Among various calamities, conflagrations stand out as one of the most-prevalent and -menacing adversities, posing significant perils to public safety societal progress. Traditional fire-detection systems primarily rely on sensor-based detection techniques, which have inherent limitations in accurately promptly detecting fires, especially complex environments. In recent years, with advancement computer vision technology, video-oriented fire owing their non-contact sensing, adaptability...

Convolutional neural networks (CNN) are relational with grid-structures and spatial dependencies for two-dimensional images to exploit location adjacencies, color values, hidden patterns. use sparse connections at high-level sensitivity layered connection complying indiscriminative disciplines local mapping footprints. This fact varies architectural dependencies, insight inputs, number types of layers its fusion derived signatures. research focuses this gap by incorporating GoogLeNet,...

The lack of sentiment resources in poor resource languages poses challenges for the analysis which machine learning is involved. Cross-lingual and semi-supervised approaches have been deployed to represent most common ways that can overcome this issue. However, performance existing methods degrades due quality translated resources, data sparseness more specifically, language divergence. An integrated model uses a an ensembled while utilizing available tackle divergence related issues...

High-utility itemset mining (HUIM), which is the detection of high-utility itemsets (HUIs) in a transactional database, provides decision maker with greater flexibility to exploit item utilities, such as quantity and profits, extract remarkable efficient database patterns. However, most prevailing empirical articles have focused on HUIs. Nevertheless, many practical situations, low-utility (LUIs) maintain high level significance usage (e.g., security systems represent system vulnerabilities...

The classical Isomap is the most common unsupervised nonlinear manifold method and widely being used in visualizations dimension reductions. However, when it applied to real-world datasets, shows shortcomings for shortest path between all pairs of data points, which are based on nearest neighborhood G graph via Dijkstra algorithm, makes a very time-consuming step. other critical problem has lack topological stability graph. In this paper, we propose novel technique called FastIsomapVis above...

Federated Learning (FL) is a novel framework of decentralized machine learning. Due to the feature FL, it vulnerable adversarial attacks in training procedure, e.g. , backdoor attacks. A attack aims inject into learning model such that will make arbitrarily incorrect behavior on test sample with some specific trigger. Even though range methods FL has been introduced, there are also defending against them. Many utilize abnormal characteristics models or difference between and regular models....

Allocating bandwidth guarantees to applications in the cloud has become increasingly demanding and essential as compete share network resources. However, cloud-computing providers offer no a environment, predictably preventing tenants from running their applications. Existing schemes practical cluster abstraction solutions emulating underlying physical resources, proving impractical; however, providing virtual abstractions remained an step right direction. In this paper, we consider...

A number of studies and methods have been proposed to obtain more efficient patterns that meet the requirements decision makers by considering frequency utility thresholds, for example, frequent high rare patterns. However, need know lead minimal benefits appear frequently understand reasons low interest rates, as their may be a factor in increasing rates. Therefore, this paper we propose new framework extracting model is important many cases, named as, inefficient item set (FLUI). In order...

High Utility Itemset Mining (HUIM) is the task of extracting actionable patterns considering utility items such as profits and quantities. An important issue with traditional HUIM methods that they evaluate all using a single threshold, which inconsistent reality due to differences in nature importance items. Recently, algorithms were proposed address this problem by assigning minimum item threshold each item. However, since (MIU) expressed percentage external utility, these still face two...

Abstract High Utility Itemset Mining (HUIM) alludes to the identification of itemsets high utility in value-based database UP-Growth algorithm is a standout amongst best algorithms for overcome challenge candidate generation and scan reputedly previous algorithms. However, it needs twice actualize UP tree. Regarding updating existing data with new information, UP-growth twofold scanning information information. The fundamental motivation behind this work build up another algorithm,...

Nonlinear Dimensionality Reduction (NLDR) is a well-known approach of manifold learning to transform the data from high low dimensional space. After studying various techniques proposed for NLDR, we find that performance improvement still required. Therefore, adopt classical Isomap, which faces Shortest Path Distance (SPD) and computational time cost problems. These problems are occurring due Dijkstra algorithm. This paper presents A*FastIsomap method SPD issues, based on A* Search Algorithm...

Despite the success of current High Utility Itemset Mining (HUIM) methods in calculating utility items using internal and external utility, effect cost an item's is unknown. Thus, this paper proposes a novel method for HUIM that considers item cost, named Actual High-Utility (AHUIM), to extract new patterns called Itemsets (AHUIs). In proposed method, calculated its cost. However, challenge different may not have same impact on whether threshold satisfied or not. Hence, single inappropriate....