Journal of Research in Artificial Neural Network Systems
This is blind peer-reviewed journal that provides a platform which aspires to cover all the aspects of Artificial Neural Network Systems. This journal provides a deep insight in advancement and research in the field of Artificial Neural Network Systems. Focus and Scope Natural Language Processing (NLP) Convolutional Neural Network (CNN) Reinforcement Learning Image Recognition Software defined & ultra-wide band radio Bioinformatics Speech Recognition Medical Diagnosis
A Hybrid Deep Learning Framework for Multimodal Biometric Verification Utilizing ECG, Fingerprint, and Iris Data
Single-trait biometric systems frequently face criti- cal security flaws, ranging from physical sensor wear to sophisti- cated spoofing attacks. When deploying modern deep learning to solve these issues, developers encounter a new problem: data starvation. Standard classification models require thou- sands of images to generalize, leading to massive overfitting when only a few enrollment scans are available. To overcome this limitation, we engineered a robust multimodal biometric architecture tailored specifically for data-scarce constraints (a maximum of three training samples per user). Our solution fuses a dynamic, one-dimensional behavioral signal (Electrocar- diogram) with static, two-dimensional physical traits (Finger- print and Iris) across a dataset of 199 unique individuals. To bypass the failure of standard Convolutional Neural Networks (CNNs) on sparse physical data, we shifted the network objective from 1-to-N identification to 1-to-1 verification using Siamese architectures. We processed the physical traits through parallel Siamese ResNet50V2 networks optimized with contrastive loss to generate 512-dimensional feature embeddings. Simultaneously, a custom-built 1D-CNN evaluated the temporal ECG data. We then normalized and merged the outputs from these varying modalities using a targeted, weighted score-level fusion algorithm. Testing showed that while traditional 2D Softmax classifiers stalled below 40% accuracy, our Siamese metric learning approach drastically elevated unimodal performance. The combined multimodal sys- tem (anchored by a 50% ECG, 30% Iris, and 20% Fingerprint weight distribution) achieved a final accuracy of 92.21%. Further- more, the system recorded a False Acceptance Rate (FAR) of just 1.01% alongside a 14.57% False Rejection Rate (FRR), proving that our hybrid fusion methodology mathematically neutralizes the weaknesses of individual biometrics in highly constrained environments.
AI Based Legal Assistant with RAG & LangChain
The rapid growth of legal information and documentation has made it increasingly difficult for individuals and organizations to access accurate and relevant legal insights efficiently. This project presents the development of an AI-Based Legal Assistant using Retrieval-Augmented Generation (RAG) and LangChain, designed to provide intelligent, context-aware responses to legal queries. The system integrates large language models with a document retrieval mechanism to ensure that answers are generated based on reliable legal sources rather than generic knowledge. The architecture utilizes LangChain to orchestrate the workflow between user queries, vector databases, and language models. Legal documents are ingested, processed, and stored as embeddings in a vector database, enabling efficient semantic search. When a user submits a query, the system retrieves relevant legal information and augments the response generation process, ensuring accuracy, relevance, and explainability.
Anomaly Detection in DevOps Pipelines using Deep Learning
Modern DevOps pipelines emit a torrent of telemetry — CI/CD events, build and deploy logs, metrics, and traces — far beyond what humans or static thresholds can monitor. Anomalies in this stream (failing builds, latency spikes, error surges, configuration drift, resource exhaustion) are early signals of incidents, yet they are rare, varied, and buried in noise. This article surveys and organizes deep-learning approaches to detecting them. We frame anomaly detection as a largely unsupervised problem over high-volume, partly unstructured data, and present a four-layer detection stack: ingestion and log parsing, deep detection models, scoring and thresholding, and triage and action. We cover the three dominant model families — forecasting models (LSTM/GRU next-event prediction, exemplified by DeepLog), reconstruction models (autoencoders flagging high reconstruction error), and sequence/transformer models — and formalize their detection criteria and the precision/recall/F1 metrics used to evaluate them. Crucially, we highlight a widely documented evaluation pitfall: under a realistic chronological train/test split, reported F1 scores can collapse (for example from roughly 0.93 to below 0.5 on the BGL benchmark), a caution against over-optimistic random-split results. Public benchmark datasets (HDFS, BGL) and methods are cited throughout.
Sushma Sunkollu Nagaraj Sunkollu Nagaraj
Solution Architecture Patterns for Scalable Generative AI Workloads
Generative AI workloads place unusual demands on system architecture: inference is stateful, memory-bandwidth-bound, and bursty, and a single large model can saturate expensive accelerators while still leaving them underutilized. This article catalogs the solution-architecture patterns that have converged in production large-language-model (LLM) systems and organizes them into a layered reference architecture spanning the model gateway, orchestration, knowledge and caching, and the inference-serving cluster. We treat the core scaling primitives — continuous (in-flight) batching, PagedAttention-style KV-cache management, prefix and prompt caching, tensor parallelism, quantization, and speculative decoding — as composable patterns, and give the quantitative relationships that govern their cost and performance, including KV-cache memory growth, the throughput–latency trade-off, and an arithmetic-intensity (roofline) view of the prefill versus decode phases. We also cover application-level patterns (retrieval-augmented generation, agent orchestration, the model gateway, and semantic caching) and operational concerns (autoscaling, multi-tenancy, and reliability). The aim is a practitioner's map: which pattern solves which bottleneck, what it costs, and how the patterns combine. Public technical sources are cited throughout.
TerraHawk – AI Drone-Based Precision Agriculture System
This work presents a farmer-oriented assistant that combines real-time crop detection with knowledge-based agronomic guidance. The system uses a YOLOv5-based computer vision module to detect crops and visually annotate images and videos with bounding boxes and confidence scores. It also features a bilingual chatbot (English/Kannada) that answers specific questions using a curated agricultural knowledge base. The backend is built with Fast API and offers unified REST endpoints for detection, chat, and history. Meanwhile, a React and Vite frontend delivers an interactive user interface for uploading media, viewing annotated results, and chatting with the assistant. From a design perspective, the system features a modular architecture. The detection model is wrapped in a lightweight layer and works with a multi-stage retrieval pipeline. This pipeline uses exact matching, crop-intent keywords, fuzzy token overlap, and semantic embeddings with FAISS. This design ensures strong, low-latency performance on standard hardware. It also lowers the chance of incorrect answers by using template-based response generation and explicit logging. The paper details the overall architecture, reviews related work in crop detection and agricultural decision support, and compares the proposed crop detection subsystem to traditional and modern machine learning methods. The discussion covers strengths, limitations, and design trade-offs, especially regarding deployment challenges, extensibility, and suitability for smallholder farming contexts.
Reinforcement Learning for Auto-Scaling and Cost Optimization in Multi-Cloud
Auto-scaling decides how much capacity to provision for a workload and — in a multi-cloud setting — where to place it across providers, regions, and pricing models. Traditional threshold and forecast-based autoscalers react with fixed rules and tend to over-provision, wasting money, or under-provision, breaching service-level agreements (SLAs). This article presents reinforcement learning (RL) as a principled alternative: an agent that learns a cost-aware, SLA-constrained scaling and placement policy directly from interaction with the environment. We formalize multi-cloud auto-scaling as a Markov decision process, design a composite reward that trades resource cost against SLA penalties, utilization, and scaling churn, and extend the action space to multi-cloud placement — choosing provider, region, and on-demand versus spot capacity to exploit cross-cloud price differences and spot discounts while respecting interruption risk and data-residency constraints. We review the dominant algorithms (DQN, PPO, DDPG), discuss safe exploration via bootstrapping from a conventional autoscaler, and give illustrative results consistent with the published literature, where RL autoscalers report on the order of 20% cost savings and improved tail latency relative to threshold-based baselines. Public technical sources are cited throughout.
Adaptive Cloud Resource Allocation using Deep Reinforcement Learning
Allocating cloud resources — deciding which physical machine should host each arriving task, virtual machine, or container — is a combinatorial, online, multi-resource problem that classical bin-packing heuristics (first-fit, best-fit) solve only crudely: they ignore workload structure, over- or under-consolidate, and leave hosts fragmented. This paper formulates adaptive resource allocation as a Markov decision process and learns the allocation policy with deep reinforcement learning (DRL). The scheduler observes the heterogeneous CPU/memory/GPU utilization of a machine pool and the resource profile of the next task, and chooses a placement that maximizes a composite reward trading utilization against energy, SLA violations, and resource fragmentation. We give the MDP formulation, the multi-resource utilization and reward definitions, and the value objective; survey the algorithm landscape (DQN and its Rainbow extensions, A3C, and multi-agent variants); and discuss GPU fragmentation and consolidation as special cases. Illustrative results, consistent with the published literature, show that a learned policy consolidates onto fewer active hosts, raising utilization and cutting energy while reducing SLA violations relative to heuristic baselines. This paper is distinct from horizontal auto-scaling: it concerns where to place work on a fixed pool, not how many replicas to run. Public sources are cited throughout.