Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning

In the era of advancing artificial intelligence (AI), its application in agriculture has become increasingly pivotal. This study explores the integration of AI for the discriminative classification of corn diseases, addressing the need for efficient agricultural practices. Leveraging a comprehensive dataset, the study encompasses 21,662 images categorized into four classes: Broken, Discolored, Silk cut, and Pure. The proposed model, an enhanced iteration of MobileNetV2, strategically incorporates additional layers—Average Pooling, Flatten, Dense, Dropout, and softmax—augmenting its feature extraction capabilities. Model tuning techniques, including data augmentation, adaptive learning rate, model checkpointing, dropout, and transfer learning, fortify the model's efficiency. Results showcase the proposed model's exceptional performance, achieving an accuracy of ~96% across the four classes. Precision, recall, and F1-score metrics underscore the model's proficiency, with precision values ranging from 0.949 to 0.975 and recall values from 0.957 to 0.963. In a comparative analysis with state-of-the-art (SOTA) models, the proposed model outshines counterparts in terms of precision, recall, F1-score, and accuracy. Notably, MobileNetV2, the base model for the proposed architecture, achieves the highest values, affirming its superiority in accurately classifying instances within the corn disease dataset. This study not only contributes to the growing body of AI applications in agriculture but also presents a novel and effective model for corn disease classification. The proposed model's robust performance, combined with its competitive edge against SOTA models, positions it as a promising solution for advancing precision agriculture and crop management

A model for computing skyline data items in cloud incomplete databases

Skyline queries intend to retrieve the most superior data items in the database that best fit with the user’s given preference. However, processing skyline queries are expensive and uneasy when applying on large distributed databases such as cloud databases. Moreover, it would be further sophisticated to process skyline queries if these distributed databases have missing values in certain dimensions. The effect of data incompleteness on skyline process is extremely severe because missing values result in un-hold the transitivity property of skyline technique and leads to the problem of cyclic dominance. This paper proposes an efficient model for computing skyline data items in cloud incomplete databases. The model focuses on processing skyline queries in cloud incomplete databases aiming at reducing the domination tests between data items, the processing time, and the amount of data transfer among the involved datacenters. Various set of experiments are conducted over two different types of datasets and the result demonstrates that the proposed solution outperforms the previous approaches in terms of domination tests, processing time, and amount of data transferred

A model for skyline query processing in a partially complete database

In the recent years, skyline queries become one of the predominant and most frequently used queries among preference queries in the database system. Its main theme is to identify and return those data items that are not dominated by any other data item in the database. In the past decade, a tremendous number of research have been conducted emphasized on skyline queries by proposing many variations of skyline techniques for a different type of database. Most of these techniques claimed that a database has complete data and values are always present when process skyline queries. However, this is not necessary to be always the case, particularly for large databases with a high number of dimensions as some values may be missing. Thus, existing techniques cannot be easily tailored to derive skylines in a database with missing values. Two significant issues might be raised, the issue of losing transitivity property which thus leads to the issue of cyclic dominance. Finding skylines in a database with partially complete data has not received enough attention. This paper proposes an efficient model to identify skylines over a database with partial complete data. Experimental results on various types of datasets demonstrate that the proposed approach outperforms the previous approach in terms of the number of pairwise comparisons

SCSA: Evaluating skyline queries in incomplete data

Skyline queries have been extensively incorporated in various contemporary database applications. The list includes but is not limited to multi-criteria decision-making systems, decision support systems, and recommendation systems. Due to its great benefits and wide application range, many skyline algorithms have already been proposed in numerous data settings. Nonetheless, most researchers presume the completion of data meaning that all data item values are available. Since this assumption cannot be sustained in a large number of real-world database applications, the existing algorithms are rather inadequate to be directly applied on a database with incomplete data. In such cases, processing skyline queries on incomplete data incur exhaustive pairwise comparisons between data items, which may lead to loss of the transitivity property of the skyline technique. Losing the transitivity property may in turn give rise to the problem of cyclic dominance. In order to address these issues, we propose a new skyline algorithm called Sorting-based Cluster Skyline Algorithm (SCSA) that combines the sorting and partitioning techniques and simplifies the skyline computation on an incomplete dataset. These two techniques help boost the skyline process and avoid many unnecessary pairwise comparisons between data items to prune the dominated data items. The comprehensive experiments carried out on both synthetic and real-life datasets demonstrate the effectiveness and versatility of our approach as compared to the currently used approaches

Optimizing skyline query processing in incomplete data

Given the significance of skyline queries, they are incorporated in various modern applications including personalized recommendation systems as well as decision-making and decision-support systems. Skyline queries are used to identify superior data items in the database. Most of the previously proposed skyline algorithms work on a complete database where the data are always present (non-missing). However, in many contemporary real-world databases, particularly those databases with large cardinality and high dimensionality, such assumption is not necessarily valid. Hence, missing data pose new challenges if the processing skyline queries cannot easily apply those methods that are designed for complete data. This is due to the fact that imperfect data cause the loss of the transitivity property of the skyline method and cyclic dominance. This paper presents a framework called Optimized Incomplete Skyline (OIS) which utilizes a technique that simplifies the skyline process on a database with missing data and helps prune the data items before performing the skyline process. The proposed strategy assures that the number of the domination tests is significantly reduced. A set of experiments has been accomplished using both real and synthetic datasets aimed at validating the performance of the framework. The experiment results confirm that the OIS framework is indeed superior and steadily outperforms the current approaches in terms of the number of domination tests required to retrieve the skylines

Disaster recovery in cloud computing systems: an overview

With the rapid growth of internet technologies, large-scale online services, such as data backup and data recovery are increasingly available. Since these large-scale online services require substantial networking, processing, and storage capacities, it has become a considerable challenge to design equally large-scale computing infrastructures that support these services cost-effectively. In response to this rising demand, cloud computing has been refined during the past decade and turned into a lucrative business for organizations that own large datacenters and offer their computing resources. Undoubtedly cloud computing provides tremendous benefits for data storage backup and data accessibility at a reasonable cost. This paper aims at surveying and analyzing the previous works proposed for disaster recovery in cloud computing. The discussion concentrates on investigating the positive aspects and the limitations of each proposal. Also examined are discussed the current challenges in handling data recovery in the cloud context and the impact of data backup plan on maintaining the data in the event of natural disasters. A summary of the leading research work is provided outlining their weaknesses and limitations in the area of disaster recovery in the cloud computing environment. An in-depth discussion of the current and future trends research in the area of disaster recovery in cloud computing is also offered. Several work research directions that ought to be explored are pointed out as well, which may help researchers to discover and further investigate those problems related to disaster recovery in the cloud environment that have remained unresolved

Skyline queries computation on crowdsourced- enabled incomplete database

Data incompleteness becomes a frequent phenomenon in a large number of contemporary database applications such as web autonomous databases, big data, and crowd-sourced databases. Processing skyline queries over incomplete databases impose a number of challenges that negatively influence processing the skyline queries. Most importantly, the skylines derived from incomplete databases are also incomplete in which some values are missing. Retrieving skylines with missing values is undesirable, particularly, for recommendation and decision-making systems. Furthermore, running skyline queries on a database with incomplete data raises a number of issues influence processing skyline queries such as losing the transitivity property of the skyline technique and cyclic dominance between the tuples. The issue of estimating the missing values of skylines has been discussed and examined in the database literature. Most recently, several studies have suggested exploiting the crowd-sourced databases in order to estimate the missing values by generating plausible values using the crowd. Crowd-sourced databases have proved to be a powerful solution to perform user-given tasks by integrating human intelligence and experience to process the tasks. However, task processing using crowd-sourced incurs additional monetary cost and increases the time latency. Also, it is not always possible to produce a satisfactory result that meets the user's preferences. This paper proposes an approach for estimating the missing values of the skylines by first exploiting the available data and utilizes the implicit relationships between the attributes in order to impute the missing values of the skylines. This process aims at reducing the number of values to be estimated using the crowd when local estimation is inappropriate. Intensive experiments on both synthetic and real datasets have been accomplished. The experimental results have proven that the proposed approach for estimating the missing values of the skylines over crowd-sourced enabled incomplete databases is scalable and outperforms the other existing approaches

Unveiling the core of IoT: comprehensive review on data security challenges and mitigation strategies

The Internet of Things (IoT) is a collection of devices such as sensors for collecting data, actuators that perform mechanical actions on the sensor's collected data, and gateways used as an interface for effective communication with the external world. The IoT has been successfully applied to various fields, from small households to large industries. The IoT environment consists of heterogeneous networks and billions of devices increasing daily, making the system more complex and this need for privacy and security of IoT devices become a major concern. The critical components of IoT are device identification, a large number of sensors, hardware operating systems, and IoT semantics and services. The layers of a core IoT application are presented in this paper with the protocols used in each layer. The security challenges at various IoT layers are unveiled in this review paper along with the existing mitigation strategies such as machine learning, deep learning, lightweight encryption techniques, and Intrusion Detection Systems (IDS) to overcome these security challenges and future scope. It has been concluded after doing an intensive review that Spoofing and Distributed Denial of Service (DDoS) attacks are two of the most common attacks in IoT applications. While spoofing tricks systems by impersonating devices, DDoS attacks flood IoT systems with traffic. IoT security is also compromised by other attacks, such as botnet attacks, man-in-middle attacks etc. which call for strong defenses including IDS framework, deep neural networks, and multifactor authentication system