Development of student intent-based educational chatbot system with adaptive and attentive DTCN on symmetric convolution approach.

The Accessing academic information like exam time tables, exam scores, syllabus and tutor information on institutional websites can be time consuming and tedious on behalf of the student. In this case, we will solve this dilemma by creating an intelligent and an automated Student Intent-based Educational Chatbot that integrates deep learning along with state-of-art optimization algorithms. Following the pre-processing of student inquiries with generic chatbot data sets, the system starts operating.

Ratcheting fatigue behaviour of advanced structural materials at their service temperatures.

Ratcheting fatigue behavior is a critical phenomenon in structural materials undergoing cyclic loading, particularly when combined with nonzero mean stress or multiaxial stress states. This manifests as the progressive buildup of plastic strain in one direction with each loading cycle, ultimately leading to material degradation and failure. This behavior is highly relevant in industries such as automotive, aerospace, nuclear, and civil engineering, where components frequently experience complex loading conditions.

Artificial intelligence powered crime scene analysis service.

Crime remains a major concern in modern culture. As a result, emphasizing prevention and ensuring prompt justice delivery are critical. In criminal investigations and law enforcement, forensic science plays a critical role.

Advancements in Biogas Digester Materials: A Review of Strength, Durability, and Suitability.

This paper explores the materials used to construct biogas digesters, essential for sustainable energy production. The study investigates various materials, such as thermoplastics like polyvinyl chloride (PVC), polyethylene (PE), and high-density polyethylene (HDPE), as well as traditional construction elements like masonry, stone, and concrete. It considers metals such as steel and composite materials, which all contribute to the efficacy and strength of biogas digesters.

Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach.

Improving soil health evaluation and crop output forecasting are essential for developing sustainable agricultural methods. By applying data-driven insights, farmers may optimize resources, increase productivity, and promote environmental sustainability. The intricacy of environmental conditions and the lack of access to trustworthy data make it difficult to anticipate crop yields and evaluate soil health accurately.

HEDL: Deep learning multiple approaches for early detection of depression using sarcastic text.

Sarcasm is particularly notorious towards mental health, and thus it is quite essential for early identification of depressive indicators. This paper introduces the Hybrid Ensemble Deep Learning model as novel for the task of the detection of sarcasm task, targeting the weaknesses which were found in traditional approaches of SVC, DT, RF, and LR by using unique combinations of CNN, LSTM, and GRU to capture the sarcasm patterns that appear fine feature representation and enhanced robustness and accuracy. Our model uniquely integrates the architectures of CNN, LSTM, and GRU into one framework for capturing more complex patterns in feature representation, accuracy, and robustness.

Novel load balancing mechanism for cloud networks using dilated and attention-based federated learning with Coati Optimization.

Load balancing (LB) is a critical aspect of Cloud Computing (CC), enabling efficient access to virtualized resources over the internet. It ensures optimal resource utilization and smooth system operation by distributing workloads across multiple servers, preventing any server from being overburdened or underutilized. This process enhances system reliability, resource efficiency, and overall performance.

Deep recurrent neural network with fractional addax optimization algorithm for influenza virus host prediction.

The accurate prediction of the host of influenza viruses is a significant challenge in bioinformatics, as it is crucial for understanding viral transmission dynamics and host-virus interactions. This research•Introduces a novel approach for predicting the host of influenza viruses by leveraging protein sequences.•Extraction of features, including sequence length, Amino Acid Composition (AAC), Dipeptide Composition (DPC), Tripeptide Composition (TPC), aromaticity, secondary structure fraction, and entropy from protein sequence.

Determination of Judd-Ofelt Parameters for LiGdY:Eu O Nanophosphor Synthesis by Combustion Method With Glycine as Fuel for Near UV Light Emitting Diode.

This paper introduces a combustion synthesis method utilizing glycine as an eco-friendly fuel for the synthesis of LiGd₂₋ₓYₓ:EuO₃ nanophosphors. Glycine, being a naturally occurring amino acid, serves as a sustainable, renewable, and low-emission alternative to conventional fuels, minimizing the environmental impact of the synthesis process. The research focuses on analyzing Judd-Ofelt (JO) parameters to explore the luminescent properties and optical performance of the nanophosphors, with a specific emphasis on their application in near-ultraviolet (UV) light-emitting diodes (LEDs).

Analyzing Oxygen Atom Distribution in FDA-Approved Drugs to Enhance Drug Discovery Strategies.

Despite advancements in molecular design rules and understanding biochemical processes, the field of drug design and discovery seeks to minimize the number and duration of synthesis-testing cycles to convert lead compounds into drug candidates. A promising strategy involves gaining insightful understanding of key heteroatoms such as oxygen and nitrogen. This work presents a comprehensive analysis of oxygen atoms in approved drugs, aiming to streamline drug design and discovery efforts.

Species identification for Indian seafood markets: A machine learning approach with a fish dataset.

The Indian sea fish market is a dynamic and significant sector, contributing to both the domestic economy and the global seafood trade. This fish dataset is specifically curated for machine learning applications in the Indian seafood industry. It includes a comprehensive collection of images representing eight commercially significant fish species native to Indian waters, comprising a total of 8488 images.

Secure multimedia communication: advanced asymmetric key authentication with grayscale visual cryptography.

The secure authentication of user data is crucial in various sectors, including digital banking, medical applications and e-governance, especially for images. Secure communication protects against data tampering and forgery, thereby bolstering the foundation for informed decision-making, whether managing traffic, enhancing public safety, or monitoring environmental conditions. Conventional visual cryptographic protocols offer solutions, particularly for color images, though they grapple with challenges such as high computational demands and reliance on multiple cover images.

Novel energy efficient RND inverter using quantum dot cellular automata in nanotechnology.

Quantum-Dot Cellular Automata (QCA) is a promising technology for designing high-performance and efficient logic circuits, surpassing traditional Complementary Metal Oxide Semiconductor approaches. In today's digital era, the demand for digital circuits with high speed, device density, and energy efficiency is paramount. This paper focuses on the innovative Rotated Normal Cells with Displacement (RND) inverter model, employing normal and rotated cells with a 10 nm displacement through a cell interactive method.

Securing image-based document transmission in logistics and supply chain management through cheating-resistant visual cryptographic protocols.

In today's digital landscape, securing multimedia visual information-specifically color images-is of critical importance across a range of sectors, including the burgeoning fields of logistics and supply chain management. Traditional Visual Cryptography (VC) schemes lay the groundwork for encrypting visual data by fragmenting a secret image into multiple shares, thereby ensuring no single share divulges the secret. Nevertheless, VC faces challenges in ascertaining the integrity of reconstructed images, especially when shares are manipulated maliciously.

Performance analysis of biofuel-ethanol blends in diesel engine and its validation with computational fluid dynamics.

The engine tests aimed to produce comparable data for fuel consumption, exhaust emissions, and thermal efficiency. The computational fluid dynamics (CFD) program FLUENT was used to simulate the combustion parameters of a direct injection diesel engine. In-cylinder turbulence is controlled using the RNG k-model.

Adaptive windowing based recurrent neural network for drift adaption in non-stationary environment.

In today's digital era, many applications generate massive data streams that must be sequenced and processed immediately. Therefore, storing large amounts of data for analysis is impractical. Now, this infinite amount of evolving data confronts concept drifts in data stream classification.

Novel Face Mask Detection Technique using Machine Learning to control COVID'19 pandemic.

The COVID-19 pandemic has been scattering speedily around the world since 2019. Due to this pandemic, human life is becoming increasingly involutes and complex. Many people have died because of this virus.

Healthcare Assistance to COVID-19 Patient using Internet of Things (IoT) Enabled Technologies.

The IoT can lead to disruptive healthcare innovation. Research articles on IoT in healthcare and COVID-19 pandemics are thus researched in order to discover the potential of this technology. This literature-based research may help professionals to explore solutions to associated issues and battle the COVID-19 epidemic.