Ajith Abraham

FLAME University · Faculty of Computing and Data Sciences

Sentiment categorization at the aspect level tries to provide fine-grained sentiment polarities for distinct aspects inside a sentence. Some issues remain unresolved in the previous work. First, the specific position context is not fully addressed. Second, the distinct aspect of an opinionated sentence is evaluated independently. Also, the present,...

A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms. Random Forest and Support Vector Machine (SVM) were used to classify the input histopathological images into whether they were cancerous or non-cancerous. The imp...

Although the Internet and social media provide people with a range of opportunities and benefits in a variety of ways, the proliferation of fake news has negatively affected society and individuals. Many efforts have been invested to detect the fake news. However, to learn the representation of fake news by context information, it has brought many...

Epilepsy is a chronic seizure state of an individual. The group of brain cells reflects abnormal electrical activity. Electroencephalography (EEG) is a popular tool that monitors brain activities and diagnoses neurological disorders. The classification of seizure and non-seizure data is a challenging task when dealing with complex transformed featu...

This paper presents a roadmap to the application of AI techniques and big data for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and big data techniques as future solutio...

The speech and hearing-impaired community use sign language as the primary means of communication. It is quite challenging for the general population to interpret or learn sign language completely. A sign language recognition system must be designed and developed to address this communication barrier. Most current sign language recognition systems...

Web Information Processing (WIP) has enormously impacted modern society since a huge percentage of the population relies on the internet to acquire information. Social Media platforms provide a channel for disseminating information and a breeding ground for spreading misinformation, creating confusion and fear among the population. One of the techn...

Visual analysis of an electroencephalogram (EEG) by medical professionals is highly time-consuming and the information is difficult to process. To overcome these limitations, several automated seizure detection strategies have been introduced by combining signal processing and machine learning. This paper proposes a hybrid optimization-controlled e...

One of the most common oncologies analyzed among people worldwide is lung malignancy. Early detection of lung malignancy helps find a suitable treatment for saving human lives. Due to its high resolution, greater transparency, and low noise and distortions, Computed Tomography (CT) images are most commonly used for processing. In this context, this...

Today’s datasets are usually very large with many features and making analysis on such datasets is really a tedious task. Especially when performing classification, selecting attributes that are salient for the process is a brainstorming task. It is more difficult when there are many class labels for the target class attribute and hence many resear...

The emergence of many urban problems in the field of urban transportation, such as air pollution, increased accidents, and economic losses, reinforces the need to move toward sustainable transportation. In this regard, it is very important to identify and prioritize sustainable transport development policies. In the meantime, creating a change in t...

The growth and development of scientific applications have demanded the creation of efficient resource management systems. Resource provisioning and scheduling are two core components of cloud resource management systems. Cloud resource scheduling is the most critical problem to solve efficiently due to the heterogeneity of resources, their inter-d...

Emerging technologies such as blockchain and digital twins are essential for the rapid development and employment in Industry 5.0 revolution. With the growing number of industrial IoT nodes, optimizing the network and availing of limited resources to enable secure transmission is difficult. A digital twin is the virtual representation of a physical...

One of today's inspiring issues is the 2D histogram-based multilevel threshold selection which is used for segmenting images into several regions. The image analysis warrants exploration of multiclass thresholding techniques using various entropy-based objective functions. In this context, the Shannon type of entropic function without inherent deci...

The induction motor plays a vital role in industrial drive systems due to its robustness and easy maintenance but at the same time, it suffers electrical faults, mainly rotor faults such as broken rotor bars. Early shortcoming identification is needed to lessen support expenses and hinder high costs by using failure detection frameworks that give f...

Human ideas and sentiments are mirrored in facial expressions. They give the spectator a plethora of social cues, such as the viewer’s focus of attention, intention, motivation, and mood, which can help develop better interactive solutions in online platforms. This could be helpful for children while teaching them, which could help in cultivating a...

Air pollution is a global issue causing major health hazards. By proper monitoring of air quality, actions can be taken to control air pollution. Satellite remote sensing is an effective way to monitor global atmosphere. Various sensors and instruments fitted to satellites and airplanes are used to obtain the radar images. These images are quite co...

Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a...

Background One of the challenging and the primary stages of medical image examination is the identification of the source of any disease, which may be the aberrant damage or change in tissue or organ caused by infections, injury, and a variety of other factors. Any such condition related to skin or brain sometimes advances in cancer and becomes a l...

Coronavirus outbreaks in 2019 (COVID-19) have been a huge disaster in the fields of health, economics, education, and tourism in the last two years. For diagnosis, a quick interpretation of the COVID-19 chest X-ray image is required. There is also a strong need to find an efficient multiclass segmentation technique for the analysis of COVID-19 X-ra...

Link prediction is one of the most important methods to uncover evolving mechanisms of dynamic complex networks. Determining these links raises well-known technical challenges in terms of weak correlation, uncertainty and non-stationary. In this paper, we presented a novel gated graph convolutional network (GCN) based on spatio-temporal semi-variog...

In recent years, ransomware attacks have become increasingly rampant by the offenders for which ransomware has maintained a major cyber security threat as time progresses. With paradigm shift from social to technical factors, ransomware has also maintained the equal adaptiveness by shifting its focus from initial days' scareware and locker attacks...

This paper presents a bi-level blood supply chain network under uncertainty during the COVID-19 pandemic outbreak using a Stackelberg game theory technique. A new two-phase bi-level mixed-integer linear programming model is developed in which the total costs are minimized and the utility of donors is maximized. To cope with the uncertain nature of...

Autonomic computing investigates how systems can achieve (user) specified “control” outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and...

Achieving sustainable profit advantage, cost reduction and resource utilization are always a bottleneck for resource providers, especially when trying to meet the computing needs of resource hungry applications in mobile edge-cloud (MEC) continuum. Recent research uses metaheuristic techniques to allocate resources to large-scale applications in ME...

Fake news detection mainly relies on the extraction of article content features with neural networks. However, it has brought some challenges to reduce the noisy data and redundant features, and learn the long-distance dependencies. To solve the above problems, Dual-channel Convolutional Neural Networks with Attention-pooling for Fake News Detectio...

A new chaotic time-varying binary whale optimization algorithm (CBWOATV) is introduced in this paper to optimize the feature selection process in Amphetamine-type Stimulants (ATS) and non-ATS drugs classification. Two enhancement methods were introduced in this study to provide a fit balance between exploration and exploitation in standard WOA. Fir...

Clustering is a technique of grouping the data objects into clusters. Many metaheuristic algorithms based on swarm intelligence, physic laws, and chemical reactions, among others, have been developed for clustering. In this study, an enhanced whale optimization algorithm (EWOA) is introduced to solve clustering problems. The whale optimization algo...

Publisher: The book will be published in Springer Book Series, which is indexed in Scopus etc. Theme : In recent times, advancements to blockchain and quantum technologies are widely discussed on various platforms. These technologies and their integration with other useful technologies of recent times have created numerous applications and abilitie...

The development of a Time series Forecasting System is a major concern for Artificial Intelligence researchers. Commonly, existing systems only assess temporal features and analyze the behavior of the data over time, thus, resulting in uncertain forecasting accuracy. Although many forecasting systems were proposed in the literature; they have not y...

Online group buying, could be a framework that gives daily discounts for different administrations and items, be a system of showcasing at the intersection of advancement and estimating that had pulled in the consideration of both specialists and the scholarly community. The aim of this study is to provide a theoretical model to inspect the intenti...

Lung cancer is one of the most common causes of deaths worldwide. The ability to predict and diagnose cancer has become increasingly significant in recent years. Early identification of lung cancer appears to be the only way to improve patients’ survival rates, which is a difficult effort due to the structure of cancer cells, which has most of the...

The pre-processing approach is the first stage in the diagnostic procedure. This is particularly significant in noisy and fuzzy photos. It is one of the prerequisite procedures for achieving great efficiency in subsequent image processing steps. The initial step toward an automated CAD (Computer Aided Detection) system for a range of medical applic...

Clinicians can detect diseases early, thanks to the digital image processing methodologies, which improve health together with the treatment experience. The technology of magnetic resonance imaging (MRI) is frequently employed in the brain, research for any kind of related illness. The brain MR image requires precise automated thresholding for a me...

Lessons from COVID-19: Impact on Healthcare Systems and Technology uncovers the impact that COVID-19 has made on healthcare and technology industries. State-of-the-art case studies, empirical research, and new trends in technology-mediated solution are discussed to help inform and guide readers in understanding the effects that the COVID-19 outbrea...

The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficienc...

Millions of affected people and thousands of victims are consequences of earthquakes, every year. Therefore, it is necessary to prepare a proper preparedness and response planning. The objectives of this paper are i) minimizing the expected value of the total costs of relief supply chain, ii) minimizing the maximum number of unsatisfied demands for...

This paper addresses multi-objective optimization and the truss optimization problem employing a novel meta-heuristic that is based on the real-world water cycle behavior in rivers, rainfalls, streams, etc. This meta-heuristic is called multi-objective water cycle algorithm (MOWCA) which is receiving great attention from researchers due to the good...

Healthcare systems are increasingly incorporating Artificial Intelligence into their systems, but it is not a solution for all difficulties. AI's extraordinary potential is being held back by challenges such as a lack of medical datasets for training AI models, adversarial attacks, and a lack of trust due to its black box working style. We explored...

Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of machinery. Majority of these machines comprise rotating components and are called rotating machines. The engineers' top priority is to maintain these critical machines to reduce the unplanned shutdown and increase the useful life of machinery. Predictive m...

The circulation of fake news among people is not something new, as it has been present ages ago. In a connected world, due to the rapid development in the means of communication, fake news has become a very dangerous factor in daily life due to its massive impact. Furthermore, the size and speed of data shared through mediums makes it is difficult...

This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amount of pollutants, and the number of job opportunities created in the proposed supply chain network u...

Regression testing is essential for continuous integration and continuous development. It is needed to ensure that the modifications have not produced any errors or faults, thereby maintaining the quality and reliability of the software. The testers usually avoid exhaustive retesting because it requires lots of effort and time. The test case priori...

In this paper, a new production, allocation, location, inventory holding, distribution, and flow problems for a new sustainable-resilient health care network related to the COVID-19 pandemic under uncertainty is developed that also integrated sustainability aspects and resiliency concepts. Then, a multi-period, multi-product, multi-objective, and m...

Delivering high-quality software products is a challenging task. It needs proper coordination from various teams in planning, execution, and testing. Many software products have high numbers of defects revealed in a production environment. Software failures are costly regarding money, time, and reputation for a business and even life-threatening if...

Swarm-intelligence (SI) algorithms have received great attention in addressing various binary optimization problems such as feature selection. In this article, a new time-varying modified Sigmoid transfer function with two time-varying updating schemes is proposed as the binarization method for particle swarm optimization (PSO), grey wolf optimizat...

The diagnosis of tumours in its initial stage plays a crucial role in improving the clinical outcomes of a patient. Evaluation of brain tumours from a large number of MRI images generated regularly in a clinical environment is a complex and time-consuming process. With this, there comes a need for an an efficient and accurate model for the early de...

The spread of COVID-19 has had a serious impact on either work or the lives of people. With the decrease in physical social contacts and the rise of anxiety on the pandemic, social media has become the primary approach for people to access information related to COVID-19. Social media is rife with rumors and fake news, causing great damage to the S...

This paper proposes a hybrid version of the Salp Swarm Algorithm (SSA) and the hill climbing (HC) technique using various selection schemes to solve engineering design problems. The proposed algorithm consists of two stages. In the first stage, the basic SSA is hybridized with HC local search to improve its exploitation capabilities; we refer to th...

In computational chemistry, the high-dimensional molecular descriptors contribute to the curse of dimensionality issue. Binary whale optimization algorithm (BWOA) is a recently proposed metaheuristic optimization algorithm that has been efficiently applied in feature selection. The main contribution of this paper is a new version of the nonlinear t...

Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and in...

Typically, in sparse representation‐based classifiers, the weight associated with each training sample is ignored, resulting in reduced accuracy. Moreover, individual binary classifiers solved a multiclass problem. It requires more time as multiple runs are needed to compute the accuracy. In this paper, we propose a novel optimal sparse representat...

Recently, the entropic based multilevel threshold selection methods use 2D histogram, which is constructed using the local averages, leading to a loss of edges. Further, the computation of the entropy using the diagonal pixel values only leads to a loss of information. Nevertheless, traditional 2D histogram based multilevel thresholding methods suf...

Today, the issue of monitoring water quality in distribution networks is of particular importance as the last step in the water production process. In this research, a change in the standard of vehicle routing is introduced, which is used in monitoring the quality of the water distribution network. In this study, a large number of teams visit candi...