Fog computing provides microdata center (MDC) facilities closer to the users and applications, which help overcome application latency response time concerns. However, guaranteeing specific service-level objectives (SLOs) for applications running on MDC requires automatic scaling of allocated resources by efficiently utilizing available infrastructure capacity. In this article, we propose a novel predictive autoscaling method microservices fog satisfy SLO. Initially, our proposed approach...

The purpose of the study is to (a) contribute annotating an Altmetrics dataset across five disciplines, (b) undertake sentiment analysis using various machine learning and natural language processing–based algorithms, (c) identify best-performing model (d) provide a Python library for dataset. First, researchers gave set guidelines two human annotators familiar with task related tweet annotation scientific literature. They duly labelled sentiments, achieving inter-annotator agreement (IAA)...

Abstract Most existing studies are focused on popular languages like English, Spanish, Chinese, Japanese, and others, however, limited attention has been paid to Urdu despite having more than 60 million native speakers. In this paper, we develop a deep learning model for the sentiments expressed in under‐resourced language. We an open‐source corpus of 10,008 reviews from 566 online threads topics sports, food, software, politics, entertainment. The objectives work bi‐fold (a) creation...

We introduce a sophisticated deep-learning model designed for the early detection of COVID-19 and pneumonia. The employs convolutional neural network-integrated with atrous spatial pyramid pooling. pooling mechanism enhances network model's ability to capture fine large-scale features, optimizing accuracy in chest X-ray images. This improvement, along transfer learning, significantly overall performance. By utilizing data augmentation address scarcity available images, our pooling-enhanced...

In developing countries like Pakistan, cleft surgery is expensive for families, and the child also experiences much pain. this article, we propose a machine learning–based solution to avoid in mother’s womb. The possibility of lip palate embryos can be predicted before birth by using proposed solution. We collected 1000 pregnant female samples from three different hospitals Lahore, Punjab. A questionnaire has been designed obtain variety data, such as gender, parenting, family history cleft,...

The majority of online comments/opinions are written in text-free format. Sentiment Analysis can be used as a measure to express the polarity (positive/negative) comments/opinions. These comments/ opinions different languages i.e. English, Urdu, Roman Hindi, Arabic etc. Mostly, people have worked on sentiment analysis English language. Very limited research work has been done Urdu or languages. Whereas, Hindi/Urdu is third largest language world. In this paper, we focus Urdu. There no...

A citation is deemed as a potential parameter to determine linkage between research articles. The has extensively been employed form multifarious academic aspects like calculating the impact factor of journals, h-Index researchers, allocate different grants, find latest trends, etc. current state-of-the-art contends that all citations are not equal importance. Based on this argument, trend in classification community categorizes into important and non-important reasons. proposed approaches...

Metabolic alterations play a crucial role in glioma development and progression can be detected even before the appearance of fatal phenotype. We have compared circulating metabolic fingerprints patients versus healthy controls, for first time, quest to identify panel small, dysregulated metabolites with potential serve as predictive and/or diagnostic marker clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used untargeted...

We propose an approach for the early detection of COVID-19 and other related lung diseases using artificial intelligence (AI) deep learning-based methods. The proposed involves utilizing transfer learning over convolutional neural networks (CNNs) classification chest X-ray images as normal or positive. To address limited availability images, we employed data augmentation techniques to expand dataset size enhance our model's performance on unseen data. used variations four pre-trained models:...

Named entity recognition (NER) refers to the identification of proper nouns from natural language text and classifying them into named types, such as person, location, organization. Due widespread applications NER, numerous NER techniques benchmark datasets have been developed for both Western Asian languages. Even though Shahmukhi script Punjabi has used by nearly three fourths speakers worldwide, Gurmukhi main focus research activities. Specifically, a corpus is non-existent, which...

Containers provide a lightweight runtime environment for microservices applications while enabling better server utilization. Automatic optimal allocation of CPU pins to the containers serving specific workloads can help minimize completion time jobs. Most existing state-of-the-art focused on building new efficient scheduling algorithms placing infrastructure, and resources are allocated manually statically. An automatic method identify allocate improve efficiency algorithms. In this...

Recently, the use of NoSQL databases has grown to manage unstructured data for applications ensure performance and scalability. However, many organizations prefer transfer from an operational database a SQL-based relational using existing tools business intelligence, analytics, decision making, reporting. The methods transformation require manual schema mapping, which requires domain expertise consumes noticeable time. Therefore, efficient automatic method is needed transform into structured...

Autoscaling methods are important to ensure response time guarantees for cloud-hosted microservices. Most of the existing state-of-the-art autoscaling use rule-based reactive policies with static thresholds defined either on monitored resource consumption metrics such as CPU and memory utilization or application-level time. However, it is challenging determine most appropriate threshold values minimize performance violations. Whereas, predictive can help address these challenges. These...

Predicting student's academic performance during online learning has been considered a major task the pandemic period. During mode of learning, activities have affected in such way that management educational institutions planned to design support systems for predicting reduce dropout ratio students and bring improvement activities. COVID-19, main challenge is maintaining grades by their using different techniques as Education Data Mining Learning Analytics. Different features identified...

Containerized workloads are gaining traction due to microservices architecture adaptation in many fields, including healthcare, finance, Internet of Things, and smart cities. Modern data centers containerized facilitate this growing demand. Most the existing resource allocation methods for used efficient scheduling algorithms place containers using static computing resources. These techniques not energy do help maximize center utilization. Dynamic a migration-enabled placement method can...

Autoscaling methods are employed to ensure the scalability of cloud-hosted applications. The public-facing applications prone receive sudden workload bursts, and existing autoscaling do not handle bursty workloads gracefully. It is challenging detect burst online from incoming dynamic traffic, then identifying appropriate resources address without overprovisioning even harder. In this paper, we challenge by investigating method for detection proposed a novel predictive use satisfying...

Laparoscopic education and surgery assessments increase the success rates lower risks during actual surgeries. Hospital residents need a secure setting, trainees require safe controlled environment with cost-effective resources where they may hone their laparoscopic abilities. Thus, we have modeled developed surgical simulator to provide initial training in Partial Nephrectomy (LPN—a procedure treat kidney cancer or renal masses). To achieve this, created virtual using an open-source game...