GAMANAM: Global Advances in Multidisciplinary Applications in Next-Gen And Modern Technologies
Global Advances in Multidisciplinary Applications in Next-Gen And Modern Technologies (GAMANAM) ISSN (Online): 3107-653X Global Advances in Multidisciplinary Applications in Next-Gen And Modern Technologies (GAMANAM) is an international, peer-reviewed, open-access journal dedicated to publishing pioneering research that integrates science, engineering, life sciences, and technology. GAMANAM serves as a platform for interdisciplinary collaboration, fostering innovative methodologies and impactful solutions to address pressing global challenges. Aims and Scope The journal focuses on high-quality, original research from multiple disciplines, including but not limited to: Artificial Intelligence, Machine Learning, and Explainable AI (XAI) Internet of Things (IoT) and Smart Systems Biotechnology, Microbiology, and Applied Life Sciences Sericulture and Agricultural Innovations Pharmacy, Pharmacology, and Health Sciences Blockchain and Decentralized Technologies Sustainable Energy and Green Engineering Advanced Robotics and Automation Computational Methods in Science and Engineering Smart Cities, Healthcare, Agriculture, and Industrial Applications GAMANAM promotes cross-disciplinary collaboration among researchers, industries, and policymakers to generate impactful, technology-driven solutions. Mission and Vision Mission: GAMANAM is committed to fostering interdisciplinary research that drives technological advancement and practical innovation. Our mission is to bridge diverse fields—ranging from science and engineering to life sciences and modern technologies—to generate impactful research that contributes to sustainable development, societal progress, and global well-being. Vision: We envision a future where collaborative, cross-disciplinary innovation addresses the complex challenges of our time. GAMANAM aspires to become a leading platform for research that empowers academic, industrial, and policy communities to co-create solutions in areas such as healthcare, environmental sustainability, agriculture, and emerging technologies. Peer Review Policy All manuscripts submitted to GAMANAM are subject to a rigorous double-blind peer-review process, wherein both the reviewers and authors remain anonymous to each other. This process ensures the highest standards of academic integrity, objectivity, and scholarly quality. Each submission is evaluated based on its originality, methodological soundness, relevance to the journal’s scope, and overall contribution to interdisciplinary research. Plagiarism Policy All submissions to GAMANAM undergo rigorous screening using advanced plagiarism detection tools and AI-generated content detection systems to ensure academic integrity and originality. The acceptable similarity index for submitted manuscripts must be less than 10%, excluding references and commonly used terminology. Content generated using artificial intelligence (AI) tools must not exceed 5% of the manuscript and must be clearly disclosed, if used. Manuscripts exceeding these thresholds or found to involve unethical practices will be rejected immediately. GAMANAM follows strict guidelines aligned with international publishing ethics and reserves the right to take further action in cases of academic misconduct. Author Guidelines & Submission Details GAMANAM invites authors to submit original, unpublished research that aligns with the journal’s interdisciplinary scope and standards of academic excellence. Detailed submission guidelines, including manuscript formatting, referencing style, ethical requirements, and publication policies, are available on the [Submissions] page. For inquiries or assistance with the submission process, authors may contact the editorial office at editor@gamanamspmvv.in. Copyright & Licensing: All articles are published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors retain copyright and grant rights for sharing and adaptation with proper citation.
Articles in this journal are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. This license permits others to copy, distribute, and adapt the work, provided it is for non-commercial purposes, and the original author and source are properly credited.
Composition Analysis of Marketed Poly Herbal Formulations
Herbal formulations are dosage forms comprising single or multiple herbs or modified herbs in stated amounts to furnish definite nutritional, cosmetic application and offer its use in diagnosis, treatment, mitigation of diseases of human beings or animals, alter the structure or physiology of human beings or animals. Herbal formulations are prepared by subjecting herbal substances after size reduction to any of the processes like extraction, distillation, expression, fractionation, purification, concentration or fermentation. This study focussed to review the composition of 100 marketed poly herbal preparations and identify the most prevalently used herb in each formulation. This study helps reveal current trends in herbal drug formulation, supporting better standardization, rational ingredient selection, and consistency in herbal medicines. The findings also serve as a useful reference for manufacturers, academicians, and students in Pharmacognosy and herbal drug technology. Additionally, the study aids quality control, regulatory evaluation, and research prioritization by highlighting herbs that are most frequently incorporated into marketed preparations. Understanding commonly used herbs can guide future pharmacological, clinical, and safety studies, ensuring that research efforts focus on ingredients with widespread therapeutic use and commercial relevance
Herbal syrups, Tablets, Capsules, Ointments, Shampoos, Lotions
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
Bioplastic Production from Microalgae: A Green Technology Approach
Marine Microalgae such as O. salina, S. peavaleikii are a potential source for the production of bioplastic. Petroleum based plastic pollution is the accumulation of plastic in the environment, which can harm humans, marine life, and their habitats. It's a global issue that's linked to other environmental challenges like climate change and biodiversity loss. Microalgae based bioplastics has gained attention as a sustainable alternative to traditional petroleum-based plastics. Microalgae have become one of the more promising and environmentally safe feedstocks to produce bioplastics. The production and extraction of poly hydroxy butyrate’s from marine algal species (O. salina, S. peavaleikii) by solvent extraction method. Solvent extraction process is often considered the most common method for PHB extraction due to its high yield, purity low cost. The extracted PHB were characterized through UV-SPECTROPHOTOMETER. In Bioplastic used in different fields like food packaging, pharmaceuticals, agricultural and medicinal industries
Marine microalgae, Photoautotrophic, PHB (Poly hydroxy butyrates) and Bioplastic
Holoveda-The Fusion of Holography and Ayurveda for Future Wellness
With the global health paradigm moving towards integrative and personalized care, the combination of Ayurveda's proven traditional system with advanced visualization technologies such as holography leads to extraordinary outcomes. 'Holoveda' embodies this advanced integration, where non-invasive holographic instruments improve diagnostic accuracy, patient learning, therapeutic visualization, and surgical planning within Ayurvedic contexts. This review examines how holography has the potential to transform Ayurvedic practices by facilitating real-time, three-dimensional mapping of Doshi imbalances, organ functions, and biofield energies, thereby merging ancient knowledge with contemporary advancements. The document additionally examines ongoing studies, practical applications, and commercial models, highlighting the importance of cross-disciplinary teamwork, ethical implementation, and policy backing. In recent years, the wellness sector has experienced a rise in technological advancements, especially in the combination of conventional medicine and contemporary technologies. Among these, the idea of Holoveda is remarkable as a blend of Ayurveda, the age-old study of life, and holography, a cutting-edge visualization technology. This examination seeks to investigate the rise of Holoveda, its foundational concepts, uses, and the possibilities it presents for transforming holistic health and wellness approaches. Holoveda offers a unique approach to improving human well-being in the 21st century and beyond by merging the individualized healing principles of Ayurveda with holographic visualizations.
Ayurveda, Holography, Holoveda, Visualization technology, Dosha mapping, 3D diagnostics
A Brief Review on Gestational Diabetes: Insights into Current Understanding and Future Perspectives
The term "Gestational Diabetes Mellitus" (GDM) is the term used to describe different levels of glucose intolerance that were first discovered during pregnancy. About 2-10% women worldwide experience Gestational Diabetes. In order to identify GDM, pregnant women are screened for clinical risk factors and at risk women are tested for impaired glucose tolerance, which is typically, but not always, mild and asymptomatic. The wide range of physiological and genetic problems that define diabetes outside of pregnancy also seem to be the cause of GDM. It is true that women with GDM have a higher chance of getting diabetes outside of pregnancy. As a result, GDM offers a rare chance to research the early causes of diabetes and create preventative measures. Obesity, impaired glucose metabolism, and cardiovascular disease are among the long-term concerns that GDM raises the risk of for both the mother and the kid. In most parts of the world, preventive measures are not widely used, making it difficult to treat mothers and infants optimally during long-term follow-up.The main treatments for GDM include dietary changes and increased exercise, but when normoglycemia cannot be reached, medication typically insulin is utilized.
Gestational Diabetes Mellitus, Obesity, Glucose metabolism, Insulin, Normoglycemia
Advantages of CCD Spectrometers over Traditional Spectrophotometers
Spectroscopy is one of those key instruments in science that quietly reveals the hidden stories of materials—from the makeup of a new drug to the pollutants in a river. For decades, traditional spectrophotometers, which scan through wavelengths one by one using moving parts, have been the reliable workhorses in labs worldwide. But now, charge-coupled device (CCD) spectrometers have stepped in with a smarter approach: they capture the entire spectrum in a single snapshot using an array of tiny light-sensitive pixels. This article takes a closer look at why CCD systems are often the better choice today. They bring real gains in speed, sensitivity, resolution, and everyday reliability, making them perfect for fast-paced experiments, fieldwork, and high-volume testing. We will walk through how both technologies work, compare them side by side, and see why so many researchers are making the switch. In the end, CCD (Charge Coupled Device) spectrometers feel less like an upgrade and more like the natural next step for modern science
Spectroscopy, CCD Spectrometer, Traditional Spectrophotometer, Optical Detection, Beer–Lambert Law, Solid-State Detectors
Millets: An Ancient Grain for Modern Nutritional Security and Sustainable Development
In 2023, millets were given global recognition as the International Year of Millets. These crops are cultivated in many parts of the world and play an important role in food and nutritional security. Millets are valued for their rich nutrient content and natural health-supporting properties. They contain beneficial antioxidants and are known for their anti-aging, antibacterial and potential cancer-preventive effects. In addition, millets provide essential vitamins such as riboflavin, thiamine, and niacin and beta carotene, along with important minerals that support overall well-being. Among many other health benefits, millets are effective against malnutrition, diabetes mellitus, cardiovascular disease and gastrointestinal tract disorders. In the process of ensuring future food and nutritional security, millet production can maintain the productivity of dry areas. Although millets are a significant traditional food of India with numerous nutritional and therapeutic uses, people are less aware of their importance. This brief overview presents the growing role of millets in health and nutrition
Millets, diabetes mellitus, cardiovascular disease and nutritional security
Fake Product Detection System Using Blockchain
One of the most significant challenges in today's retail market is the widespread counterfeiting of products. Counterfeit products are inferior replicas of authentic brand names. Various techniques have been implemented at different times to tackle the issue of counterfeit products, including RFID tags, artificial intelligence, machine learning, and product identification systems. But these methods have disadvantages such as product id can copy from a genuine product to a fake product, artificial intelligence and machine learning need high computational power to do operations, and many more methods adopted but a fulfilled method has not been developed. In this project, we have attempted to enhance the identification of counterfeit goods using blockchain technology. Our approach involves maintaining the supply chain of products at each stage of the product's journey to a new party, facilitated by a unique product identifier. Blockchain helps us to store the supply chain of products as a blockchain-based system makes a decentralized system and one of the main advantages of blockchain is that if the data is recorded in the system then nobody can change it at any cost so it makes our data more secure and protected from the third parties
Counterfeit (Fake) product, Blockchain, Supply Chain, Product ID, Blockchain, Supply Chain, Transaction history.
Cybersecurity Intrusion Detection in Industry 4.0 WSN’s Using Ml/Dl
Industry 4.0 is revolutionizing manufacturing and industrial processes by integrating intelligent technologies, including Wireless Sensor Networks (WSNs). However, this increased reliance on connected devices exposes these networks to cyber threats. To address this vulnerability, this article proposes an intrusion detection system for Industry 4.0 WSNs based on Machine Learning (ML) and Deep Learning (DL). Specifically, we employ ensemble learning methods, such as Stacking Classifier, XGBoost, and AdaBoost, to optimize detection accuracy and minimize false alarms. By applying our methods to real-world data, we demonstrate superior performance compared to existing intrusion detection models.
Industry 4.0, Wireless Sensor Networks (WSNs), Cybersecurity, Intrusion Detection, Machine Learning, Deep Learning, Stacking Classifier, XGBoost, AdaBoost
Total Domatic Number of Involutory Cayley Graph
The Involutory Cayley graph is a graph with vertex set Zm and the edge set E={ab:a-b∈I_(x ) orb-a∈I_x } for all m>1, where Ix={n∈Z_m ∶ n^2≡1(modm)} and it is denoted by G(Zm,Ix ). In this paper the concept of Total domination and Total domatic number for the Involutory Cayley graph is discussed and the results are presented at different n values of G(Zm,Ix ).
Involutory set, Involutory Cayley graph, Total domination, Total domatic number.
Development and Standardization of Banana Flour, Wheat Bran and Chia Seeds Biscuits
A convenience food that falls within the baked food category is the biscuit. Because of its many benefits, including low production costs, ease of carrying and eating, and extended shelf life, biscuits have become popular among a broad range of consumers. Using banana flour, wheat bran, and chia seeds, biscuits are the goal of this investigation. Since starch and fiber in green bananas cannot be metabolized in the small intestine, they resist digestion. Instead, in the large intestine, they are fermented by gut bacteria. Wheat bran contains phytochemicals (including phenolic acid and alkylresorcinols). Present study, biscuits were developed keeping in mind. The biscuits were developed by incorporating banana flour, wheat bran and chia seeds in different proportional into the basic biscuit recipe. Variation -2 was highly acceptable than the remaining variations
Biscuits, Banana flour, Wheat flour, Chia seeds, Resistant Starch
Development and Standardization of Palmyra Leather Bar
The ongoing study was engaged in fabricating a novel product from Palmyra palm. The soft orangish- yellow mesocarp of the ripe fruit (Pulp) is used to prepare "Palmyra leather bar" with all-natural ingredients like, jaggery and lemon juice. Three variations (V1, V2 and V3) were planned with different composition to standardize the product, all the variations were subjected to sensory evaluation through 5-point hedonic scale rating based on which, one variation was refined for the further processing. The Sensory and textural attributes of V2 were found to be similar to that of commercially available mango and other fruit leathers in the market. This value addition and development of novel process for Palmyra palm leather bar not only brings variation in flavors in the line of fruit leathers, but also an effective solution to improve the consumption of less known edible parts of this indigenous plamyra palm plant which has high nutrition profile. The nutritive value of V2 Palmyra leather bar is energy 253k.cal, carbohydrates 60.7gms, protein 1.83gms, fat 0.3gms, fiber 1.67gms, Ascorbic acid 9.5mg , Iron 0.72mg, Calcium 5.25mg, Phosphorus 9mg, Magnesium 6.12mg, Potassium 12.9mg, ash 2.53gms; flavonoids 8.6709mg QE/g and phenolic content 8.126±0.027µg GAE/g. The Palmyra leather bar concentrate samples were analyzed for total plate count, yeast and mould count in different time periods i.e. on 0th day, 15th day 30th day and 60th day and the values are nil. This observation gives the scope of estimating that the Palmyra leather bar shelf life may increase to 6 months based on storage conditions
Palmyra palm, Leather bar, jaggery, Phytochemicals, Nutrients
Involutory Cayley Graph and its Topological Indices
For the positive integer m>1, G(Z_m,I_x ) is Involutory Cayley graph with vertex set Z_m={1,2,3,…..,m-1} and the edge set Z={ab:a,b∈Z_(m ,) a-b∈I_x or b-a∈I_x }where I_x={n∈Z_m ∶ n^2≡1(mod m)}. In this paper, degree-based Zagreb indices, Shigehalli and Kanabur indices and Harmonic index for the Involutory Cayley graph are discussed and the results are presented.
Involutory Cayley Graph, Zagreb indices, Shigehalli and Kanabur indices, Harmonic index.
On The Total Domination and Connected Total Domination Sets of G_(p,q)^M GRAPH
In graph theory, the theory of domination has several applications in various fields of science and technology, which is considered as a turn up field of research. In real life, it is extremely important in fields like network design, wireless sensor networks, logistics,mobile computing, telecommunication and others. Problems with facility location, communication or electrical network monitoring can lead to dominance. Undirected graphs is one of the most excellent models in connection with distributed computation and parallel processing. A set is said to be a dominating set of the graph if every vertex in is adjacent to at least one vertex in The domination number of the graph is the minimum cardinality of a dominating set of In this paper, some results on total dominating sets and connected total dominating sets of graph on a finite subset of natural numbers are presented and the domination numbers are obtained for various values of p, q
Dominating sets, Total Dominating sets, connected total domination sets, total domination number and connected total Domination number, G_(p,q)^Mgraph.
Topological Indices on Molecular Structure in Anti-HIV Drugs
Molecular topology plays a significant role in establishing quantitative structure-property relationships (QSAR) between chemical structures and their activity. QSAR provides directions to design optimized structures as potential therapeutic agents. In view of the contribution of topological indices (Zagreb indices) in the Human Immunodeficiency Virus-1(HIV-1) drug research, few reverse transcriptase inhibitors were selected, and their Zagreb indices were calculated using graph theory derived mathematical models.
Public health, Millet consumption, Nutrition education, Health awareness, socioeconomic status
Exploring a study on consumption of Millets from Madanapalle urban of Annamayya district in Andhra Pradesh
Public health aims to enhance well-being through disease prevention and health promotion at individual, community, and organizational levels. This study focuses on the role of awareness in influencing dietary behaviors, specifically the consumption of millets, which are highly nutritious and beneficial for managing various health conditions. Millets are rich in essential amino acids, B vitamins, and minerals such as calcium, iron, and magnesium. They offer numerous health benefits including anti-diabetic, anti-cancer, and heart-protective properties. The study was conducted in the urban area of Madanapalli to assess the awareness, acceptance, and consumption patterns of millets among households. A random sampling method was used to select 200 households for a community-based cross-sectional survey using a structured questionnaire. An awareness program was conducted through nutrition education. Before the intervention, 63.5% of respondents were aware of millet consumption; this increased to 71% post-intervention. Ragi, Jowar, and Bajra were the most commonly consumed millets, often incorporated into breakfast and savory dishes. The findings revealed a strong link between millet consumption and socioeconomic status, health conditions, and awareness levels. The study underscores the effectiveness of health education in influencing dietary practices and promoting millet consumption for better public health outcomes.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
A Network Pharmacology-Based Strategy for Predicting Active Ingredients and Potential Targets of Talinum portulacifolium in Treating Diabetes
Diabetes was a significant healthcare burden that severely impacts patients' quality of life. Its complex pathophysiological mechanisms, combined with limited available data, made effective management challenging and placed a substantial economic strain on healthcare systems. As researchers explored alternative treatment options, plant-based remedies gained attention for their potential therapeutic benefits. Among them, Talinum portulacifolium emerged as a promising candidate, with several in vitro studies demonstrating its effectiveness against diabetes. In the present study, the mechanism of action and anti-diabetic activity of T. portulacifolium were explored through network pharmacology. Phytoconstituents were sourced from literature and databases, including SwissADME and ADMET lab 3.0. Disease genes and targets of phytoconstituents were collected from GeneCards and SwissTargetPrediction, respectively. Common targets were determined through the application of a bioinformatic Venn as well as biological pathways, functional enrichment evaluations. Additionally, network construction was created in the form of a PPI network. The certain compounds performed a crucial role in diabetes development by influencing important genes. Through the application of network pharmacology analysis, Talinum portulacifolium was determined to display a potential preventive effect on diabetes by regulating diabetes-related signalling pathways. These results gave important information about the inherent mechanisms behind its anti-diabetic effect as well as supporting its potential as an herbal therapeutic drug for the treatment of diabetes
Time-Correlated Single-Photon Counting LiDAR: Quantum-Enhanced Precision for Atmospheric Remote Sensing
Time-correlated single-photon counting (TCSPC) LiDAR represents a pivotal advancement in remote sensing, leveraging ultra-sensitive detection of individual photons to achieve picosecond-resolution time-of-flight measurements. By synchronizing pulsed laser excitations with high-speed single-photon avalanche diode (SPAD) detectors, TCSPC enables the reconstruction of light intensity profiles from sparse photon arrivals, surpassing the limitations of traditional analog LiDAR systems in low-signal environments. This technique records the precise arrival times of photons relative to excitation pulses, facilitating high dynamic range profiling with minimal dead-time distortions. At its core, TCSPC LiDAR embodies quantum sensor technologies through its reliance on single-photon detection and quantum-limited sensitivity. Employing non-classical light sources and detectors, it approaches the Cramér-Rao bound for ranging accuracy, mitigating noise in photon-starved regimes and enabling entanglement-enhanced protocols for future scalability. Such quantum integration not only amplifies signal-to-noise ratios but also opens avenues for secure, quantum-secure atmospheric monitoring. In atmospheric applications, TCSPC LiDAR excels in profiling aerosols, clouds, and turbulence through obscurants like fog and smoke, generating high-resolution 3D point clouds for real-time environmental assessment. Ground-based prototypes have demonstrated robust vertical profiling up to kilometers, supporting climate modeling, pollution tracking, and aviation safety. Ongoing innovations, including time-gated variants and hybrid photodetectors, promise further enhancements in flux handling and distortion-free imaging, positioning TCSPC LiDAR as a cornerstone for quantum-augmented Earth observation
Time-correlated single-photon counting, TCSPC LiDAR, quantum sensor technologies, single-photon detection, atmospheric remote sensing
Harnessing Science, Technology, and Innovation to Combat Diabetes and Alleviate Poverty: An In Silico Approach to Natural Alpha-Amylase Inhibitors
Diabetes mellitus is a chronic metabolic disorder marked by hyperglycemia due to impaired insulin secretion or action, often leading to serious complications such as organ damage.While modern drugs like sulfonylureas and biguanides are widely used, they frequently cause adverse effects, prompting interest in safer, plant-based alternatives. α-Amylase inhibitors from medicinal plants are particularly important, as they can reduce glucose absorption. Computational chemistry, especially molecular docking, provides a cost-effective and efficient approach to drug discovery by predicting protein–ligand interactions, binding affinity, and inhibitory potential. In this study, phytochemicals from Pterocarpus santalinus, particularly Trimetrexate, were evaluated using molecular docking. Docking with human pancreatic α-amylase (PDB: 3OLD) and DPP-IV (PDB: 5Y7H) revealed that Trimetrexate showed strong binding affinities (–10.58 kcal/mol and –9.45 kcal/mol, respectively), surpassing standard drugs like Glibenclamide and Acarbose. SwissADME analysis confirmed favorable drug-like properties, including solubility, bioavailability, and absorption. These results suggest that Pterocarpus santalinus could be a valuable source for developing safer, low-cost therapies against Type 2 Diabetes Mellitus. The integration of science, technology, and innovation (STI) is crucial in tackling such health challenges. Computational tools like molecular docking accelerate drug discovery and reduce costs, making therapies more affordable. By combining medicinal plants and modern technology, accessible treatments can be developed to benefit low-income populations disproportionately affected by diabetes. Thus, the fusion of scientific research, technological innovation, and traditional knowledge can improve healthcare and contribute to poverty alleviation, supporting sustainable development goals.
Alpha-Amylase Inhibitors, Diabetes Mellitus, In Silico Docking, Molecular Docking, Pterocarpus Santalinus, Trimetrexate
Development of Federated Blockchain-IoT Public Health Network with Privacy-Preserving Analytics
The rapid expansion of Internet of Things (IoT) devices in healthcare has caused concerns around data privacy, interoperability, and system scalability. Conventional centralized data processing models expose sensitive patient information to security vulnerabilities and hinder collaborative analytics across healthcare institutions. To address these challenges, this paper proposes a novel Federated Blockchain-IoT Public Health Network that combines privacy-preserving federated learning with permissioned blockchain technology and cross-chain atomic swap protocols. This integration enables secure, efficient, and interoperable healthcare data analytics in a distributed environment. The framework employs homomorphic encryption and secure aggregation to protect patient data privacy while allowing collaborative model training on decentralized healthcare nodes
Federated Learning, Blockchain, IoT Healthcare, Privacy Preservation, Cross-Chain Interoperability, Epidemic Response, Secure Aggregation, Sharding, Network Security
Ensemble of 1D-CNN and LightGBM for Encrypted Traffic Differentiation and Attack Detection
Encrypted network data traffic has turned into a double-edged model , protecting user privacy while at the same time masking the malicious behavior from conventional detection systems. To overcome this issue, the system in this proposal comes up with an ensemble type model that combines one- dimensional Convolutional Neural infrastructure (1D- CNN) with Light Gradient Boosting type Machine (LightGBM) in order to effective way of classifies encrypted traffic and detect attacks. The 1D-CNN module is an effective related feature absorbs that learns to automatically capture complex temporal and spatial data patterns hidden within raw unseen traffic streams without any manual feature process. The deep representations will then refined by the LightGBM type model, which is particularly strong in efficient differentiation with the use of gradient-depending decision trees and capable of dealing with complicated decision boundaries with higher scalability. The advantage of deep learning's representational power and gradient boosting's strong decision-maker ability provides an extensive detection process that is capable of differentiation of benign from malicious data traffic in the presence of encryption. By utilizing both techniques together in a complementary type, the framework improves the reliability of detection, lowers false alarms, and improves adaptability to changing attack schemas, thus giving a feasible and smart solution for recent cybersecurity systems.
Encrypted Form of Traffic Differentiation, Attack Identification, 1D-CNN Mod, Light type GBM, Ensemble Based Learning, Supervisor Learning, Gradient Type Boosting, Threat Identification, Attacks Traffic Analysis
Dynamic Multi-Objective Framework for Migrating Live Virtual Machines in the Cloud
When it comes to data storage environments, cloud computing has been a very promising area of technology. Data centres housing IT (Information Technology) servers are spread out across the world by cloud service providers in response to the high demand for storage and processing power. Efficient energy utilisation for a large number of applications running on separate physical machines is especially challenging in cloud computing environments with a scientific focus, as tasks in resource distribution are dependent on the availability of those resources. The difficult task at hand is to allocate resources more efficiently while simultaneously lowering the energy cost of maintaining data centers, which impacts the quality of service (QoS) of running scientific workflow applications. This research presents a Novel Hybrid Heuristic Framework (NHHF) that uses significant solutions for resource allocation, task scheduling, and scientific workflow optimization through optimized energy utilization. With NHHF, you may migrate virtual machines (VMs) in a multi-objective mechanism that optimizes resource wastage and other parameters, and you can use an Optimized Energy Aware Migration technique to run tasks without dominating the process and reduce energy consumption. Without interfering with or influencing the execution of workflow activities, the multi-objective combinatorial optimization issue is efficiently solved by the suggested innovative hybrid heuristic framework. Energy consumption in data centers is decreased by our proposed approach. Compare the results of the proposed technique's simulation research to those of state-of-the-art technologies, such as distributed dynamic virtual machine management (DDVM), optimized dynamic virtual machine migration approach (ODVMMA), and dynamic voltage frequency scaling (DVFS), using CloudSim
Cloud Computing, Virtual Machine, VM Migration, Resource Allocation, Resource Utilization, Energy Consumption, Cloud data centers and Hybrid Heuristic Calculation.
Deep Analysis of Autism Spectrum Disorder Detection Techniques
Communication and social relations and behavioral disabilities come under ASD, a complex neurodevelopmental disorder. Early diagnosis has to be performed to intervene promptly to improve someone's developmental outcome who has such a disorder. This research provides an enhanced predictive framework for an early approach and diagnosis of ASD through state-of-the-art deep and machine learning methods. Two datasets are used to build a strong and robust classification model the Autism Prediction Dataset and the Autism Spectrum Disorder Screening Data for Toddlers in Saudi Arabia. Feature engineering, data preprocessing, and various algorithms are utilized in the methodology to assure high prediction accuracy. In addition to standard machine learning (ML) techniques of Random Forest and SVM, deep learning architectures such as Neural Network, MobileNet, Vision Transformer, MobileNet+LSTM, and GNN over band gradient boosting are experimented with. The main comparative measurements used for evaluating the models are accuracy, precision, recall, and F1-score. Additionally, to improve trust and transparency in clinical use, we can use Explainable AI (XAI) techniques are used to offer insights into model decisions. According to experimental findings, the performance of ASD prediction systems is considerably improved by integrating these cutting-edge models. The suggested strategy has a great chance of bolstering early screening initiatives and helping medical professionals make better-informed diagnostic choices. Real-world implementation and ongoing model improvement using bigger and more varied datasets are potential future directions.
MobileNet, Vision Transformer, MobileNet-LSTM, GNN, early detection, machine learning, deep learning, classification, explainable AI, predictive modeling, and autism screening.
Rice Leaf Disease Detection using Deep Learning and Model Ensembling Techniques
Rice is a common staple crop in rural areas where farmers face a critical threat of food insecurity, disease surveillance can significantly reduce loss of crop yield and extend the lifecycle of the farm. In this study, a novel deep learning and ensemble learning method is proposed for real-time disease detection of rice leaves. Pre-trained convolutional neural network models of ResNet152V2, DenseNet169, InceptionV3 and MobileNetV2 are evaluated to detect disease patterns presented in rice leaves with classification accuracy of 97. 5%, 97%, 97% and 96%, respectively. Using a stacking-based ensemble learning method, the best-performing models of these models are combined to obtain a meta-model accuracy of 99. 9%. A model of the proposed model classifies diseases observed in the rice leaf to ten categories. It is then trained and evaluated against a rice disease dataset and with performance metrics like accuracy and F1-score. A mobile application is then developed for real-time disease detection by which farmers can easily use this method for early intervention and crop management; hence promoting precision agriculture by a robust solution that significantly improves the crop yield and food security.
Rice Disease Detection, Ensemble Learning, ResNet152V2, InceptionV3, MobileNetV2, DenseNet169, Stacking, Transfer Learning, Disease Classification, Feature Extraction.
Securing Digital Systems: Pre-Login and Post-Login Security Landscape for User Protection
Pre-Login Security is a basic website system designed to prevent brute-force attacks during the login process. The system allows users to register and log in securely while monitoring failed login attempts. If incorrect credentials are entered three times, an authorization email is sent to the legitimate user to confirm the activity. If the user does not authorize the login and failed attempts continue, the system automatically blocks access to the website, effectively stopping further intrusion attempts. This mechanism adds an extra layer of verification before login and helps prevent unauthorized access. By proactively identifying and responding to suspicious activity, Pre-Login Security enhances the overall protection of web applications against brute-force threats.
Brute-force attack, authentication security, pre-login verification, failed login detection, account protection, email authorization, web application security, access control, login monitoring, cybersecurity.
Intelligent Systems in Borehole Rescue Operations: A Survey of Current Technologies
In this paper, the issue of children, who often fall into boreholes, is looked at,it leads to sadness for involved families, and it's a problem that happens frequently. It's very essential to consider methods that work effectively for rescuing children in such situations. Different current methods were examined, methods like digging near boreholes or using robots were mentioned for the rescue tasks. However, significant challenges exist with these methods because they often require much time, can be unsafe sometimes, and don’t always produce the intended results. After examining these, new ideas were then discussed that are being thought of to improve the rescue process. These ideas involve using certain types of sensors, drones equipped with cameras, and various advanced robotic devices designed for the task. Highlights of these innovative concepts were discussed in relation to how they could potentially help in saving a child trapped in a well. The argument presented is that new types of technologies, as seen with the Internet of Things, robotics, and systems involving computer vision, can contribute to making rescues not only faster but also significantly better as well.
Boreholes, Sensor Technology, Detection through Drones, IoT Technologies
Optimized Machine Learning Techniques for Accurate Autism Spectrum Disorder Diagnosis
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in communication, social interaction, and behavior. Early diagnosis and prompt intervention can help to maximize developmental outcomes. This work proposes a refined predictive ASD model using other advanced techniques of machine learning. We leverage two datasets—“Autism Spectrum Disorder Screening Data for Toddlers in Saudi Arabia” and the “Autism Prediction Dataset”—to develop a robust predictive model. The methodology includes extensive data preprocessing, feature engineering, and algorithm selection to improve classification performance. Various machine learning models, including Random Forest, Support Vector Machine (SVM), Gradient Boosting, and Neural Networks, are employed and evaluated based on accuracy, precision, recall, and F1-score. The study also integrates explainable AI (XAI) techniques to interpret model predictions, enhancing transparency in decision-making. Experimental results demonstrate that the proposed approach significantly improves the predictive accuracy of ASD detection compared to traditional methods. The findings suggest that AI-driven diagnostic tools can assist healthcare professionals in making more informed decisions, ultimately aiding in early intervention strategies. Future work includes expanding the dataset and refining models for real-world applications.
Autism Spectrum Disorder, machine learning, predictive modelling, data analysis, feature engineering, classification, neural networks, explainable AI, early diagnosis, autism screening.
AI-Powered Chatbot: Enhancing User Interaction and Information Accessibility at SPMVV
Artificial Intelligence (AI) has revolutionized various industries, with chatbots emerging as one of its most practical applications. This paper explores the different types of AI, categorizing them based on capabilities and functionality. It further discusses how AI-driven chatbots are transforming multiple domains, including education, customer service, and entertainment. By reviewing existing research, the study highlights the advantages and challenges of implementing chatbot technology in real-world scenarios. A significant focus of this paper is the development of a chatbot system for Sri Padmavati Mahila Visvavidyalayam (SPMVV). The chatbot is designed to assist users by responding to frequently asked questions using predefined rules and keyword-based recognition. The implementation is carried out using Python and Flask, ensuring efficient and dynamic responses to user queries. The methodology involves natural language processing techniques to enhance the chatbot’s accuracy and relevance. The chatbot is deployed on the PythonAnywhere platform, enhancing accessibility and automation. The results indicate that the chatbot significantly improves access to information, reducing the need for manual assistance. Users benefit from quick and accurate responses, making communication more efficient. Future enhancements may include the integration of machine learning algorithms to refine responses over time and improve the chatbot's adaptability to complex queries. This study contributes to the growing field of AI-driven conversational agents and their practical applications in academic environments.
Artificial Intelligence, Chatbot System, Keyword-Based Recognition, Natural Language Processing
Detection and diagnosis of arrhythmias using computational models
Accurate classification of Electrocardiogram (ECG) signals is crucial for early detection of arrhythmias and timely medical intervention. This study introduces a machine learning framework that combines three classifiers—Support Vector Machine (SVM), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN)—to analyze ECG signals from the MIT-BIH Arrhythmia Database. A total of 100,689 ECG signal segments were processed to extract 31 morphological features, such as QRS intervals and peak amplitudes. Experimental results showed that SVM achieved an accuracy of 88.31%, ANN (with 24 hidden neurons) reached 97.01%, and CNN attained a validation accuracy of 96.02%, demonstrating CNN’s advantage in automated feature extraction. These results highlight CNN’s potential for clinical applications by minimizing dependence on manual preprocessing. Future work will focus on refining deep learning architectures for real-time implementation.
ECG Classification, Arrhythmia Detection, Machine Learning, Deep Learning, SVM, ANN, CNN
An Optimized Edge Preserving Medical Image Fusion Framework Using NSCT for Improved Diagnostic Accuracy
Medical image fusion plays a crucial role in enhancing diagnostic accuracy by integrating complementary information from multiple imaging modalities. This study presents an optimized fusion framework that combines Contrast Limited Adaptive Histogram Equalization (CLAHE) for image enhancement, Non-Subsampled Contourlet Transform (NSCT) for multi-scale decomposition, guided filtering for edge preservation, and gradient-based weight computation for detail layer fusion. The proposed method effectively preserves critical structural details while minimizing information loss and fusion artifacts. Experimental results demonstrate that the method achieves a minimum improvement of 5.75% and a maximum improvement of 38.23% in information retention, while fusion loss is reduced by 6.09% to 79.52%, and fusion artifacts are minimized by 10.00% to 96.97% compared to traditional approaches. The fused images exhibit superior visual quality, enhanced contrast, and improved feature preservation, making the method highly suitable for medical diagnosis and treatment planning
Medical image fusion, Contrast Limited Adaptive Histogram Equalization, Non-Subsampled Contourlet Transform, visual quality, medical diagnosis and treatment planning
A Review of The Analytical Methods Established for FDA Approved Drugs In 2022
In response to the rising number of diseases, numerous new medications have been developed and approved by the FDA to treat various disorders. Before these drugs can enter the market, they must undergo extensive validation and analytical processes to ensure their purity and reliability. These approaches use a variety of analytical methods to gather information regarding the drugs. This overview covers a variety of analytical procedures, including UV spectrophotometry & chromatography approaches (including HPTLC, HPLC, and Gas Chromatography). Additionally, it discusses hyphenated techniques like LC-MS used in the development of newly approved drugs in 2022. In 2022, significant advancements were made in the refinement of these analytical methods, introducing more precise, rapid, and sensitive techniques for drug evaluation. A notable trend was the increased integration of automation and artificial intelligence in analytical processes, enhancing the accuracy of results and streamlining workflows, thereby facilitating quicker approval timelines for emerging pharmaceuticals.
FDA approved drugs, HPLC, LC-MS/MS, Analytical methods