The generation volatility of photovoltaics (PVs) has created several control and operation challenges for grid operators. For a secure reliable day or hour-ahead electricity dispatch, the operators need visibility their synchronous asynchronous generators' capacity. It helps them to manage spinning reserve, inertia frequency response during any contingency events. This study attempts provide machine learning-based PV power forecasting both short long-term. chosen Alice Springs, one...

ABSTRACT Background Confidential computing has gained prominence due to the escalating volume of data‐driven applications (e.g., machine learning and big data) acute desire for secure processing sensitive data, particularly across distributed environments, such as edge‐to‐cloud continuum. Objective Provided that works accomplished in this emerging area are scattered various research fields, paper aims at surveying fundamental concepts cutting‐edge software hardware solutions developed...

In this paper, we provide a comprehensive performance evaluation of popular symmetric and asymmetric key encryption algorithms to selecting better utilization on resource constraint mobile devices. We focus AES, RC4, Blowfish, CAST, 3DES, Twofish as DSA ElGamal an algorithms. The is compared using different parameters such size, data blocks, types, encryption/decryption speed. experiments are performed exploit the effectiveness cryptographic use in real life applications where fast execution...

Security and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt services. One common approach address is client-side encryption where encrypted on client machine before being cloud. Having cloud, however, limits ability clustering, which a crucial part analytics applications, such as search systems. To overcome limitation, this paper, we present an named ClustCrypt efficient topic-based clustering unstructured dynamically estimates optimal...

Word sense disambiguation (WSD) methods identify the most suitable meaning of a word with respect to usage that in specific context. Neural network-based WSD approaches rely on sense-annotated corpus since they do not utilize lexical resources. In this study, we both context and related gloss information target model semantic relationship between set glosses. We propose SensPick, type stacked bidirectional Long Short Term Memory (LSTM) network perform task. The experimental evaluation...

Smart IoT-based systems often desire continuous execution of multiple latency-sensitive Deep Learning (DL) applications. The edge servers serve as the cornerstone such IoT based systems, however, their resource limitations hamper (multi-tenant) DL challenge is that, applications function on bulky "neural network (NN) models" that cannot be simultaneously maintained in limited memory space edge. Accordingly, main contribution this research to overcome contention challenge, thereby, meeting...

Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions do not provide control of the data users. This has raised security privacy concerns for many organizations (users) with sensitive utilize cloud-based solutions. User-side encryption can potentially address these by establishing user-centric granting user. Nonetheless, user-side limits ability process (e.g., search) encrypted on cloud. Accordingly,...

With the meteoric growth of technology, individuals and organizations are widely adopting cloud services to mitigate burdens maintenance. Despite its scalability ease use, many users who own sensitive data refrain from fully utilizing due confidentiality concerns. Maintaining for at rest in transit has been explored but remains vulnerable while it is use. This vulnerability further elevated once scope computing spans across edge-to-cloud continuum. Accordingly, goal this dissertation enable...

Confidential computing has gained prominence due to the escalating volume of data-driven applications (e.g., machine learning and big data) acute desire for secure processing sensitive data, particularly, across distributed environments, such as edge-to-cloud continuum. Provided that works accomplished in this emerging area are scattered various research fields, paper aims at surveying fundamental concepts, cutting-edge software hardware solutions developed confidential using trusted...

Cloud services are widely deployed to store and process big data. Organizations who deal with data, especially large document set, prefer utilizing cloud for storage computational efficiency. However, processing text corpus, an inefficient data is computationally expensive real-time systems. In addition, efficient memory utilization important cluster including corpus. Clustering of the corpus component various retrieval systems such as PubMed <sup...

Personalized top-N recommendation algorithms are among the most effective techniques providing customized suggestions in information retrieval applications.Most of current methods construct personalized recommendations based on various loss functions such as pairwise ranking and point-wise recovery loss.In this paper, we propose a method non-negative matrix factorization with divergence function.Our finds latent factors from existing data to improve predictions.We formulate learning problem...

Summary Cloud‐based enterprise search services (e.g., Amazon Kendra) are enchanting to big data owners by providing them with convenient solutions over their datasets. However, individuals and businesses dealing confidential criminal reports) reluctant fully embrace such cloud due valid privacy concerns. Solutions based on client‐side encryption have been developed mitigate these Nonetheless, hinder processing, especially, clustering, which is pivotal in applications as real‐time large...

Unravelling transcription factor binding sites, popularly known as motifs, is a prime concern in Bioinformatics. Several evolutionary algorithms like Linear PSO are used for motif discovery. However, searches linearly and appears slow. Here, we have clustering algorithm based on k nearest neighbor finding eligible sequence that can be potential motif. The proposed needs no preprocessing reference PSO. Hamming distance taken criteria generate clusters makes the faster. Primarily, neighbors...

Security and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt services. One common approach address is client-side encryption where encrypted on client machine before being cloud. Having cloud, however, limits ability clustering, which a crucial part analytics applications, such as search systems. To overcome limitation, this paper, we present an named ClustCrypt efficient topic-based clustering unstructured dynamically estimates optimal...

Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions do not provide control of the data users. This has raised security privacy concerns for many organizations (users) with sensitive utilize cloud-based solutions. User-side encryption can potentially address these by establishing user-centric granting user. Nonetheless, user-side limits ability process (e.g., search) encrypted on cloud. Accordingly,...

An important issue for concurrent garbage collection in virtual machines (VM) is to identify which collector (GC) use during the process. For instance, Java program execution times differ greatly based on employed GC. It has not been possible optimal GC algorithms a specific before exhaustively profiling all available algorithms. In this paper, we present an adaptive and (ACGC) technique that can predict algorithm without going through We implement machine test it using standard benchmark...