Advancements in cyberthreat intelligence through resource exhaustion attack detection using hybrid deep learning with heuristic search algorithms.

The Distributed Denial of Service (DDoS) attack is uncontrollable and appears in different patterns and shapes; accordingly, it is not easily detected and solved with preceding solutions. A DDoS attack is the most serious threat on the Internet. These attacks became a preferred weapon for cyber extortionists, terrorists, and hackers.

A holistic methodology for evaluating flood vulnerability, generating flood risk map and conducting detailed flood inundation assessment.

Flood risk assessment (FRA) is a process of evaluating potential flood damage by considering vulnerability of exposed elements and consequences of flood events through risk analysis which recommends the mitigation measures to reduce the impact of floods. This flood risk analysis is a technique used to identify and rank the level of flood risk through modeling and spatial analysis. In the present study, Musi River in the Osmansagar basin is taken in to consideration to evaluate the flood risk, which is located at Hyderabad.

Experimental study on the effect of microwave heating power on the drying behavior of onion slices.

Microwave drying has become a highly effective and energy-efficient technique for the processing of agricultural products, mainly onion slices. This method proves to be advantageous over traditional methods of drying like sun drying and hot-air drying, which are often accompanied by longer periods of drying time and quality loss in terms of color, texture, and nutrient content. Microwave drying allows moisture removal through volumetric heating and thus saves energy while preserving essential quality attributes.

Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments.

Cybersecurity has often gained much popularity over the years in a fast-evolving discipline, as the number of cybercriminals and threats rises consistently to stay ahead of law enforcement. Recently, cybercriminals have become more complex with their approaches, though the underlying motives for conducting cyber threats remain largely the same. Classical cybersecurity solutions have become poor at identifying and alleviating evolving cyber threats.

Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms.

Brain tumours (BTs) are severe neurological disorders. They affect more than 308,000 people each year worldwide. The mortality rate is over 251,000 deaths annually (IARC, 2020 reports).

An efficient camouflaged image segmentation with modified UNet and attention techniques.

Camouflaged object segmentation (COS) is a challenging task in computer vision where the objective is to recognize and precisely separate objects that blend in with their environment. Traditional models, including the standard UNet architecture, struggle with this task due to ambiguous object boundaries, texture similarity between object and background, and over-segmentation or under-segmentation caused by redundant skip connections. CAMO-UNet addresses these issues by including residual blocks which improve feature learning by easing the gradient flow and enabling deeper architectures.

Blockchain enhanced distributed denial of service detection in IoT using deep learning and evolutionary computation.

The Internet of Things (IoT) is emerging as a new trend mainly employed in developing numerous vital applications. These applications endure on a federal storage framework primarily concerned with multiple issues. Blockchain technology (BC) is one of the supportive methods for developing IoT-based applications.

3D bioprinting in tissue engineering: current state-of-the-art and challenges towards system standardization and clinical translation.

Over the past decade, three-dimensional (3D) bioprinting has made significant progress, transforming into a key innovation in tissue engineering. Despite the early strides, critical challenges remain in 3D bioprinting that must be addressed to accelerate clinical translation. In particular, there is still a long way to go before functionally-mature, clinically-relevant tissue equivalents are developed.

Enhanced effective convolutional attention network with squeeze-and-excitation inception module for multi-label clinical document classification.

Clinical Document Classification (CDC) is crucial in healthcare for organizing and categorizing large volumes of medical information, leading to improved patient care, streamlined research, and enhanced administrative efficiency. With the advancement of artificial intelligence, automatic CDC is now achievable through deep learning techniques. While existing research has shown promising results, more effective and accurate classification of long clinical documents is still desired.

An ECG signal processing and cardiac disease prediction approach for IoT-based health monitoring system using optimized epistemic neural network.

The rising prevalence of cardiac diseases necessitates advanced IoT-driven health monitoring systems for early detection and diagnosis. This study presents an efficient ECG-based cardiac disease prediction framework leveraging a multi-phase approach to enhance computational efficiency and classification accuracy. The Convolutional Lightweight Deep Auto-encoder Wiener Filter (CLDAWF) is employed for signal preprocessing, while the Quantized Discrete Haar Wavelet Transform (QD-HWT) extracts critical cardiac features, including P-wave fluctuations, QRS complex, and T-wave intervals.

Nature-powered bio-cathodes: Synergistic effects of laccase immobilization and green nanoparticles on enhanced PGEs for sustainable biofuel cells.

As part of growing energy demands and threats to ecology, there is an immense need for greener energy technologies that are novel, cost-effective, and ecofriendly. This paper presents a scalable strategy that uses biocompatible nanomaterials to modify the surface of a pencil graphite electrode (PGE) to enhance the performance of enzymatic biofuel cells (EBFC). Rosa centifolia flowers, an abundantly available plant extract, was used to green synthesize Ag and Cu nanoparticles and applied to various grades of pencil graphite lead surfaces (2H, HB, 2B) via dip-coating, and covalently immobilized with laccase enzyme.

An integrated review on the role of different biocatalysts, process parameters, bioreactor technologies and data-driven predictive models for upgrading biogas.

As energy consumption and waste generation from human activities continue to rise, the technology of anaerobic digestion (AD), which converts waste into bioenergy, has gained popularity. Biogas produced from AD commonly contains 60 % CH, 40 % CO and a minor fraction of impurities. Currently, several anaerobic reactors have been designed to upgrade the biogas with biomethane content above 90 %.

Annual effective dose assessment: Natural background gamma radiation at Kothapally village, Nalgonda district, Telangana, India.

The annual effective dose due to natural background gamma and radionuclides in soil samples was estimated in the indoors and outdoors of Kothapally village located in the vicinity of proposed Chitrial Uranium mineralized area. In the present study μR-survey meter and Thermoluminescence dosimeters (TLDs) were used for instantaneous and integrated measurements of natural background gamma radiation levels respectively. The annual effective dose to the general public, was found to be vary from 0.

A new H6 neutral point clamped transformerless photo voltaic inverter.

Transformerless photovoltaic (PV) inverters are widely used in grid-connected solar energy systems due to their high efficiency and compact design. However, conventional transformerless inverters suffer from oscillating common-mode voltage (CMV), which leads to higher common-mode leakage current (CM-LC) due to the lack of galvanic isolation. This issue adversely affects system performance, safety, and compliance with grid standards.

Investigations on mechanical and stress strain characteristics of geopolymer concrete reinforced with glass fibers.

Geopolymer concrete (GPC) is an eco-friendly alternative to traditional cement concrete and has sparked widespread interest due to its potential to reduce CO emissions. However, its mechanical performance, notably stress-strain behaviour for plain concrete and fiber reinforced concrete, has been a significant topic for development over the last few decades. This study examines the mechanical and stress-strain parameters of geopolymer concrete reinforced with alkali-resistant glass fibers with varying aspect ratios (444, 963) and volume fractions (0%, 0.

Cu-Ag/SBA-15 nano catalysts for the control of microorganisms in water.

Because of their uniform and regular channels, adjustable pore size, large surface area, controllable wall composition, high hydrothermal stability, ease of functional modification, and good accessibility of larger reactant molecules, mesoporous siliceous SBA-15 is of excellent catalyst carrier that is highly versatile and has been used extensively to prepare a variety of supported catalysts with ideal catalytic properties. In this study, we report the synthesis, characterization, and catalytic application of Cu-Ag/ SBA-15 nanoalloy catalysts towards the control of microorganisms in drinking water has been reported. The Cu-Ag/SBA-15 nanoalloy catalysts with different molar mass ratio of copper to silver (Cu:Ag = 1: 0, 0.

Hybrid salp swarm maximum power point tracking algorithm for photovoltaic systems in highly fluctuating environmental conditions.

The maximum power delivered by a photovoltaic system is greatly influenced by atmospheric conditions such as irradiation and temperature and by surrounding objects like trees, raindrops, tall buildings, animal droppings, and clouds. The partial shading caused by these surrounding objects and the rapidly changing atmospheric parameters make maximum power point tracking (MPPT) challenging. This paper proposes a hybrid MPPT algorithm that combines the benefits of the salp swarm algorithm (SSA) and hill climbing (HC) techniques.

Hybridization of synergistic swarm and differential evolution with graph convolutional network for distributed denial of service detection and mitigation in IoT environment.

Enhanced technologies of the future are gradually improving the digital landscape. Internet of Things (IoT) technology is an advanced technique that is quickly increasing owing to the development of a network of organized online devices. In today's digital era, the IoT is considered one of the most robust technologies.

Secure cloud computing: leveraging GNN and leader K-means for intrusion detection optimization.

Over the past two decades, cloud computing has experienced exponential growth, becoming a critical resource for organizations and individuals alike. However, this rapid adoption has introduced significant security challenges, particularly in intrusion detection, where traditional systems often struggle with low detection accuracy and high processing times. To address these limitations, this research proposes an optimized Intrusion Detection System (IDS) that leverages Graph Neural Networks and the Leader K-means clustering algorithm.

Augmenting cybersecurity through attention based stacked autoencoder with optimization algorithm for detection and mitigation of attacks on IoT assisted networks.

The Internet of Things (IoT) network is a fast-growing technology, which is efficiently used in various applications. In an IoT network, the massive amount of connecting nodes is the existence of day-to-day communication challenges. The platform of IoT uses a cloud service as a backend for processing information and maintaining remote control.

A novel energy efficient 4-bit vedic multiplier using modified GDI approach at 32 nm technology.

Multipliers are essential components within digital signal processing, arithmetic operations, and various computational tasks, making their design and optimization crucial for improving the efficiency and performance of integrated circuits. Among multiplier architectures, Vedic multipliers stand out due to their inherent efficiency and speed, derived from ancient Indian mathematical principles. This study presents a comprehensive analysis and comparison of 4-bit Vedic multiplier designs utilizing Gate Diffusion Input (GDI), Complementary Metal-Oxide-Semiconductor (CMOS), and Transmission Gate (TG) technologies, utilizing different adder architectures such as Ripple Carry Adder (RCA), and Carry Lookahead Adder (CLA), Carry Skip Adder (CSA).