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Deep Learning-Based Signal Detection Techniques for Real-Time Communication in Fading Channels
Dependable signal detection has also been a major concern in real-time wireless communication especially in the case of fading channels that cause non-adaptive distortion and deteriorate the overall performance drastically. The conventional detection meth-ods, like the maximum likelihood detection, are not always adaptive in the circumstances of dynamic and therefore unpredictable channel conditions, and particularly in the cases when the statistical profiles are unknown or vary too quickly. In order to address these shortcomings, the papers introduce a new paradigm of deep learning signal detection trained to learn hierarchies and temporal patterns of raw received signals, which by their pas integrate convolutional neural networks (CNN) and recurrent neural networks (RNN). The trained architecture is end-to-end that is able to map the noisy distorted inputs to their symbols which are inherently transmitted in the context of channel state informa-tion. Heavy simulation over Rayleigh and Rician fading channels with different Doppler spreads and SNR values shows that the suggested approach shows substantial improve-ment over the traditional maximum likelihood and classical machine learning-based detec-tors regarding bit error rate (BER), inference latency and computational overhead. Such results emphasize the performance as well as the flexibility of deep learning model in very dynamic propagation conditions. On the whole, this paper draws the conclusion that deep learning is a perspective direction to solve the problem of real-time detection of a signal in next-generation wireless networks, such as a 6G or IoT edge setup. 2025, Society for Communication and Computer Technologies. All rights reserved. -
Residual-Based Statistical Process Control Charts in the Presence of Multicollinearity: an EWMA Framework with (RK) Estimator
Reliability monitoring of financial health requires strong control mechanisms, and the residual chart is an invaluable instrument to perform it. One of the key problems statisticians face while modeling is the problem of multicollinearity which arises when there is a strong correlation between independent variables leading to imprecise coefficient estimates and poor outcomes. To solve this problem and to make sure that the control chart works even with correlated data, we integrated a Weighted Moving Average Exponential smoothing chart within the modeling technique. The theoretical approach assures long-term variability and consistency of the residual control chart. These control charts are used to understand the process and the performances in various sectors. The charts can be used as analytical instruments to help recognize patterns, variations, or anomalies in economic indicators specifically in budget deficit data and facilitate rapid identification of any changes or inconsistencies in the fiscal deficit by policymakers. Further advances in statistical process control are rendered feasible by this study, which deepens the understanding and awareness of the potential uses and implications of the Weighted Moving Average Exponential smoothing chart for fiscal deficit data in the Economic realm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Tracking Sigmoid Regression with Multicollinearity in Phase I: An Approach Incorporating Control Charts
Regression and quality control are two crucial techniques that data analysis employs in improving the decision-making process. We use the sigmoid function to model the connection between independent factors and the dependent variable in sigmoid regression. When there is a significant correlation among the independent variables in a regression model, multicollinearity a statistical phenomenon exists. Multicollinearity presents problems with higher uncertainty when estimating individual coefficients possibly making it harder to identify each variable's distinct contribution to the model. By suggesting a control chart specifically designed for the sigmoid regression model, this research presents a strategy to address the impact of influential observations using regression control charts, by making use of principal component regression class estimators. Principal component regression merges from the principal component analysis and linear regression methodologies, aiming to alleviate multicollinearity issues and enhances the stability of regression models. The performance of the model is evaluated using Pearson's residuals, Deviance residuals, and residuals. This strategy is proven to be useful in real world situations demonstrated through an application in the field of sleep wellness disorder. In conclusion, this study introduces a unique control chart to manage multicollinearity in sigmoid regression, providing a new perspective on the topic to spot differences in the underlying process by highlighting trends in the residuals. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Residual-based MEWMA control charts in the presence of multicollinearity
Statistical Process Control has been performing a critical role in attaining quality assurance from historic times to the modern era. Examining and governing the process variables involves rigorous stages and several control charts. The multivariate process is considered for a more comprehensive understanding of handling multiple correlated variables of the process. The study here focuses on the unique creation and deployment of residual-based Multivariate Exponentially Weighted Moving Average control charts in the presence of multicollinearity, specially constructed and evaluated for Phase I and Phase II. The chart offers a reliable framework for understanding shifts in multivariate processes across time from minor to moderate changes in process parameters. Agro-Economy data of Indian States for the years 2019 and 2020 are utilized in an application example. The proposed residual-based MEWMA control charts detect out-of-control circumstances with few false alarms and this is critical for rapid interventions, resulting in optimal crop management and production. 2025, Prince of Songkla University. All rights reserved. -
Biocompatible Sodium Alginate Modified BaO2H2O2 Nanoparticles With Improved Therapeutic Efficacy Against Multidrug-Resistant Pathogens and Cancer Cells
The increasing problem of multidrug-resistant pathogens and the limitations of conventional therapies for cancer treatments require designing new therapeutic agents. BaO2H2O2 and BaSA nanoparticles were prepared and characterized to determine their antimicrobial, antifungal, and anticancer activities. The XRD confirmed the crystallite sizes to be 34 nm for BaO2H2O2 and 25 nm for BaSA. The UVvisible analysis confirmed the band gap energies as 4.13 and 4.11 eV for BaO2H2O2 and BaSA, respectively. A shift in the blue-green PL emission from 488510 nm in BaO2H2O2 to 535 nm in BaSA indicated increased oxygen vacancies. EDAX analysis demonstrated elemental variations due to SA modification, whereas DLS measurements showed a decrease in the mean size of the nanoparticles from 116.70 nm (BaO2H2O2) to 111.90 nm (BaSA). Antimicrobial activity was shown against Klebsiella pneumoniae, Shigella dysenteriae, Escherichia coli, Pseudomonas aeruginosa, and Proteus vulgaris, while a considerable enhancement of antifungal activity against Candida albicans was observed in BaSA. Against MG-63 osteosarcoma cells, BaSA exhibited lower IC50 values (21.5, 20.2, 18.7 ?g mL?1 at 24, 48, and 72 h) when compared with BaO2H2O2 (23.4, 22.5, 21.3 ?g mL?1). Zebrafish embryos tolerated BaSA at 0.5 mg mL?1, with developmental abnormalities observed only at 1.0 mg mL?1. 2025 John Wiley & Sons Ltd. -
Economic Insights: The Computational Intelligence Perspective on Finance
Using technological advancements and shifting risk landscapes as a driving force, this abstract investigates the revolutionary approaches that have reshaped risk mitigation in contemporary contexts. Introducing a new era of proactive risk management has been made possible by the combination of artificial intelligence (AI), machine learning (ML), and predictive analytics. Organizations are able to recognize patterns and anticipate potential risks with an accuracy that has never been seen before, thanks to these technologies, which analyze vast datasets. By extracting valuable insights from unstructured data sources, natural language processing (NLP) and sentiment analysis broaden the scope of risk assessment with their respective capabilities. Blockchain technology improves both transparency and security, particularly in the realm of financial transactions, thereby lowering the likelihood of fraudulent activity. Cloud computing makes dynamic risk modeling easier to accomplish, which in turn makes it possible to simulate real-time scenarios. The cumulative effect of these innovations not only improves the efficiency of risk reduction, but it also helps organizations develop risk management frameworks that are more agile and resilient. When navigating the complexities of a risk landscape that is constantly shifting, it is essential to strike a balance between technological advancements, ethical considerations, and transparency. 2026 by Apple Academic Press, Inc. -
Thermorheological effect on RayleighBard magnetoconvection in a biviscous Bingham fluid with rough boundary condition on velocity and Robin boundary condition on temperature
The thermorheological effect on the onset of RayleighBard convection in a biviscous Bingham fluid in the presence of a horizontal magnetic field is investigated considering rough boundary conditions on velocity and Robin boundary conditions on temperature. The viscosity of the electrically conducting fluid is assumed to be sensitive to temperature variation. Linear and global nonlinear stability analyses are performed using the Chebyshev pseudospectral method to determine the existence of instability or otherwise. A general interpretation is made from the results to show the effects of the magnetic field and the variable viscosity on the system's stability. The biviscous Bingham parameter and the Chandrasekhar number are shown to have a delay in the onset of convection, while the effect of temperature sensitivity is to advance the onset. It is found that the results of linear and global nonlinear stability are not in agreement, so the region of subcritical instability exists. Also, the results obtained for RayleighBard convection agree pretty well with those of Platten and Legros and Siddheshwar et al. for the limiting cases. 2023 Wiley Periodicals LLC. -
Study of Influence of Combustion on DarcyBard Convection with Inherent Local Thermal Non-equilibrium Between Phases
This work deals with a DarcyBard convection problem in the presence of combustion and with local thermal non-equilibrium between the fluid and the solid phases. The effects of combustion and local thermal non-equilibrium on the onset of convection is studied in the linear and nonlinear regimes. Unlike all reported local thermal non-equilibrium problems reported so far, the present problem has a unique situation of having thermal non-equilibrium not only in the perturbed state but also in the basic state. Further, we observe that local thermal non-equilibrium does not, under any circumstance, lead to local thermal equilibrium except in an approximate sense when the combustion is quite weak. The effect of combustion is to advance the onset of convection compared to that in its absence. The effect of local thermal non-equilibrium is to reinforce the effect of combustion. In the presence of both these effects, sub-critical instability exists. The results are obtained numerically and have implication in practical porous medium convection problems. 2022, The Author(s), under exclusive licence to Springer Nature B.V. -
The impact of feedback mechanisms on RayleighBard penetrative convection in micro-polar fluids
This study examines the effects of feedback control and internal heat sources on the onset criterion of RayleighBard convection (RBC) in a horizontal Boussinesq micropolar fluid layer. A linear stability analysis, employing the Chebyshev pseudospectral method, is conducted to compute the eigenvalues and assess the stability of the system under varying conditions. The analysis considers several parameters, including heat conduction, coupling, couple stress, scalar controller gain, and internal heat sources. The findings reveal that the introduction of internal heat sources destabilizes the system, while the scalar controller gain significantly delays the onset of convection, thereby enhancing system stability. Additionally, it is demonstrated that an increase in both the coupling and heat conduction parameters contributes positively to system stabilization, whereas an increase in the couple stress parameter hastens the onset of convection. Notably, the investigation indicates that the system demonstrates greater stability when the boundary is heated from above as opposed to from below. These results provide crucial insights for the control of heat transfer in micropolar fluids and suggest that optimizing the scalar controller gain, along with careful tuning of other system parameters, can significantly enhance stability. The implications of this research are substantial for the design of efficient fluid dynamical systems, particularly in scenarios requiring precise control over temperature, pressure, and flow, such as those encountered in chemical processing, power generation, and manufacturing. 2025 Elsevier Ltd -
THE MONOCHROME TAPESTRY OF SOLO EXISTENTIAL TRAVEL IN 21ST CENTURY HOLLYWOOD: A CRITICAL ANALYSIS
Solo existential travel films of Hollywood enjoyed their heyday in the first two decades of the 21st century with most of them emerging as cult classics that have inspired millions to venture out on backpacking trips. The solo travel beyond the margins of a materialistic society that promises the traveller some existential clarity, in theory, is a truly existential endeavour that lets the individual exercise their Sartrean freedom and responsibility. But a quick survey of the films produced by Hollywood over the decades reveals a rather stealthy racism within. Solo existential travellers in Hollywood films of the 21st century have predominantly been white Americans. Despite being a powerful tool to create ones meaning and authentic identity in society, solo travel is still an instrument of self-redemption that is kept away from people of colour, especially the black American community. The paper will look into the significance, relevance and consequences of this seemingly invisible omission. From an embodiment perspective, the paper will attempt to analyze the absence of racial diversity in the genre to shed light on why the coloured body is to find its space in Hollywoods tapestry of solo existential travel. Copyright 2024 Namitha Nandan. -
Cyanogenic glycosides: A sustainable carbon and nitrogen source for developing resilient Janus reversible oxygen electrocatalysts for metal-air batteries
Most of the transition metal based heteroatom doped carbon electrocatalysts, utilizes the fossil fuel derived commercially available precursors as source of nitrogen and carbon which may question our environmental generosity. Herein, we have developed Ni-based efficient bifunctional electrocatalysts using apple seeds (that contains cyanogenic glycosides) as the precursor for nitrogen and carbon. With tuning the temperature, we were able to optimize the nitrogen doping up to ?3 at.%. The optimized electrocatalyst catalyses the oxygen reduction reaction (ORR) process with muted peroxide generation (for 0.7500.1 V the % HO2 ? generation ?3 - 2%), preferential 4e? reduction pathways (n ? 3.93 to 3.98 in 0.750.1 V range) and electron transfer via inner-sphere electron transfer mechanism which ensures the maximum utilization of instituted active centres owing to the direct interaction of reactant species. Alike to ORR, the superior oxygen evolution reaction (OER) performance with smaller Eonset, EJ=10, Tafel slope and enduring accelerated stability test advocates its potential as a bifunctional oxygen electrocatalyst. Moreover, smaller potential gap ?E (EJ10_OER - E1/2_ORR) of 0.845 V further warrants the energy efficient OER/ORR process. A porotype of Al-air battery system using our catalysts as oxygen electrode and chocolate wafer as anode material is well capable of powering the light emitting diodes. This study hopefully opens a new avenue to explore cyanogenic glycosides plants product to develop multifunctional electrocatalysts. 2019 Elsevier Ltd -
Nicotiana genus: a green and sustainable source for designing of nitrogen-rich efficient carbon nanocomposites for the hydrogenation of nitrophenol and non-enzymatic glucose sensing
Transition metals based nitrogen-doped carbon nanocomposites have been envisioned as a potential replacement for precious metal-based nanostructures to catalyze a variety of reactions. Herein, we report the synthesis of a group of nitrogen-doped carbon nanocomposites derived from the Nicotiana genus family plant, e.g. tobacco, a highly nicotine rich entity, and iron nitrate mixture followed by their exploitation for the reduction of 4-nitrophenol (4-NP) and non-enzymatic electrochemical glucose sensing. The controlled study suggests that the pyrolysis of tobacco results in ?7 at.% of nitrogen doping, an important heteroatom to enhance the catalytic efficiency of nanocomposites. The kinetics of the reduction of 4-NP follow a pseudo-first-order reaction. The time constant is found to increase with the Fe content in the composite owing to the formation of FeNx centers. The separation of a catalyst with the aid of a magnetic field offers a huge add-on to vouch for the recovery of these catalysts. Along with the display of appealing catalytic reduction, its application to non-enzymatic electrochemical glucose sensing is also demonstrated. Overall, the Nicotiana genus can be used as nitrogen-carbon precursors for designing of targeted N-doped carbon-based composites that could be exploited for various applications. 2021 Elsevier Ltd -
Generative AI and the Future of Cyber Threats: Building Resilient, Trustworthy Defenses
Generative AI is transforming cybersecurity, introducing autonomous, adaptive threats that challenge traditional defences. Capable of producing realistic content, mimicking behaviour, and scaling deceptive attacks, GAI reshapes phishing, malware, deepfakes, and social engineering. Vulnerabilities in AI- generated code and synthetic data demand proactive, AI- driven countermeasures. This chapter explores XAIs role in transparency and trust, highlights emerging technologies for intrusion detection and predictive modelling, and emphasises ethical design, verification, and collaboration to build resilient infrastructures against next- generation intelligent cyber threats. 2026 by IGI Global Scientific Publishing. -
Multi Disease Identification in Tomato Plant using CNN and SVM
Tomato is a major trade crop; it is among the most widely consumed crops in daily life. Crop diseases reduce not only the quality of the crops but also their amount of production, thus, detection and identification of the specific diseases is of great importance. Diseases like the Mosaic virus, Bacterial Spot, and Yellow Leaf Curl Virus infect the tomato plant. The advanced detection and classification techniques are mainly employed in the diagnosis of these diseases. This helps in informing the farmers about the types of diseases that attack their crops. In this study, independent CNN and SVM classifiers built to classify the diseases. The CNN model extracts feature such as color and leaf edges from input images- then, it proceeds to classification. For SVM, PCA is applied for feature reduction in order to enhance performance and accuracy before classification. A dataset sourced from plant village has been utilized to train the network CNN and SVM. The proposed neural network model has been applied to categorize 4 types of tomato leaf conditions: one healthy and three diseased types of tomato leaves. The results show that the SVM approach achieves a classification accuracy of 94.33%, whereas the CNN model has slightly higher accuracy of 95.17%. 2025 IEEE. -
COVID-19 and the cry of the poor sensitivity and solidarity
[No abstract available] -
Machine Learning Approaches for Suicidal Ideation Detection on Social Media
Social media suicidal ideation has become a serious public health issue that requires creative solutions for early diagnosis and management. An extensive investigation of machine-learning techniques for the automated detection of suicidal thoughts in internet postings is presented in this research. We start off by talking about the concerning increase in information on social media about mental health issues and the pressing need to create efficient monitoring mechanisms. The research explores the several methods used to identify the subtleties of suicidal thought conveyed in text, photographs, and audio-visual information. These methods include sentiment analysis, natural language processing, and deep learning models. We look at the problems with unbalanced data, privacy issues, and the moral ramifications of keeping an eye on user-generated material. We also go over the research's practical ramifications, such as the creation of instruments for real-time monitoring and crisis response techniques. Through comprehensive experiments and benchmarking, we demonstrate the potential of machine learning in providing timely support for those in need, thereby reducing the impact of suicidal ideation on society. 2023 IEEE. -
Review on the Biogenesis of Platelets in Lungs and Its Alterations in SARS-CoV-2 Infection Patients
Thrombocytes (platelets) are the type of blood cells that are involved in hemostasis, thrombosis, etc. For the conversion of megakaryocytes into thrombocytes, the thrombopoietin (TPO) protein is essential which is encoded by the TPO gene. TPO gene is present in the long arm of chromosome number 3 (3q26). This TPO protein interacts with the c-Mpl receptor, which is present on the outer surface of megakaryocytes. As a result, megakaryocyte breaks into the production of functional thrombocytes. Some of the evidence shows that the megakaryocytes, the precursor of thrombocytes, are seen in the lungs interstitium. This review focuses on the involvement of the lungs in the production of thrombocytes and their mechanism. A lot of findings show that viral diseases, which affect the lungs, cause thrombocytopenia in human beings. One of the notable viral diseases is COVID-19 or severe acute respiratory syndrome caused by SARS-associated coronavirus 2 (SARS-CoV-2). SARS-CoV-2 caused a worldwide alarm in 2019 and a lot of people suffered because of this disease. It mainly targets the lung cells for its replication. To enter the cells, these virus targets the angiotensin-converting enzyme-2 (ACE-2) receptors that are abundantly seen on the surface of the lung cells. Recent reports of COVID-19-affected patients reveal the important fact that these peoples develop thrombocytopenia as a post-COVID condition. This review elaborates on the biogenesis of platelets in the lungs and the alterations of thrombocytes during the COVID-19 infection. 2023, SAGE Publications Ltd. All rights reserved. -
Improving maternal health by predicting various pregnancy-related abnormalities using machine learning algorithms
Over the past few decades, artificial intelligence has been showing its high relevance and potential in a vast number of applications, particularly in the healthcare domain. Having a healthy pregnancy is one of the best ways to promote a healthy birth. Getting early and regular prenatal care improves the chances of a healthy pregnancy. Complications involved in the individual's pregnancy need to be predicted on time accurately. AI can help clinicians to make decisions by assisting them in decision-making. In this regard, the objective of this chapter is to provide a detailed survey of various pregnancy-related abnormalities; and to explore various machine learning algorithms to classify/predict pregnancy-related abnormalities with higher accuracy. A generic framework that focuses more on classifying various features into normal and abnormal, and to be monitored patients to provide support and care during an emergency. 2023 by IGI Global. All rights reserved. -
An Early-Stage Diabetes Symptoms Detection Prototype using Ensemble Learning
Diabetes is one of the most increasing health issues that the whole world is facing. Recent research has shown that diabetes is spreading quickly in India. Having more than 77 million sufferers, India is actually regarded as the diabetes capital of the world. The lifestyle and eating patterns of people who move from rural to urban settings alter, which raises the prevalence of diabetes. Diabetes has been linked to consequences like vision loss, renal failure, nerve damage, cardiovascular disease, foot ulcers, and digestive issues. Diabetes can harm the blood arteries and neurons in a variety of organs. FPG (Flaccid Plasma Glucose) is a popular test that is done to find out whether a person is a diabetic patient or not. However, not all people consistently take medication and neither monitor their blood sugar levels on a regular basis. Early detection of this disease is also an important thing that people usually don't do. Technology these days has emerged a lot in the healthcare zone. Many prototypes have already been made for the detection of diabetes. The prototype discussed in this paper is an ensemble learning approach for the detection of diabetes in a very early stage. Ensemble learning which includes the use of multiple model prediction has been used to make the outcome stronger and more trustworthy. The overall accuracy achieved by the model is 96.54%. XGBoost also records the minimal execution time of 2.77 seconds only. 2023 IEEE. -
Dolastatins and their analogues present a compelling landscape of potential natural and synthetic anticancer drug candidates
Human cancer remains a leading cause of global mortality. Traditional treatment methods, while effective are often associated with substantial side effects, high technical requirements, and considerable expenses. Recently, anticancer peptides, such as dolastatin-type peptides naturally found in marine mollusc Dolabella auricularia, have gained attention due to their enhanced characteristics and specific targeting of cancer cells with minimal toxicity to normal cells. This review aims to provide a comprehensive summary of the anticancer activities of natural dolastatins and synthetic analogues over the past 35 years, focusing on their utilization in advancing cancer treatment strategies. This updated review encompasses a detailed analysis of numerous studies demonstrating the cytotoxic effects of dolastatins and their synthetic analogues on various human tumour cell lines. The analysis includes investigations into their ability to activate apoptosis pathways, inhibit cell cycle progression, and indirectly limit inflammation and angiogenesis in tumours. Both natural dolastatins and synthetic analogues have demonstrated significant anticancer properties through a variety of mechanisms in vitro and in vivo pharmacological studies. Some have even advanced to clinical trials, either alone or in combination with other agents, and have shown promising outcomes. The biological activities of dolastatins and their synthetic analogues offer a promising path in the development of more effective and sustainable anticancer drugs. Their specific action on cancer cells and relative non-toxicity to normal cells highlight their potential as superior cancer therapeutic agents. The current study provides a platform for the most recent preclinical and clinical research on dolastatins and their analogues. Further research into these marine peptides may contribute to the development of sustainable and efficient treatment models for cancer, filling a significant gap in the current cancer therapeutic portfolio. 2023 The Authors