TrustStore that implements a dual security mechanism: one for the data in storage and the other for user authentication to access the data. TrustStore uses a time-based one-time password as the authentication mechanism, which is a time-constrained, unique temporary code generated for the user to gain system access. Additionally, to protect the data in TrustStore, cost-effective RSA and AES algorithms have been implemented to perform the encryption and decryption of data. The work has been implemented over various file types of different sizes and their uploading and downloading time has been calculated. This work also presents a case study in which financial data for a banking system is considered for protection against malicious activities by intruders.

Jack Bean Urease (Ure, EC. 3.5.1.5) was effectively immobilized on the glutaraldehyde‐activated amino‐functionalized Al2O3/SiO2 nanocomposite through covalent conjugations via Schiff base linkages. The chemical composition, size, crystal structure, surface morphology, and particle distribution of the prepared Al2O3 nanoparticles and Al2O3/SiO2 nanocomposite were studied by Ultraviolet visible spectroscopy (UV–visible), Fourier‐transform infrared spectroscopy (FTIR), X‐Ray diffraction analysis (XRD), scanning electron microscopy (SEM), energy‐dispersive X‐ray microanalysis (EDAX), and transmission electron microscopy (TEM) techniques. Immobilized and free urease exhibited the optimum catalytic activity at pH of 8.5, 60 °C, and pH of 7.8, 25 °C, respectively. Kinetic parameters (Km, Vmax) of bound (1.102 mM, 17.73 µM), and free urease (0.998 mM, 23.58 µM) were determined elucidating that the efficiency of urease was enhanced by immobilization. Moreover, immobilized urease preserved more than 50% of initial activity until the 9th repetitive cycle of utilization and sustained 50% of catalytic activity after 45 days of storage at 4 °C. Thereby, the current outcomes suggest that Al2O3/SiO2‐APTES‐Glu‐Urease bionanoconjugate would be a viable biocatalyst for enormous proteomic research and biotechnological applications.

The increasing relevance of biomaterials in medical curements and the care of aging populations has led to significant advancements in the development and modification of these materials. Hydroxyapatite (HAp), a biocompatible ceramic that mimics the composition of bone mineral, stands out for its remarkable abilities. Its stability in body fluids and ability to integrate with bone without causing toxicity or inflammation made it a prime candidate for biomedical utilization, particularly in odontology and orthopedics. Dental implants, which necessitate a strong interface with the jawbone to effectively support prosthetic devices, are enhanced by coatings of calcium phosphate-based materials like HAp. This enhances osseointegration, ensuring a strong bond and longevity of the implant. HAp may be synthesized both synthetically and from natural sources like mammalian bones, marine shells, and plants, each offering unique trace elements that improve its bioactivity. The synthesis of HAp involves differential technique, including chemical precipitation, and hydrothermal techniques, each impacting the final abilities of the material. The use of natural sources is especially promising, providing a sustainable, cost-effective alternative that retains essential biocompatibility. Hence, this article aims to explore the synthesis, properties, and biomedical applications of hydroxyapatite (HAp), with a special emphasis on its role in improving the performance and durability of dental implants. It also addresses the challenges in manufacturing and biocompatibility, offering insights into future advancements in this field. By addressing current challenges in manufacturing and biocompatibility, HAp paves the way for more effective and long-lasting dental treatments.

Sub-6 GHz frequency band offers enhanced channel capacity and adequate bandwidth for wireless 5G automotive applications. Such applications demand user equipment antenna terminal with multiple resonating frequencies with acceptable gain to ensure the reasonable data rate for effective communications. To meet these specific requirements a H-Shaped fractal slot patch antenna with shorting metal strip is presented for WLAN/WiMAX and sub-6 GHz 5G wireless applications. Further, Whale Optimization Algorithm (WOA) is used to find the optimum position for a shorting strip placed between the fractal slot patch and ground. The proposed antenna has been evaluated with a set of representative numerical simulations have been done as per design specifications. The projected antenna resonates at S-band and C-band frequencies aroundn41 band, n46 band and n78 band. The antenna has a peak gain of 13.2 dB in the resonating range. The measured result confirms the accuracy of antenna design and results are quite promising.

Due to the exponential increase in data volume, the widespread use of intelligent information systems has created significant obstacles and issues. High dimensionality and the existence of noisy and extraneous data are a few of the difficulties. These difficulties incur high computing costs and have a considerable effect on the accuracy and efficiency of machine learning (ML) methods. A key idea used to increase classification accuracy and lower computational costs is feature selection (FS). Finding the ideal collection of features that can accurately determine class labels by removing unnecessary data is the fundamental goal of FS. However, finding an effective FS strategy is a difficult task that has given rise to a number of algorithms built using biological systems based soft computing approaches. In order to solve the difficulties faced during the FS process; this work provides a novel hybrid optimization approach that combines statistical and soft-computing intelligence. On the first dataset of diabetes disease, the suggested approach was initially tested. The approach was later tested on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset after yielding encouraging results on diabetes dataset. While finding the solution, typically, data cleaning happens at the pre-processing stage. Later on, in a series of trials, different FS methods were used separately and in hybridized fashion, such as fine-tuned statistical methods like lasso (L1 regularization) and chi-square, as well as binary Harmony search algorithm (HSA) which is based on soft computing algorithmic approach. The most efficient strategy was chosen based on the performance metric data. These FS methods pick informative features, which are then used as input for a variety of traditional ML classifiers. The chosen technique is shown along with the determined influential features and associated metric values. The success of the classifiers is then evaluated using performance metrics like accuracy, precision, F-measure, computational time, and recall. On datasets, the accuracy obtained by hybridizing the lasso technique with the HSA is highly encouraging. Our proposed hybridized approach computes astonishing results with over 99% accuracy, 98.9% F1-score, 99% AUC, 97.7% precision and 100% recall on Breast cancer dataset and 99% accuracy, 99.3% F1-score, 99%AUC, 100% precision and 98.6% recall on diabetes dataset which helps physicians make accurate diagnosis and effective treatment regimens. The key novelty of our work lies in the fusion of Lasso with HSA, resulting in a hybrid optimization technique that outperforms individual methods, other hybrid approaches, and other recent approaches mentioned in recent state-of-the-art studies. The experimental research shows that the suggested hybrid technique helps clinicians make well-informed judgments, precise diagnoses, and efficient treatment plans for patients, eventually saving lives. It serves as a vital second opinion for them.

Breast cancer detection remains one of the most challenging problems in medical imaging. We propose a novel hybrid model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and EfficientNet-B0, a pre-trained model. By leveraging EfficientNet-B0, which has been trained on the large and diverse ImageNet dataset, our approach benefits from transfer learning, enabling more efficient feature extraction from mammographic images compared to traditional methods that require CNNs to be trained from scratch. The model further enhances performance by incorporating Bi-LSTM, which allows for processing temporal dependencies in the data, which is crucial for accurately detecting complex patterns in breast cancer images. We fine-tuned the model using the Adam optimizer to optimize performance, significantly improving accuracy and processing speed. Extensive evaluation of well-established datasets such as CBIS-DDSM and MIAS resulted in an outstanding 99.2% accuracy in distinguishing between benign and malignant tumors. We also compared our hybrid model to other well-known architectures, including VGG-16, ResNet-50, and DenseNet169, using three optimizers: Adam, RMSProp, and SGD. The Adam optimizer consistently achieved the highest accuracy and lowest loss across the training and validation phases. Additionally, feature visualization techniques were applied to enhance the model’s interpretability, providing deeper insight into the decision-making process. The Proposed hybrid model sets a new standard in breast cancer detection, offering exceptional accuracy and improved transparency, making it a valuable tool for clinicians in the fight against breast cancer.

This research delves into the intricacies of designing trajectories for unmanned aerial vehicles (UAVs) within a multi-UAV system, specifically addressing the challenges presented during simultaneous rescue operations in neighboring states. The unique scenario introduces a potential risk of UAVs from one state intersecting with those from others, leading to communication issues and the looming threat of collisions. These collisions not only cause delays in emergency operations but also result in additional costs for repairing damaged UAV components. In response to this critical challenge, the study proposes an innovative approach utilizing Genetic Algorithms to facilitate collision avoidance in a multi-UAV environment, tailored explicitly for disaster mitigation scenarios. This technique is an efficient solution to enhance the safety and effectiveness of UAV operations during disaster response and relief efforts. The proposed trajectory planning method uses a genetic algorithm, with the fitness function strategically designed to optimize two pivotal objectives: utility (maximizing the number of people saved postdisaster) and collision avoidance (minimizing conflicts between multiple UAVs as they navigate predetermined paths). The overarching goal of this approach is to strike a balance, aiming to maximize utility while concurrently minimizing the risk of collisions. By adopting this approach, the research significantly contributes to advancing the field of disaster response strategies, enhancing the overall efficiency of multi-UAV systems in complex and dynamic environments. The proposed solution not only addresses the immediate challenges posed by potential collisions but also underscores the importance of optimizing UAV trajectories to achieve maximum utility in postdisaster scenarios.

Effective load balancing and resource allocation are essential in dynamic cloud computing environments, where the demand for rapidity and continuous service is perpetually increasing. This paper introduces an innovative hybrid optimisation method that combines water wave optimization (WWO) and ant colony optimization (ACO) to tackle these challenges effectively. ACO is acknowledged for its proficiency in conducting local searches effectively, facilitating the swift discovery of high-quality solutions. In contrast, WWO specialises in global exploration, guaranteeing extensive coverage of the solution space. Collectively, these methods harness their distinct advantages to enhance various objectives: decreasing response times, maximising resource efficiency, and lowering operational expenses. We assessed the efficacy of our hybrid methodology by conducting extensive simulations using a cloud-sim simulator and a variety of workload trace files. We assessed our methods in comparison to well-established algorithms, such as WWO, genetic algorithm (GA), spider monkey optimization (SMO), and ACO. Key performance indicators, such as task scheduling duration, execution costs, energy consumption, and resource utilisation, were meticulously assessed. The findings demonstrate that the hybrid WWO-ACO approach enhances task scheduling efficiency by 11%, decreases operational expenses by 8%, and lowers energy usage by 12% relative to conventional methods. In addition, the algorithm consistently achieved an impressive equilibrium in resource allocation, with balance values ranging from 0.87 to 0.95. The results emphasise the hybrid WWO-ACO algorithm’s substantial impact on improving system performance and customer satisfaction, thereby demonstrating a significant improvement in cloud computing optimisation techniques.

Wireless Sensor Networks (WSNs) are essential in various applications such as environmental monitoring, healthcare, and smart cities. However, the limited battery life of sensor nodes is still the major challenge to energy efficiency. In this paper, we present a new method based on the Spotted hyena optimization (SHO) for optimizing the clustering in wireless sensor networks (WSNs) to maximize the network lifetime and enhance energy efficiency. The SHO movement simulates the social behaviors and cooperative prey ambush strategies of spotted hyenas, thus effectively balancing exploration and exploitation in the clustering mechanism. The proposed SHO-based clustering method selects the optimal CHs to minimize energy consumption, reduce the energy level, and maximize the transmission performance of data transmission. Using available current clustering techniques such as TEEN, LEACH, LEACH-SF, and DEEC, a performance comparison is made with the help of MATLAB 2020. The experiments show that SHO-CH-WSN can extend the network life cycle and achieve a more balanced energy distribution among different nodes. Moreover, it achieves a 4% lower total network deployment cost compared to the conventional techniques. The mortality rate of each node is improved by 4% and energy efficiency has been enhanced by 3% using the proposed network. However, the SHO has a few cautions such as falling into local optima, becoming computation-heavy with increasing network size, in addition to needing to fine-tune various parameters. In future work, we will address these limitations to improve the applicability of our approach in the wild. The results show that in the case of SHO, First nodes die after 1300 rounds and Half of the nodes die after 1425 rounds, which is more than the other metaheuristic techniques. Similarly, for energy consumption in scenario 3, 0.50 J energy is spent after 1350 rounds whereas the same amount of energy is spent in other algorithms in very less rounds. The results show how SHO is optimizing energy consumption and lifetime. The results in this work indicate that SHO is a potential solution for the design of energy-efficient WSNs and could be a candidate for the upcoming wireless communications techniques.

Coumarins, naturally occurring benzopyrones, have garnered significant attention due to their diverse pharmacological activities and therapeutic potential. Derived from natural sources and synthetic routes such as the Perkin and Pechmann reactions, these compounds exhibit a broad spec-trum of biological activities, including antioxidant, anti-inflammatory, antimicrobial, anticancer, an-tidiabetic, and neuroprotective effects. The structure-activity relationship of coumarins highlights the critical role of substitutions at specific positions on the benzopyrone ring, enhancing their efficacy and selectivity. Notable applications include anticancer activities, with coumarin derivatives inhibit-ing tumor growth and inducing apoptosis in breast cancer and melanoma cells, and neuroprotection, particularly in Alzheimer's and Parkinson’s diseases, through acetylcholinesterase inhibition and β-amyloid modulation. Additionally, coumarins show promise in combating drug-resistant pathogens and oxidative stress. Despite their potential, challenges such as toxicity and bioavailability remain. Future research should focus on optimizing coumarin scaffolds and advancing clinical evaluations to establish their role as next-generation therapeutic agents.

Monitoring vital signs using a photoplethysmogram (PPG) signal has gained considerable attention, allowing users to monitor anyone, anywhere, and anytime with an objective. In recent years, advances in wearable technology and signal processing techniques have paved the way for accurate and reliable vital sign monitoring using PPG signals. Early detection of cardiovascular diseases can help the physician treat the disease promptly; thus, realtime monitoring of vital signs has emerged. Any deviation in the threshold value of vital signs can indicate potential threats to the cardiovascular system. The need to monitor vital signs in realtime using wearable devices has attracted the interest of the healthcare industry in developing simple and efficient vital sign estimation algorithms. This research introduces a framework to estimate the following important vital signs: heart rate (HR), respiratory rate (RR), blood pressure (BP), and blood oxygen saturation (SpO2), concurrently by overcoming the limitations posed by state-of-the-art techniques that primarily focus on individual or two vital sign estimations. Our proposed approach leverages signal processing techniques to determine the above-mentioned vital signs seamlessly and accurately. This innovation enhances the efficiency of vital sign monitoring and presents a unified solution for comprehensive health assessment. The widespread use of wearable devices for monitoring realtime health status in everyday life manifests in using PPG sensor-enabled wearable devices to perform more complex computational tasks. To date, the algorithms proposed to process an input PPG signal often use multiple processing steps to estimate any vital signs. This can increase the computational complexity of these algorithms, making it challenging to deploy devices with limited computational resources. The proposed work introduces a computationally efficient framework to estimate all four vital signs using the signal framework. The experimental results obtained with the proposed framework demonstrate that the proposed work outperforms the state-of-the-art estimation accuracy and computational complexity.