S P Raja
Biographic Data
| ID | 5743720 |
|---|---|
| NAME | S P Raja |
| GIVEN NAMES | S P |
| FAMILY NAME | Raja |
| SIGNATURE | RAJA S P |
| AFFILIATIONS | Vellore Institute of Technology University |
| ORCID | 0000-0002-7216-2207 |
| VERIFIED | Yes |
| TOTAL WORKS | 14 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 14 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
An Extreme Learning Machine Technique for the Numerical Solution of Fractional Differential Equations
This research intends to create a novel approach for solving fractional differential equations (FDEs) of both linear and nonlinear types utilizing the fractional shifted Legendre neural network alongside the extreme machine learning (ML) algorithm. The neural network serves as a trial solution to formulate the loss function where it is used to train the neural network through an extreme learning machine (ELM) to obtain the solution. The new appro…
Effective Analysis of Machine and Deep Learning Methods for Diagnosing Mental Health Using Social Media Conversations
The increasing incidence of mental health issues demands innovative diagnostic methods, especially within digital communication. Traditional assessments are challenged by the sheer volume of data and the nuanced language found on social media and other text-based platforms. This study seeks to apply machine learning (ML) to interpret these digital narratives and identify patterns that signal mental health conditions. We apply natural language pro…
Designing Energy-Aware Scheduling and Task Allocation Algorithms for Online Reinforcement Learning Applications in Cloud Environments
With the rapid proliferation of machine learning applications in cloud computing environments, addressing crucial challenges concerning energy efficiency becomes pressing, including addressing the high power consumption of such workloads. In this regard, this work focuses much on the development of an energy-aware scheduling and task assignment algorithm that, while optimizing energy consumption, maintains required performance standards in deploy…
Ebpga
Automatic text summarization (ATS) deals with compressing a long document into a shorter version, retaining the key ideas of the original document. It aims to tackle the problem of information overload resulting from the continuous creation of documents. ATS helps people make news feeds, meeting reports, summarized legal and financial documents, study tools, article outlines, social media analysis, and so on. Extractive text summarization (ETS) i…
RobinNet
It is essential to understand the underlying emotions that are imparted through speech in order to study social communications as well as to generate seamless human–computer interactions. Speech emotion recognition (SER) is a considerably challenging task due to the lack of sufficient data and the complex interdependence of phrases with the context and emotion they imply. This article presents RobinNet: a RoBERTa-and Inception-ResNet-V2-based nov…
A Novel Lie Hypergraph Based Lifetime Enhancement Routing Protocol for Environmental Monitoring in Wireless Sensor Networks
Wireless sensor network (WSN) is a rapid surging technology promising many fruitful innovations for the user community. The use of WSNs for continuous monitoring of environmental factors in severe situations, such as detection of volcanoes, forest fires, floods, and so on. Despite WSN’s various potential applications, energy and deployment are two major concerns influencing the network life span. The primary requirement is to monitor node energy …
Fog Assisted Personalized Dynamic Pricing for Smartgrid
Unit electricity pricing is of vital importance in an electric grid network. It is essential to charge the customers in a fair manner. Traditional pricing models are found to be inadequate in the ability to charge customers fairly due to a lack of support for real-time communication between customers and electricity providers. With the introduction of smart devices in the electric grid domain, the real-time gathering of information is a seamless …
Machine learning algorithm-based spam detection in social networks
Generation Expansion Planning considering environmental impact and sustainable development for an Indian state using the Leap platform
Dual Distance Center Loss
Center loss is widely used as a supervision tool in deep learning method. However, the center loss also has some shortcomings, the most important of which is that it must be combined with softmax loss to run well. In this article, we sum up five shortcomings of center loss and solve all of them by proposing a dual distance center loss (DDCL). Compared with center loss, DDCL can run without the combination of softmax to supervise training the mode…
Green Computing
Green computing refers to sustainable, environment-friendly computing that harnesses information and technology. Green computing can be thought of as applying the principles of manufacturing and design to the use and disposal of electronic products (computers, printers, servers, mobile phones, and storage devices), so the environment is not impacted. The goals of green computing include the minimal use of electronic products that do not affect th…
Green Computing and Carbon Footprint Management in the IT Sectors
The focus of the current research is on making computers as energy-efficient as possible, and applying innovative ideas to energy-related computer technology. It is anticipated that green information technology (IT) will rapidly become a reality and official organizational policy. Thus, green IT is not merely restricted to environmental strategies but is concerned with the overall development of people and society as a whole. In this regard, coll…
Prediction of Land Suitability for Crop Cultivation Based on Soil and Environmental Characteristics Using Modified Recursive Feature Elimination Technique With Various Classifiers
Crop cultivation prediction is an integral part of agriculture and is primarily based on factors such as soil, environmental features like rainfall and temperature, and the quantum of fertilizer used, particularly nitrogen and phosphorus. These factors, however, vary from region to region: consequently, farmers are unable to cultivate similar crops in every region. This is where machine learning (ML) techniques step in to help find the most suita…
An Ensemble of Heterogeneous Incremental Classifiers for Assisted Reproductive Technology Outcome Prediction
Machine learning (ML) is a futuristic concept, utilized as a tool for modeling real-world applications. Today, healthcare worldwide has drawn the attention of ML, with its ability to analyze huge data sets and convert information into clinical insights that aid physicians in disease diagnosis and treatment planning, leading to low cost, better outcomes, and greater patient satisfaction. One of such medical applications calling for ML intervention…
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Green Computing and Carbon Footprint Management in the IT Sectors
The focus of the current research is on making computers as energy-efficient as possible, and applying innovative ideas to energy-related computer technology. It is anticipated that green information technology (IT) will rapidly become a reality and official organizational policy. Thus, green IT is not merely restricted to environmental strategies but is concerned with the overall development of people and society as a whole. In this regard, coll…
Prediction of Land Suitability for Crop Cultivation Based on Soil and Environmental Characteristics Using Modified Recursive Feature Elimination Technique With Various Classifiers
Crop cultivation prediction is an integral part of agriculture and is primarily based on factors such as soil, environmental features like rainfall and temperature, and the quantum of fertilizer used, particularly nitrogen and phosphorus. These factors, however, vary from region to region: consequently, farmers are unable to cultivate similar crops in every region. This is where machine learning (ML) techniques step in to help find the most suita…
An Ensemble of Heterogeneous Incremental Classifiers for Assisted Reproductive Technology Outcome Prediction
Machine learning (ML) is a futuristic concept, utilized as a tool for modeling real-world applications. Today, healthcare worldwide has drawn the attention of ML, with its ability to analyze huge data sets and convert information into clinical insights that aid physicians in disease diagnosis and treatment planning, leading to low cost, better outcomes, and greater patient satisfaction. One of such medical applications calling for ML intervention…
Dual Distance Center Loss
Center loss is widely used as a supervision tool in deep learning method. However, the center loss also has some shortcomings, the most important of which is that it must be combined with softmax loss to run well. In this article, we sum up five shortcomings of center loss and solve all of them by proposing a dual distance center loss (DDCL). Compared with center loss, DDCL can run without the combination of softmax to supervise training the mode…
Green Computing
Green computing refers to sustainable, environment-friendly computing that harnesses information and technology. Green computing can be thought of as applying the principles of manufacturing and design to the use and disposal of electronic products (computers, printers, servers, mobile phones, and storage devices), so the environment is not impacted. The goals of green computing include the minimal use of electronic products that do not affect th…
Fog Assisted Personalized Dynamic Pricing for Smartgrid
Unit electricity pricing is of vital importance in an electric grid network. It is essential to charge the customers in a fair manner. Traditional pricing models are found to be inadequate in the ability to charge customers fairly due to a lack of support for real-time communication between customers and electricity providers. With the introduction of smart devices in the electric grid domain, the real-time gathering of information is a seamless …
Machine learning algorithm-based spam detection in social networks
Generation Expansion Planning considering environmental impact and sustainable development for an Indian state using the Leap platform
RobinNet
It is essential to understand the underlying emotions that are imparted through speech in order to study social communications as well as to generate seamless human–computer interactions. Speech emotion recognition (SER) is a considerably challenging task due to the lack of sufficient data and the complex interdependence of phrases with the context and emotion they imply. This article presents RobinNet: a RoBERTa-and Inception-ResNet-V2-based nov…
A Novel Lie Hypergraph Based Lifetime Enhancement Routing Protocol for Environmental Monitoring in Wireless Sensor Networks
Wireless sensor network (WSN) is a rapid surging technology promising many fruitful innovations for the user community. The use of WSNs for continuous monitoring of environmental factors in severe situations, such as detection of volcanoes, forest fires, floods, and so on. Despite WSN’s various potential applications, energy and deployment are two major concerns influencing the network life span. The primary requirement is to monitor node energy …
An Extreme Learning Machine Technique for the Numerical Solution of Fractional Differential Equations
This research intends to create a novel approach for solving fractional differential equations (FDEs) of both linear and nonlinear types utilizing the fractional shifted Legendre neural network alongside the extreme machine learning (ML) algorithm. The neural network serves as a trial solution to formulate the loss function where it is used to train the neural network through an extreme learning machine (ELM) to obtain the solution. The new appro…
Effective Analysis of Machine and Deep Learning Methods for Diagnosing Mental Health Using Social Media Conversations
The increasing incidence of mental health issues demands innovative diagnostic methods, especially within digital communication. Traditional assessments are challenged by the sheer volume of data and the nuanced language found on social media and other text-based platforms. This study seeks to apply machine learning (ML) to interpret these digital narratives and identify patterns that signal mental health conditions. We apply natural language pro…
Designing Energy-Aware Scheduling and Task Allocation Algorithms for Online Reinforcement Learning Applications in Cloud Environments
With the rapid proliferation of machine learning applications in cloud computing environments, addressing crucial challenges concerning energy efficiency becomes pressing, including addressing the high power consumption of such workloads. In this regard, this work focuses much on the development of an energy-aware scheduling and task assignment algorithm that, while optimizing energy consumption, maintains required performance standards in deploy…
Ebpga
Automatic text summarization (ATS) deals with compressing a long document into a shorter version, retaining the key ideas of the original document. It aims to tackle the problem of information overload resulting from the continuous creation of documents. ATS helps people make news feeds, meeting reports, summarized legal and financial documents, study tools, article outlines, social media analysis, and so on. Extractive text summarization (ETS) i…
Computer Science (13 works) · Artificial Intelligence (9 works) · Machine learning (6 works) · Engineering (5 works) · Mathematics (5 works) · Business (3 works) · Economics (3 works) · Environmental economics (3 works) · Green IT and Sustainability (3 works) · IoT and Edge/Fog Computing (3 works)