Welcome to the forefront of innovation! Delve into the boundless realm of 'Advances in Deep Learning Techniques', where groundbreaking discoveries and transformative breakthroughs shape the future of AI. Explore cutting-edge research, insightful analyses, and real-world applications driving the evolution of deep learning. Join us on a journey of discovery, innovation, and limitless possibilities in the realm of artificial intelligence. Journal Snapshot Journal Name: Advances in Deep Learning Techniques (ADLT) Editor-in-Chief: Dr. Ramesh Dadi (see Editorial Board) Publisher Imprint: The Science Brigade Publishers Publisher: Libertatem Media Private Limited h-index: 15 (By Google Scholar) i10-index: 22 (By Google Scholar) Journal Initials: ADLT Research Scope: Deep Learning, Machine Learning, Neural Networks and Representation Learning, Computer Vision and Image Analytics, Natural Language Processing, Generative Artificial Intelligence, Reinforcement Learning, Explainable and Trustworthy AI, Deep Learning for Healthcare and Scientific Applications, Edge AI and Intelligent Systems Publication Mode: Digital (On this Website) Frequency: Semi-Annual (2 Issues a year) Launch Year: 2021 Review Mode: Double Blind Peer Review Plagiarism Allowed: 10% (as per Turnitin) Coverage: India Language: English Editorial Contact - editor@thesciencebrigade.com
License Terms Ownership and Licensing: Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License. License Permissions: Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers. Additional Distribution Arrangements: Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal. Online Posting: Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers. Responsibility and Liability: Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
Transcriptomic Similarity Mapping and Knowledge Graph Embeddings: Computational Strategies for Systematic Drug Repurposing
Drug repurposing focuses on identifying novel therapeutic indications for existing drugs, currently marketed for other diseases, and has several advantages over traditional de novo drug discovery. Repurposing is both time and cost-efficient, allowing faster drug development by one to two years, with R&D costs reduced by 60% when compared to novel drug development, and can bring drugs for new indications to the market at a reduced cost of $30–40 million.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
Intelligent Product Lifecycle Orchestration: Predictive Analytics and Decision Automation in Retail Category Management
Introduction Retail Product Lifecycle Management (RPLCM) encompasses a collection of plant and corporate processes related to how a company brings a new product to market, decides on discontinuing a product, develops a new generation version of a product, and covers a variety of other product-related issues.
intelligent product lifecycle orchestration
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
Machine Intelligence and Consumer Decision Patterns: A Computational Framework for Retail Behavioural Analytics
This dissertation offers an extensive treatment of customer behavior analytics (CBA), focused more specifically on the retail field and relying on advanced AI techniques, algorithms, and methods. In recent years, artificial intelligence technologies have been shown to be especially useful when large amounts of data need to be interpreted, resulting in a better understanding of customer needs.
Multi-Agent Coordination and Optimisation in Distributed Retail Networks: An AI-Driven Framework for Supply Chain Synchronisation
Retail supply chain is a complex network that includes many circulation centers and hundreds of suppliers. When goods are delivered from the factory to retailing, including the warehouse and the terminal sales outlets, it forms a retail supply chain channel. The key to retailers' competition is dealing with suppliers and other issues in the channel service level.
Multimodal Clinical Data Integration for Post-Discharge Risk Stratification: Ensemble Learning Approaches to Hospital Readmission Prediction
1. Introduction Hospital readmissions are a recurring issue that affects millions of people annually across the globe. When patients are sent home before necessary, are unable to afford prescribed drugs, forget to take them, are uninformed about symptoms that necessitate prompt medical attention, or are unable to contact their medical practitioner, the odds of them returning to the hospital increase.
ensemble learning approaches to hospital readmission prediction
Predictive Actuarial Intelligence: Machine Learning-Based Customer Segmentation and Risk Profiling in General Insurance
This essay is centered around AI-driven customer analytics for competitive advantage within the insurance space. In the global insurance world, the volume and variety of available customer data are exploding. The perception of the importance of customer analytics to competition is high, and superior customer analytics is believed to be among the most critical drivers of competitive advantage.
Predictive Intelligence in Supply Network Dynamics: A Data-Driven Approach to Operational Analytics and Disruption Forecasting
In an effort to streamline logistics and support functions, industries are leaning heavily towards supply chain analytics. The installation of sensor-based systems, RFID tagging, IoT, etc., augments the tracking of products and intensifies available historical data. In this connected era, the application of AI decouples the traditional methodology of managing the supply chain.
data-driven approach to operational analytics
Predictive Scheduling and Throughput Optimisation in Outpatient Clinical Workflows: Machine Learning Models for Ambulatory Care Efficiency
Machine Learning Approaches for Improving Patient Flow and Efficiency in Ambulatory Care: AI Models for Streamlining Scheduling and Reducing Wait Times The value and importance of healthcare services are realized through care delivery; therefore, it is vital to seek innovations for increasing operational efficiency and patient access to care. Artificial intelligence (AI) provides a way to achieve this endeavor.
Reinforcement Learning and Stochastic Optimization in Multi-Asset Portfolio Construction: A Data-Driven Investment Framework
Artificial intelligence (AI) today is critical in portfolio optimization. Since the 1960s, technology has become a significant part of investing. The way investment managers generate outperformance has completely changed. Advancements in big data, machine learning, and AI have allowed managers to uncover innovative ways for the detection of valuable insights that human capability could not do so easily.
Streaming Analytics and Anomaly Detection in Complex Supply Networks: An Event-Driven Framework for Real-Time Operational Intelligence
The management of supply chains is undergoing major changes due to numerous factors, including rapid technological advancements and shifts in business models. The integration between supply chain operational and strategic decisions of a business, as well as supply network design, has been suggested as crucial factors leading to competitive advantage. This essay describes an AI-based system of real-time supply chain state monitoring, analysis, and reconfiguration.
Temporal Graph Networks and Online Learning for Adversarial Claim Detection: A Real-Time Anti-Fraud Architecture in Insurance Operations
Insurance fraud is a significant issue in the United States and around the world. The annual cost of insurance fraud to property and casualty insurers ranges from $15 billion to $30 billion, and the average U.S. family pays nearly $900 more in insurance premiums because of the cost of lawbreaking by others. Automobile insurers alone lose at least $10 billion a year in premiums due to rate evasion, and 20–30% of bodily injury claims are inflated.
Intelligent Process Automation in Retail Banking: A Machine Learning Framework for Operational Efficiency Enhancement
Banks have been at the forefront of adopting modern technologies to ensure higher efficiency, convenience, and ease of use. The ongoing revolution from traditional banking to digital-first practices has put automation and artificial intelligence (AI) at the core of banking operations. Several customer-centric use cases of AI technology have been studied very well.
Intelligent Anomaly Detection in Insurance Claims Processing: A Supervised Learning Approach to Fraud Classification
The insurance industry is inevitably required to compensate for fraud cases, which have led to significant financial losses. Some of the typical examples of fraudulent activities are exaggerating the value of a claim, intentionally damaging the assets, re-filing the claim by hiding some information, or not revealing the existence of other insurance carriers.
supervised learning approach to fraud classification
Graph Neural Networks and Multi-Echelon Optimisation: A Computational Framework for Resilient Supply Chain Network Configuration
The COVID-19 related disruptions of early 2020 uncovered the vulnerability of contemporary supply chains. It became clear that organizations often do not maintain sufficient flexibility and contingency to deal with potential disruptions effectively.
resilient supply chain network configuration
Generative Molecular Modelling for Orphan Disease Therapeutics: Deep Learning Approaches to Target-Specific Drug Discovery
Rare diseases are diseases that affect a small number of people when compared to the general population. In the United States, a rare disease is defined as a disease or condition that affects fewer than 200,000 individuals. Seven thousand rare diseases are known and are estimated to affect 25 to 30 million Americans. Of those seven thousand rare diseases, only approximately 5 percent have treatments.
deep learning approaches to target-specific drug discovery
Demand Sensing and Replenishment Intelligence: Neural Network-Driven Inventory Velocity Optimization in Omnichannel Retail
Optimizing inventory turnover is a critical operational hurdle for a retailer looking to increase competitive advantage. As e-commerce continues to reshape consumer shopping behavior and competition becomes increasingly global, the need for leveraging technology to keep up with consumers' increasing demands rises as well. The role that a retailer's merchandise plays in its competitive strategy is determined largely by the return on assets metric of the firm.
neural network-driven inventory velocity optimization
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
Deep Learning-Augmented Preoperative Planning: Computational Approaches to Surgical Precision and Perioperative Outcome Optimization
Artificial intelligence (AI) can play a pivotal role in enhancing the surgical workflow, from planning to execution. The major benefits of involving AI in these steps include an increase in the precision and accuracy of surgical tasks and the potential to provide personalized surgical solutions. AI technology can also offer assistance during surgical procedures and supply real-time, relevant information about the patient's tissue status, the margin of resection, and more.
deep learning-augmented preoperative planning
computational approaches to surgical precision
Adaptive Pricing Mechanisms in Consumer Banking: Reinforcement Learning Models for Real-Time Financial Product Valuation
Dynamic pricing is a strategy that individualizes prices or offers to different customers based on their responsiveness and the prices offered and/or the product features. Based on the response to the first price offered, the firm may also choose not to offer a second price. There are three main types of dynamic pricing strategies. The first is to determine a sequence of prices in advance or to commit to a sequence of prices using information only on the first price realized.
Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/
Cognitive Automation and Predictive Personalisation: Transforming Policyholder Interactions Through Intelligent Systems in Insurance
The global insurance industry is a highly competitive space. As such, many large insurers are choosing to adopt a more consumer-driven focus. This strategy focuses on incorporating various consumer preferences and needs. Among the trends in the industry, these preferences include simplicity, better accessibility, on-demand products, and superior services. Despite the move toward more consumer-focused strategies, few companies have successfully implemented technology to assist in such efforts.
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/
Collaborative Filtering and Contextual Bandits in Retail Finance: A Hybrid Recommender System for Personalised Financial Product Matching
Financial services are striving to adapt to rapidly changing consumer behavior and financial landscapes. For decades, financial services have been exclusively used by intermediaries, and customers have had to rely on advisors' judgment. With digitalization and advances in finance, services are becoming increasingly available to self-directed individual investors.
Computational Toxicology Through Neural Network Architectures: Predictive Modelling of Pharmacological Adverse Effects
Identifying the potential toxicological effects of a new drug has assumed a key role in pharmaceutical development trials. New tools based on artificial intelligence that can more effectively and accurately identify such effects are highly desirable. A first step in this direction is represented by machine learning-based approaches that predict if and to what degree a given compound may increase the risk for side effects.
Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/
Condition-Based Asset Monitoring in Logistics Infrastructure: Deep Learning Models for Predictive Failure Analysis in Retail Supply Networks
In today's dynamic markets, retail supply chain organizations face multiple challenges to optimize their operations and processes. These challenges may account for up to a third of the costs of final products in markets where margins are particularly tight, reducing the ability to lower customer prices or invest in other initiatives to remain competitive.
Conversational Intelligence and Behavioural Nudging: Machine Learning Architectures for Personalised Insurance Client Engagement
Customer engagement is one of the key performance indicators in several industries, including insurance. Engaged customers are individuals who experience strong, frequent, and diverse interactions with a brand and, consequently, exhibit positive sales patterns, retention, satisfaction, and loyalty. It has been suggested that improving customer engagement could decrease the level of insurable asset underinsurance, increasing worldwide protection consumption.
personalised insurance client engagement
Deep Learning Architectures for Phenotypic Bioactivity Profiling in High-Throughput Chemical Screening Assays
As high-throughput screening (HTS) has always been one of the key technologies in drug discovery, it can identify a potential lead compound from millions of compounds rapidly and has attracted much attention in the pharmaceutical industry. In recent decades, high-throughput screening has grown profoundly, accompanied by the development of automation technology.
high-throughput chemical screening assays
Adaptive Health Communication Through Intelligent Systems: Natural Language Processing Approaches to Personalised Patient Education and Literacy Enhancement
The importance of effective patient education has been increasingly realized as a potent vehicle to improve the overall quality and safety of patient care. Today’s patient requires accurate, comprehensive, and reliable health-related information before undertaking any major procedure in a healthcare setting. Barriers in learning can considerably impact a patient’s ability to understand disease processes and treatment options.
natural language processing approaches to personalised patient education
Accelerating Drug Discovery Throughput with Streaming Inference Pipelines: Real-Time Computational Analytics in Pharmaceutical R&D
The generation of high volume and diverse data in pharmaceutical research has led to the application of sophisticated analytics in order to enhance the productivity of the pharmaceutical research organization. The major focus of these applications has been limited to the development of algorithms to solve and devise new alternative solutions to provide greater productivity.
Artificial Intelligence for Scalable Cloud Systems: Innovations in Resource Optimization
Resource optimization for intelligent and scalable solution for exponential growth of cloud computing demands which ensures performance efficiency, cost-effectiveness, and reliability. The objective of this paper is to explore the integration of artificial intelligence (AI) techniques which includes machine learning, deep reinforcement learning, and predictive analytics in cloud infrastructure management for dynamic resource provisioning, workload prediction, and anomaly detection.
Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Learning for Network Traffic Analysis: Detecting Security Breaches in Real-Time
The exponential growth of network traffic presents significant challenges in real-time security breach detection. Traditional intrusion detection systems (IDS) struggle to efficiently analyze vast data streams, leading to delays and undetected anomalies. This paper explores the application of deep learning models for network traffic analysis, leveraging their ability to autonomously detect anomalous patterns and potential security threats. We investigate various architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models, evaluating their effectiveness in identifying malicious activities. Feature engineering techniques, dataset preprocessing, and model optimization strategies are discussed to enhance real-time detection capabilities. Furthermore, we analyze computational overhead, detection accuracy, and false positive rates, highlighting trade-offs in deploying deep learning-based IDS in large-scale networks. Case studies demonstrate the superiority of AI-driven approaches over conventional methods, underscoring their potential for proactive cybersecurity defense.
Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
Distributed Systems for Financial Reconciliation and Clearing: Enhancing Real-Time Performance and Resilience
Accounting and clearing have long plagued the banking industry. These duties ensure accurate, transparent, and timely financial transactions. Batch processing and centralized architecture hinder real-time transaction handling, scalability, and robustness within traditional approaches. With distributed systems, the financial industry may improve its working abilities and solve key difficulties.
Machine Learning-Driven Anomaly Detection and Proactive Insights for Cloud Telemetry and Monitoring
Machine learning-driven anomaly detection has emerged as a transformative approach for enhancing cloud telemetry and monitoring systems. Cloud environments are characterized by massive amounts of dynamic, real-time telemetry data generated by a plethora of services, applications, and infrastructure components. As cloud computing continues to evolve, the need to proactively identify anomalies, predict resource utilization trends, and automate incident resolution becomes increasingly critical. Traditional monitoring systems often rely on rule-based approaches or simplistic threshold settings, which are limited in their ability to detect novel or complex patterns that deviate from expected behavior. Machine learning (ML) offers a more sophisticated and scalable solution to this challenge, enabling the automation of anomaly detection and providing proactive insights for effective cloud management. This research paper explores the application of ML algorithms in the context of cloud telemetry, focusing on their role in anomaly detection, trend prediction, and incident resolution. Machine learning provides significant advantages over traditional approaches by leveraging data-driven models that continuously adapt to changing cloud environments. By analyzing large datasets from cloud platforms, ML algorithms can detect outliers, unusual patterns, and performance degradations with high accuracy. These capabilities empower organizations to detect potential issues before they impact users, reducing downtime and improving system reliability. Anomaly detection in cloud telemetry involves identifying deviations from normal operational behavior, which can indicate a range of issues such as performance bottlenecks, security breaches, or system failures. Machine learning models, such as supervised learning, unsupervised learning, and reinforcement learning, are employed to recognize these anomalies through training on historical telemetry data. Supervised learning techniques, including classification and regression, require labeled data and are effective in identifying known patterns of anomalies. In contrast, unsupervised learning techniques, such as clustering and autoencoders, do not require labeled data and are suitable for detecting novel, unknown anomalies that may arise in complex, distributed systems. Reinforcement learning, on the other hand, offers the potential for real-time anomaly detection and adaptive decision-making by continuously interacting with the cloud environment and optimizing system performance. Beyond anomaly detection, machine learning can also be used to predict resource utilization trends, a key aspect of cloud monitoring. Cloud environments are highly dynamic, with resources being provisioned and de-provisioned based on demand. Predicting resource consumption, such as CPU usage, memory, and network bandwidth, allows organizations to optimize resource allocation and reduce operational costs. Time-series forecasting models, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, are commonly used for this purpose. These models are capable of capturing temporal dependencies and forecasting future resource demands based on historical telemetry data. Accurate resource prediction facilitates better scaling decisions, ensuring that cloud services can handle peak loads without over-provisioning or under-provisioning resources. Automation of incident resolution is another area where machine learning can have a profound impact. By integrating anomaly detection and resource utilization prediction with automated response systems, cloud platforms can resolve incidents in real time without human intervention. For example, when an anomaly is detected, a machine learning system can trigger predefined remediation actions such as scaling resources, rerouting traffic, or restarting services. Reinforcement learning can play a critical role in this area, as it allows the system to continuously improve its decision-making process by learning from past actions and their outcomes. Automation not only accelerates incident response but also reduces the burden on operations teams, enabling them to focus on more strategic tasks. The integration of machine learning into cloud telemetry and monitoring systems is not without challenges. One of the primary concerns is the quality of the data used to train machine learning models. Inaccurate or incomplete data can lead to poor model performance and unreliable anomaly detection. Additionally, the complexity and high-dimensionality of cloud telemetry data pose challenges for feature selection and model training. The interpretability of machine learning models is another important consideration, particularly in production environments where transparency and explainability are critical for troubleshooting and decision-making. Recent advances in explainable AI (XAI) are helping to address these challenges by providing more transparent and interpretable machine learning models, but further research is needed to improve their usability in cloud monitoring systems. Another challenge is the scalability of machine learning models in large-scale cloud environments. Cloud platforms generate vast amounts of telemetry data, and real-time analysis requires high computational resources. Distributed machine learning frameworks, such as Apache Spark and TensorFlow, are commonly used to address scalability issues by parallelizing model training and inference across multiple nodes. However, ensuring the efficient use of resources while maintaining high performance remains a significant area of research. The deployment of machine learning-driven anomaly detection and proactive insights for cloud monitoring can yield substantial benefits for organizations, including reduced operational costs, improved system reliability, and enhanced user experience. However, the full potential of these systems can only be realized through continuous advancements in machine learning techniques, data management practices, and integration strategies. Future research will likely focus on improving the accuracy, scalability, and interpretability of machine learning models, as well as exploring novel approaches to anomaly detection and automated incident resolution. By addressing these challenges, organizations will be better equipped to manage the increasing complexity and scale of modern cloud environments, ensuring more efficient and resilient cloud-based services.
Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0