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Advancements in Deep Learning Techniques for Potato Leaf Disease Identification Using SAM-CNNet Classification
Potato leaf diseases like Late Blight and Early Blight significantly challenge potato cultivation, impacting crop yield and quality worldwide. Potatoes are a staple for over a billion people and crucial for food security, especially in developing countries. The economic impact is substantial, with Late Blight alone causing annual damages over $6 billion globally. Effective detection and management are essential to mitigate these effects on agricultural productivity and economic stability. This paper presents a novel approach to potato leaf disease detection using advanced deep learning and optimization techniques. Key components include data normalization to eliminate noise, feature extraction using GoogLeNet, and hyperparameter tuning through the Elk Herd Optimizer (EHO). Additionally, a Spatial Attention Mechanism and Convolutional Neural Network (SAM-CNNet) are employed for robust classification. The method is validated using the Plant Village dataset, yielding an accuracy of 98.58%, with precision of 97.68%, recall of 98.42%, and F1-Score of 98.21%, demonstrating exceptional performance and reliability. This study highlights the proposed approach's efficacy in accurately identifying and classifying potato leaf diseases, offering a promising solution for precision agriculture and crop management. Copyright: 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license. -
Advancements in e-Governance Initiatives: Digitalizing Healthcare in India
In order to improve the quality of service delivery to the public, to encourage interactive communications between government and citizens or government and business, and to address development challenges in any given society, information and electronic governance is the sophisticated fusion of a wide range of information and communication technologies with non-technological measures and resources. Digital technology advancements over the past ten years have made it possible to quickly advance data gathering, analysis, display, and application for bettering health outcomes. Digital health is the study and practice of all facets of using digital technologies to improve ones health, from conception through implementation. Digital health strategies seek to improve the data that is already accessible and encourage its usage in decision-making. Digital patient records that are updated in real-time are known as electronic health records (EHRs). An electronic health record (EHR) is a detailed account of someones general health. Electronic health records (EHRs) make it easier to make better healthcare decisions, track a patients clinical development, and deliver evidence-based care. This concept paper is based on secondary data that was collected from a variety of national and international periodicals, official records, and public and private websites. This paper presents a review of advancements for scaling digital health within Indias overall preparedness for pandemics and the use of contact tracing applications in measuring response efforts to counter the impact of the pandemic. The paper provides information about the government of Indias EHR implementation and initiatives taken toward the establishment of a system of e-governance. The document also covers the advantages of keeping EHR for improved outreach and health care. Further, this paper discusses in depth the effectiveness of using contact tracing applications in enhancing digital health. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Advancements in Electronic Healthcare: A Bibliometric Analysis
Electronic healthcare has changed the traditional form of medical treatment. The integrated approach of interconnected devices had enhanced the process of record keeping and dissemination, benefitting Doctors, patients, and other stakeholders. This study aims to highlight the research carried out in the field of electronic healthcare from the year 2011 to 2020. Metadata of 821 publications from Scopus database was extracted and analyzed. VOS viewer was used to generate the network diagrams and link strengths. It was found that Harvard Medical School and European Commission were the top publication affiliation and funder, respectively. United Stated dominated with the maximum number of publications till 2017 but was surpassed by publications from India from 2018 onwards. Publications inclined toward Internet of things, network security, retrospective study, and authentication toward the end of this decade indicating the shift in trend for the future. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Advancements in Medical Imaging: Detecting Kidney Stones in CT Scans using a ELM-I AdaBoost-RT Model
Kidney stones have been more common in recent years, leading many to believe that the condition is common. The condition's strong relationship with other terrible diseases makes it a major threat to public health. The development of instruments and procedures that facilitate the diagnosis and treatment of this ailment has the potential to enhance the effectiveness and efficiency of health care. Preprocessing, feature extraction, level set segmentation, and model training are the four steps that make up this approach. Part of the preprocessing includes eliminating the skeletal skeleton and soft-organs. Level set segmentation is commonly used for object tracking, motion segmentation, and image segmentation. An extremely effective feature extraction method called Gray level co-occurrence matrix (GLCM) is suggested for extracting the necessary characteristics from the segmented image. That ELM-I-AdaBoost-RT was used all during training. This cutting-edge technique achieves an average accuracy of 95.83%, surpassing both ELM and AdaBoost. 2024 IEEE. -
Advancements in optical steganography for secure medical data transmission in telehealth systems
Secure medical data transfer technologies have advanced as a result of the brisk growth of telehealth services. This study provides a thorough review of the most up-to-date research on using optical steganography to conceal medical records from prying eyes. Data concealing capacity has been increased without sacrificing picture quality using new techniques that make it difficult for unauthorised parties to access hidden information. Using adaptive steganography methods, medical data may be encoded in images in a way that makes it impossible to detect or extract by prying eyes. By concealing information over many picture layers, multi-layer steganography adds an extra degree of protection from prying eyes. The development of steganographic techniques has been spurred on by the use of machine learning and artificial intelligence to enhance steganalysis and the use of quantum characteristics to offer an extra layer of security in quantum steganography. Combining this with cryptographic safeguards like encryption provides an additional layer of security. In order to successfully safeguard sensitive medical data during transmission, standardisation and compliance in optical steganography are becoming more important as telehealth systems become more widespread. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Advancements in Solar-Powered UAV Design Leveraging Machine Learning: A Comprehensive Review
Unmanned Aerial Vehicles (UAVs), commonly known as drones, have seen significant innovations in recent years. Among these innovations, the integration of solar power and machine learning has opened up new horizons for enhancing UAV capabilities. This review article provides a comprehensive overview of the state-of-the-art in solarpowered UAV design and its synergy with machine learning techniques. We delve into the various aspects of solar-powered UAVs, from their design principles and energy harvesting technologies to their applications across different domains, all while emphasizing the pivotal role that machine learning plays in optimizing their performance and expanding their functionality. By examining recent advancements and challenges, this review aims to shed light on the future prospects of this transformative technology. The Authors, published by EDP Sciences, 2024. -
Advancements in Sustainable Techniques for Dried Meat Production: an Updated Review
Dried meat is one of the ethnic and aesthetic food products popular among global civilizations and communities. The background of the production is associated with several methods practiced conventionally in the olden days. This review focused on investigating the advantages, challenges, research gaps, and technological intervention in dried meat production in the modern era. Moreover, it presented a gestalt of cutting-edge thermal and non-thermal food processing technologies and their effectiveness in extending shelf life. It delved into the specific characteristics of dried meat, including biochemical, sensory, and microbiological properties and processing techniques, and addressed the contamination sources. The pros and cons of various drying methods like hot-air drying, vacuum pulsed electric field, microwave-assisted techniques, and non-thermal drying processes are comprehended. The impact on meat's structural properties, nutritional value, shelf-life, quality control, and food safety are thoroughly presented. Moreover, the review explored the biochemical dynamics of the drying process and underscored the health risks associated with mycotoxin contamination in dried meat products. Furthermore, the study also presented the avenues of AI-based platforms and non-destructive technology for validating the quality of dried meat products. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks
Wireless Sensor Networks (WSNs) are used in various healthcare and military surveillance applications. As more sensitive data is transmitted across the network, achieving security becomes critical. Ensuring security is also challenging because most sensors are deployed in remote areas, making them vulnerable to many security attacks. Sybil attacks are one of the most destructive attacks. Security against Sybil attackers can be attained by implementing effective detection techniques to distinguish attackers from genuine nodes. This paper reviews existing machine learning-based approaches for detecting Sybil attacks, and their performance is compared based on different parameters. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Advancements in thiol-yne click chemistry: Recent trends and applications in polymer synthesis and functionalization
The 2022 Nobel Prize in chemistry brought the world's attention to click chemistry, a field as fascinating as its name, characterized by attractive features like high yield, stereospecificity, and broad scope. Since its inception, click chemistry has been synonymous with copper-catalyzed azide-alkyne reactions, owing to their remarkable advantages. However, as the field advanced, there has been a proactive search to develop metal-free alternatives for more biocompatible applications. This led to the extensive adoption of thiol-ene click reactions in the past decade. Yet, due to the growing requirement for polymers with complex architecture and functionality, in recent years, thiol-yne click reactions have come to the forefront with additional advantages over its ene counterparts, allowing the addition of two thiols to an alkyne. This review provides a concise overview of some of the significant developments in polymer synthesis and functionalization utilizing thiol-yne click chemistry. The focus is primarily on radical-mediated thiol-yne reactions, considering the substantial body of work in this area. Moreover, the review provides adequate discussion on other mechanistic pathways like nucleophilic and metal-catalyzed thiol-yne reactions that have gained traction recently. 2024 Elsevier Ltd -
Advances in Carbon-Element Bond Construction under Chan-Lam Cross-Coupling Conditions: A Second Decade
Copper-mediated carbon-heteroatom bond-forming reactions involving a wide range of substrates have been in the spotlight for many organic chemists. This review highlights developments between 2010 and 2019 in both stoichiometric and catalytic copper-mediated reactions, and also examples of nickel-mediated reactions, under modified Chan-Lam cross-coupling conditions using various nucleophiles; examples include chemo- and regioselective N-arylations or O-arylations. The utilization of various nucleophiles as coupling partners together with reaction optimization (including the choice of copper source, ligands, base, and other additives), limitations, scope, and mechanisms are examined; these have benefitted the development of efficient and milder methods. The synthesis of medicinally valuable or pharmaceutically important nitrogen heterocycles, including isotope-labeled compounds, is also included. Chan-Lam coupling reaction can now form twelve different C-element bonds, making it one of the most diverse and mild reactions known in organic chemistry. 1 Introduction 2 Construction of C-N and C-O Bonds 2.1 C-N Bond Formation 2.1.1 Original Discovery via Stoichiometric Copper-Mediated C-N Bond Formation 2.1.2 Copper-Catalyzed C-N Bond Formation 2.1.3 Coupling with Azides, Sulfoximines, and Sulfonediimines as Nitrogen Nucleophiles 2.1.4 Coupling with N, N -Dialkylhydroxylamines 2.1.5 Enolate Coupling with sp 3-Carbon Nucleophiles 2.1.6 Nickel-Catalyzed Chan-Lam Coupling 2.1.7 Coupling with Amino Acids 2.1.8 Coupling with Alkylboron Reagents 2.1.9 Coupling with Electron-Deficient Heteroarylamines 2.1.10 Selective C-N Bond Formation for the Synthesis of Heterocycle-Containing Compounds 2.1.11 Using Sulfonato-imino Copper(II) Complexes 2.2 C-O Bond Formation 2.2.1 Coupling with (Hetero)arylboron Reagents 2.2.2 Coupling with Alkyl- and Alkenylboron Reagents 3 C-Element (Element = S, P, C, F, Cl, Br, I, Se, Te, At) Bond Forma tion under Modified Chan-Lam Conditions 4 Conclusions. 2021 Georg Thieme Verlag. All rights reserved. -
Advances in Crime Identification: A Machine Learning Perspective
Crime profoundly impacts individuals, communities, and families. Technological advancements have provided perpetrators with new opportunities for criminal activities. The primary objective of the police department is to resolve crimes, ensuring justice for the victims. Additionally, preventing such incidents is crucial for creating a safer world. The landscape of criminal justice has undergone a significant shift with the integration of machine learning techniques, unlocking unparalleled potential for accuracy and efficiency. This study thoroughly examines the concept of various applications of machine learning in crime detection, prediction, and prevention. We examine the evolution of these technologies, from early developments to state- of-the-art methodologies, conducting a thorough analysis of their strengths, limitations, and ethical considerations. Moreover, the paper sheds light on crimes discussed in academic circles, serving as a repository for scholars and researchers. This facilitates informed discussions and guides future research endeavours. 2024 IEEE. -
Advances in detecting non-steroidal anti-inflammatory drugs (NSAIDs) using molecular receptors and nanostructured assemblies
The detection and quantification of non-steroidal anti-inflammatory drugs (NSAIDs) are crucial due to their widespread use and potential impact on human health and the environment. This review provides a comprehensive survey of the recent advancements in sensing technologies for NSAIDs, focusing on molecular receptors and nanostructured assemblies. Molecular receptors based on different fluorescent molecules such as anthracene, naphthalimide, squaraine, quinoline, BINOL, etc. offer high selectivity and sensitivity for NSAID detection. In parallel, nanostructured assemblies including CdSe/ZnS, Cd/S quantum dots (QDs), carbon dot-containing imprinted polymers, Ag and Au nanoparticles (NPs), hydrogel-embedded chemosensors, etc. were utilized for NSAID detection. This review highlights the different binding pathways with the change of various photophysical properties combining molecular recognition elements with nanomaterials to develop innovative sensors that achieve rapid, sensitive, and selective detection of NSAIDs. The review also discusses current challenges and future prospects in the field and based on reported designed receptors and nanostructured assemblies. To the best of our knowledge, no reviews have been reported on this topic so far. Thus, this review will fruitfully guide researchers to design various new molecular receptors and nanostructured materials to detect NSAIDs. 2024 RSC. -
Advances in sensor technologies for detecting soil pollution
The present chapter elucidates progressions in the surveillance of soil pollution, with a specific emphasis on integrated systems and sensor technologies. Future trends (e.g., enhanced selectivity, regulatory adoption), deployment platforms (field-deployable, wireless networks), and sensor types (electrochemical, optical, and biosensors) are discussed. Increasing sensitivity and specificity, facilitating on-site, real-time analysis, and integrating sensing with remediation strategies are priorities. The discourse highlights the revolutionary capacity that soil pollution sensors possess to propel environmental monitoring and management forward. Collaboration among stakeholders is critical for successfully implementing sensorbased approaches and driving innovation. 2024, IGI Global. All rights reserved. -
Advances in suicide prevention: Critical overview of the gaps in suicide risk assessments, multimodal strategies, medicolegal risks, and the emerging evidence
The CDC reports that the United States has the highest suicide rates in over 80 years. Numerous public policies aimed at reducing the rising suicide rates, such as Aetna's partnership with the American Foundation for Suicide Prevention (AFSP) and the zero-suicide initiative, continue to challenge these attempts. It, therefore, remains imperative to explore the shortcomings of these efforts that hamper their efficiency in reducing suicide rates. Advancements in research over time have sparked scientific skepticism, encouraging re-evaluation of established concepts. The current paper tests prevalent assumptions and arguments to uncover a scientifically informed approach to addressing rising suicide rates in clinical settings. The Author(s), 2024. -
Advances in text steganography theory and research: A critical review and gaps
There is an immense advancement in science and technology, and computing systems with the highest degree of security are the present hot topic; however, the domination of hackers and espionage in terms of disclosing the sensitive information are steadily increasing. This chapter presents a theoretical view and critical examination of the few text steganography methods in the contemporary world. It tells the direction in which research has developed over the past few years. Cryptography, the encipherment to a certain extent, protects the data by making it unreadable but not safe. Improvisation of the same can be done using another layer of protection that is steganography in which the secret embedded inside the cover text will not be revealed. 2021, IGI Global. -
Advances in the use of ceramic catalysts in fine chemical synthesis
Ceramics are versatile materials that have been put to many different uses. Catalysis is one such area where they have been used, both as catalyst and as a robust support material for catalysts. Properties like porosity and thermal and mechanical stability make ceramics attractive in these applications. Oxidation, esterification, hydrogenation, reduction, condensation reaction, and FriedelCrafts reaction are important reactions, which have uses spanning a wide range of applications, most notably in energy and environment. This chapter gives the recent advancements in ceramic materials used in the synthetic applications of the abovementioned reactions. The type and class of the ceramic material used and its role have been mentioned for these reactions. 2023 Elsevier Ltd. All rights reserved. -
Advancing Brain Tumor Segmentation in MRI Scans: Hybrid Attention-Residual UNET with Transformer Blocks
Accurate segmentation of brain tumors is vital for effective treatment planning, disease diagnosis, and monitoring treatment outcomes. Post-surgical monitoring, particularly for recurring tumors, relies on MRI scans, presenting challenges in segmenting small residual tumors due to surgical artifacts. This emphasizes the need for a robust model with superior feature extraction capabilities for precise segmentation in both pre-and post-operative scenarios. The study introduces the Hybrid Attention-Residual UNET with Transformer Blocks (HART-UNet), enhancing the U-Net architecture with a spatial self-attention module, deep residual connections, and RESNET50 weights. Trained on BRATS20 and validated on Kaggle LGG and BTC_ postop datasets, HART-UNet outperforms established models (UNET, Attention UNET, UNET++, and RESNET 50), achieving Dice Coefficients of 0.96, 0.97, and 0.88, respectively. These results underscore the models superior segmentation performance, marking a significant advancement in brain tumor analysis across pre-and post-operative MRI scans. 2024 by the authors of this article. -
Advancing Collaborative AI Learning Through the Convergence of Blockchain Technology and Federated Learning
Artificial intelligence (AI) has revolutionized multiple sectors through its growth and diversification, notably with the concept of collaborative learning. Among these advancements, federated learning (FL) emerges as a significant decentralized learning approach; however, it is not without its issues. To address the challenges of trust and security in FL, this paper introduces a novel blockchain-based decentralized collaborative learning system and a decentralized asynchronous collaborative learning algorithm for the AI-based industrial Internet environment. We developed a chaincode middleware to bridge blockchain network and AI training for secure, trustworthy and efficient federated learning and presented a refined directed acyclic graph (DAG) consensus mechanism to reduce stale models impact, ensuring efficient learning. Our solutions effectiveness was demonstrated through application on an energy conversion prediction dataset from hydroelectric power generation, validating the practical applicability of our proposed system. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Advancing Credit Card Fraud Detection Through Explainable Machine Learning Methods
The world of finance has experienced a significant shift in the way money flows, due to the advancements in technologies such as online banking, card payments, and QR-based payment systems. These innovative banking payment facilities are offered by ensuring the safety of the transaction and ensuring that only the authorized customer can access and utilize these banking services. Credit card fraud is innovative way to cheat the user of the card. Government all over the word encouraging to the people for the uses of digital money. This research work focuses on analyzing the machine learning database by using a labelled dataset to classify legitimate and fraudulent business transactions with explainable AI. This study is based on decision tree, logistic regression, support vector machine and random forest machine learning techniques. 2024 IEEE. -
Advancing equity in digital classrooms: A personalized learning framework for higher education institutions
Since the introduction of technology-enabled education systems, personalizing the learning process has become more regarded as a promising methodology for revolutionizing the academe. Acknowledging the difference in the learning capability of students across various levels of the academic segment, a personalized learning approach is of paramount importance, especially when teachers cannot efficiently monitor each student (e.g., during emergency remote education). This chapter focused on the necessity for higher education institutions that offer courses from various streams to adopt a personalized learning initiative as a means of offering better online education services. For the successful creation of a personalized online learning experience, this chapter likewise developed a framework that provides a step-by-step guide to educational institutions in moving in this direction. As online education is a trend for future learning, this blueprint could be valuable as well in the post-pandemic era. 2022, IGI Global. All rights reserved.