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109 articles for “encoder”
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A study on Antibiotic Resistance: An Analysis of Molecular Mechanisms and Therapeutic Implications
Abstract: Antibiotic resistance (AR) represents one of the most critical existential threats to global public health, rapidly eroding the efficacy of established antimicrobial therapies and portending a return to the pre-antibiotic era. This analysis explores the intricate molecular landscape defining this crisis, focusing specifically on the primary mechanisms of action (MoA) utilized by major antibiotic classes—including cell wall inhibitors, protein synthesis inhibitors, and nucleic acid synthesis inhibitors—and the corresponding, diverse mechanisms …
Published in International Journal of Antibiotics · Vol. 3, Issue 1, 2026 · pp. 9–21 Read article
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A SHAP - Enhanced Voice-Based Conversational Agent for Agriculture Using BERT
Abstract: The integration of advanced artificial intelligence technologies into modern agriculture has become increasingly important for narrowing the persistent knowledge gap faced by farmers, especially in regions with limited access to expert advisory services. While state-of-the-art language models such as BERT (Bidirectional Encoder Representations from Transformers) demonstrate exceptional performance in understanding and generating natural language, their opaque “black-box” nature often limits user confidence, trust, and widespread adoption. Farmers may hesitate to …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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Expression Profiling of Immunomodulatory Genes in Canine Adipose Tissue–Derived Mesenchymal Stem Cells
Abstract: Adipose tissue–derived mesenchymal stem cells (AT-MSC) represent a heterogeneous population of self-renewing, multipotent progenitor cells residing in the stromal compartment of adipose tissue. Canine adipose tissue-derived mesenchymal stem cells have attracted significant interest in regenerative medicine due to their therapeutic potential in naturally occurring canine diseases and their translational relevance to human medicine. In the present study, canine adipose tissue–derived MSCs (cAT-MSCs) were isolated and differentiated into osteogenic, chondrogenic and …
Published in Research and Reviews : Journal of Veterinary Science and Technology · Vol. 15, Issue 1, 2026 Read article
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A Technical Blueprint for AI-Driven Localization in 6G Mobile Networks
Abstract: The advent of sixth-generation (6G) wireless systems promises unprecedented spatial resolution, ultra-low-latency, and pervasive connectivity, turning mobile localization from a peripheral service into a core enabler of immersive extended reality (XR), autonomous logistics, and digital twins. Yet, the sheer scale of dense terahertz (THz) deployments, the stochastic nature of reconfigurable intelligent surfaces (RIS), and the dynamic interference landscape render traditional model-based positioning techniques inadequate. This work investigates how artificial intelligence …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 13, Issue 1, 2026 · pp. 26–34 Read article
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Understanding Sentiment Trends Through Zero-Shot and Few-Shot Learning Models
Abstract: The requirement for large, manually labeled datasets is one of the main barriers to applying sentiment analysis algorithms in specialized or rapidly evolving disciplines in the present natural language processing (NLP) landscape. This work investigates a paradigm shift from traditional fully supervised learning to data-efficient methods, specifically zero-shot learning (ZSL) and few-shot learning (FSL). This study uses the advanced capabilities of instruction-tuned large language models (LLMs), like GPT-4, to assess …
Published in International Journal of Computer Science Languages · Vol. 4, Issue 1, 2026 · pp. 01–08 Read article
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Design and Optimization of Domain-Specific Languages for High-Performance Computing Applications
Abstract: The accelerating demand for computational power in scientific, engineering, and data-intensive domains has driven high-performance computing (HPC) systems toward unprecedented levels of parallelism and architectural complexity. Contemporary HPC platforms integrate multicore CPUs, many-core GPUs, accelerators, and deep memory hierarchies, creating significant challenges for software development and performance optimization. Traditional general-purpose programming languages and parallel programming frameworks provide low-level control over hardware resources but require extensive manual tuning, resulting in poor …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 · pp. 17–22 Read article
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An Overview on VLSI based Hardware Security in IoT Node
Abstract: In the coming era, security will not be a feature we add to an IoT device; it will be a property inherent to its transistor-level design. By encoding security into the VLSI architecture, we move away from the fragile "software-only" paradigm and toward a future where the identity of the device is as immutable as the laws of physics. The rapid proliferation of Internet of Things (IoT) devices has transformed …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 4, Issue 1, 2026 Read article
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A Dynamic Text Compression Model for Big Data Applications Using Hadoop
Abstract: In today’s data-driven era, efficiently handling vast amounts of information has become increasingly important. Data compression plays a vital role in this regard — it is essentially a method of encoding information in such a way that significantly reduces the number of bits required to store or transmit a file. By shrinking data to its most compact form, compression techniques help save storage space, reduce bandwidth consumption, and improve the …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 2, 2026 Read article
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A Constraint-Driven Generative Design Methodology for Modular Actuated Robotic Components in Decentralized Manufacturing
Abstract: This work formalizes a constraint-driven generative design framework for actuator-integrated robotic components intended for decentralized additive manufacturing. Conventional topology optimization typically prioritizes structural efficiency while treating actuator integration, modular interfaces, and fabrication constraints as secondary considerations. In contrast, the proposed methodology encodes these requirements as first-order geometric and mechanical constraints prior to automated material redistribution. The framework defines preserved actuator geometry, bounded design envelopes, representative loading abstractions, and manufacturability-aware domains …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 4, Issue 1, 2026 · pp. 1–12 Read article
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Group-Theoretic Symmetry Indices for Modular Building Layouts under Seismic Load Redistribution
Abstract: Symmetry in modular buildings operates simultaneously as an architectural language, a structural regularizer, and a computational design variable. This paper develops a group-theoretic framework for evaluating and optimizing plan symmetry in modular buildings subjected to seismic load redistribution. The building layout is modeled as a finite occupancy–stiffness field defined on a rectangular lattice, where each module encodes both mass and stiffness contributions. Planar reflections and quarter-turn rotations are represented as …
Published in Emerging Trends in Symmetry · Vol. 2, Issue 1, 2026 · pp. 01–07 Read article
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A Hybrid Multi-Physics Ensemble Deep Learning Framework for Simultaneous Prediction of Thermal and Electrical Conductivity in Functional Polymer-Based Nanocomposites
Abstract: Polymer-based nanocomposites have become promising materials for applications in energy storage, flexible electronics, biomedical devices, aerospace components, and advanced engineering because of their tunable thermal and electrical properties. Reliable prediction of these properties is essential for accelerating material design; however, existing analytical models and conventional machine learning techniques often fail to represent the complex interactions among filler characteristics, polymer matrices, processing conditions, and interfacial transport phenomena. This work presents HMEP-Net, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1062–1082 Read article
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Explainable Sentiment Mining Model in Mental Health Forums for Emotion Classification and Justification
Abstract: Understanding and interpreting emotions expressed in online mental health discussions plays a crucial role in enabling early detection of psychological distress and facilitating timely interventions. As individuals increasingly turn to digital platforms to share personal experiences and seek support, automated systems capable of accurately identifying emotional states can significantly assist clinicians, moderators, and support communities. This paper presents a deep learning–based sentiment mining and emotion classification framework specifically designed to …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 22–32 Read article
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Carbon-Aware Autonomous AI Systems: Reinforcement Learning for Sustainable Cloud and Edge Computing
Abstract: The field of communication and information technology is expanding quickly. Because of this, a significant amount of carbon emissions are produced by cloud data centres and edge computing nodes. In fact they are now responsible for 3 to 4 percent of the worlds total greenhouse gas emissions. Most of the time people who manage these resources focus on how they are working and how quickly they can get things done.. …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 16, Issue 2, 2026 Read article
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Passive Digital Phenotyping for Longitudinal Burnout and Occupational Mental Health Surveillance: A Transformer-Based Explainable Deep Learning Approach Using Smartphone Behavioral Streams
Abstract: Occupational burnout constitutes a pervasive yet chronically under-surveilled public health threat, its insidious temporal evolution rendering episodic self-report instruments structurally inadequate for early detection. This paper introduces BurnoutSense, a passive digital phenotyping framework that continuously harvests eight heterogeneous smartphone behavioral data streams encompassing application usage ecology, communication metadata, geospatial mobility, screen interaction dynamics, inferred sleep rhythmicity, keystroke kinematics, ambient noise exposure, and battery/charging cadence to construct individualized multivariate behavioral signatures …
Published in International Journal of Behavioral Sciences · Vol. 3, Issue 2, 2026 · pp. 44–53 Read article
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Harnessing Bacteria for Next-Generation Data Storage Technologies – A Review
Abstract: The exponential increase in global digital information has created a pressing need for storage technologies that are more durable, compact, and sustainable than conventional electronic media. While hard drives, solid-state drives, and cloud-based systems have transformed information management, they face severe limitations related to storage density, energy consumption, maintenance costs, and long-term preservation. Researchers have, therefore, begun exploring biological systems as alternative information storage platforms. Among these, bacteria have emerged …
Published in International Journal of Molecular Biotechnological Research · Vol. 4, Issue 2, 2026 Read article
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Neuromorphic Spiking Neural Networks and Event-Driven Hardware: Architectures, Learning Paradigms, and Experimental Realizations
Abstract: With the current computational boom the research community is seeking for more sustainable energy efficient i.e. biologically inspired models of conventional Artificial Neural Networks (ANNs). Spiking Neural Networks (SNNs) known as the third generation of neural network models, provide a revolutionary approach by mimicking the asynchronized event-driven and temporally accurate signaling of the mammalian brain. Whereas conventional deep learning models operate with real-valued activations and dense matrix multiplications, SNNs use …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 39–46 Read article
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Neuro-Symbolic Agentic AI for Autonomous Scientific Discovery: Integrating Deep Reinforcement Learning, Quantum Simulation, and XAI-Audited LLM Hypothesis Generation in Drug Target Identification
Abstract: The exponential growth of multi-omics data and the increasing complexity of disease-associated protein interactomes have rendered conventional drug target identification pipelines computationally and epistemologically inadequate. This paper presents the Neuro-Symbolic Agentic AI for Scientific Discovery (NS-AASD) framework, a unified architecture that cohesively integrates deep reinforcement learning (DRL) exploration strategies, variational quantum simulation (VQS) of protein conformational dynamics, and XAI-audited large language model (LLM) hypothesis generation within an autonomous scientific discovery …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 · pp. 15–24 Read article
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Analysis of Low Bit Rate Audio Compression System using Linear Predictive Coding
Abstract: Abstract The point of the task is to build up a framework for encoding great quality discourse at a low bit rate. To execute this, we have utilized the most dominant discourse investigation system called Linear Predictive Coding (LPC). It utilizes tenth request Levinson-Durbin Recursion calculation to achieve the errand. It gives amazingly exact evaluations of discourse parameters and is moderately effective for computation. The discourse flag of guys and …
Published in Current Trends in Signal Processing · Vol. 9, Issue 1, 2019 · pp. 1–6 Read article
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Robust Color Image Watermarking using Biometric Protection and Image Authentication Key
Abstract: In recent days, it becomes difficult to provide protection to color images which were captured by modern capturing devices. This paper presents a novel work on copyright protection by providing authentication process for extraction of watermark of color still images. New blind frequency domain non-correlation based watermarking algorithm using one dimensional Walsh coding technique is introduced to improve the robustness. The developed algorithms do not required the original image for …
Published in Current Trends in Signal Processing · Vol. 5, Issue 2, 2015 · pp. 15–23 Read article
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Design And Implementation Of Audio Steganography On Fpga
Abstract: As the emergence of latest hacking programs and attention of hackers in hacking more and more data, the need arises to make an integrated circuit that can help hiding the message in such a form that no one can hack it easily. The audio Steganographer is a device or an integrated circuit that facilitates the user to hide a massage in the ongoing digital audio stream. It also ensures the …
Published in Journal of Electronic Design Technology · Vol. 1, Issue 1-3, 2011 · pp. 1–6 Read article