Using transfer learning-based plant disease classification and detection for sustainable agriculture.

Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's "Zero Hunger," "Climate Action," and "Responsible Consumption and Production" sustainable development goals. In addition to already available methods for early disease detection and classification facing overfitting and fine feature extraction complexities during the training process, how early signs of green attacks can be identified or classified remains uncertain. Most pests and disease symptoms are seen in plant leaves and fruits, yet their diagnosis by experts in the laboratory is expensive, tedious, labor-intensive, and time-consuming.

A Comprehensive Study on Cyber Attacks in Communication Networks in Water Purification and Distribution Plants: Challenges, Vulnerabilities, and Future Prospects.

In recent years, the Internet of Things (IoT) has had a big impact on both industry and academia. Its profound impact is particularly felt in the industrial sector, where the Industrial Internet of Things (IIoT), also known as Industry 4.0, is revolutionizing manufacturing and production through the fusion of cutting-edge technologies and network-embedded sensing devices.

Security Framework for Network-Based Manufacturing Systems with Personalized Customization: An Industry 4.0 Approach.

Smart manufacturing is pivotal in the context of Industry 4.0, as it integrates advanced technologies like the Internet of Things (IoT) and automation to streamline production processes and improve product quality, paving the way for a competitive industrial landscape. Machines have become network-based through the IoT, where integrated and collaborated manufacturing system responds in real time to meet demand fluctuations for personalized customization.

High-Speed Network DDoS Attack Detection: A Survey.

Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber-Physical Systems (CPS).

Insights into the structure, functional perspective, and pathogenesis of ZIKV: an updated review.

Zika virus (ZIKV) poses a serious threat to the entire world. The rapid spread of ZIKV and recent outbreaks since 2007 have caused worldwide concern about the virus. Diagnosis is complicated because of the cross-reactivity of the virus with other viral antibodies.

Identification of natural product-based effective inhibitors of spleen tyrosine kinase (SYK) through virtual screening and molecular dynamics simulation approaches.

Spleen tyrosine kinase (SYK) is a non-receptor tyrosine kinase that plays an essential role in signal transduction across different cell types. In the context of allergy and autoimmune disorders, it is a crucial regulator of immune receptor signaling in inflammatory cells such as B cells, mast cells, macrophages, and neutrophils. Developing SYK kinase inhibitors has gained significant interest for potential therapeutic applications in neurological and cancer-related conditions.

Using a novel convolutional neural network for plant pests detection and disease classification.

Background: Early plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security.

Methodology: An enhanced convolutional neural network (CNN) along with long short-term memory (LSTM) using a majority voting ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre-trained models, deep feature extractions have been obtained from connected layers.

A Systematic Literature Review on Service Composition for People with Disabilities: Taxonomies, Solutions, and Open Research Challenges.

Integrating smart heterogeneous objects, IoT devices, data sources, and software services to produce new business processes and functionalities continues to attract considerable attention from the research community due to its unraveled advantages, including reusability, adaptation, distribution, and pervasiveness. However, the exploitation of service-oriented computing technologies (e.g.

A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities.

The use of software and IoT services is increasing significantly among people with special needs, who constitute 15% of the world's population. However, selecting appropriate services to create a composite assistive service based on the evolving needs and context of disabled user groups remains a challenging research endeavor. Our research applies a scenario-based design technique to contribute (1) an inclusive disability ontology for assistive service selection, (2) semi-synthetic generated disability service datasets, and (3) a machine learning (ML) framework to choose services adaptively to suit the dynamic requirements of people with special needs.

An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation.

With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features.

Moisture Computing-Based Internet of Vehicles (IoV) Architecture for Smart Cities.

Recently, the concept of combining 'things' on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network.

WEAMR-a weighted energy aware multipath reliable routing mechanism for hotline-based WSNs.

Reliable source to sink communication is the most important factor for an efficient routing protocol especially in domains of military, healthcare and disaster recovery applications. We present weighted energy aware multipath reliable routing (WEAMR), a novel energy aware multipath routing protocol which utilizes hotline-assisted routing to meet such requirements for mission critical applications. The protocol reduces the number of average hops from source to destination and provides unmatched reliability as compared to well known reactive ad hoc protocols i.

An enhanced backbone-assisted reliable framework for wireless sensor networks.

An extremely reliable source to sink communication is required for most of the contemporary WSN applications especially pertaining to military, healthcare and disaster-recovery. However, due to their intrinsic energy, bandwidth and computational constraints, Wireless Sensor Networks (WSNs) encounter several challenges in reliable source to sink communication. In this paper, we present a novel reliable topology that uses reliable hotlines between sensor gateways to boost the reliability of end-to-end transmissions.