1
|
Menaka SR, Prakash M, Neelakandan S, Radhakrishnan A. A novel WGF-LN based edge driven intelligence for wearable devices in human activity recognition. Sci Rep 2023; 13:17822. [PMID: 37857665 PMCID: PMC10587088 DOI: 10.1038/s41598-023-44213-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 10/05/2023] [Indexed: 10/21/2023] Open
Abstract
Human activity recognition (HAR) is one of the key applications of health monitoring that requires continuous use of wearable devices to track daily activities. The most efficient supervised machine learning (ML)-based approaches for predicting human activity are based on a continuous stream of sensor data. Sensor data analysis for human activity recognition using conventional algorithms and deep learning (DL) models shows promising results, but evaluating their ambiguity in decision-making is still challenging. In order to solve these issues, the paper proposes a novel Wasserstein gradient flow legonet WGF-LN-based human activity recognition system. At first, the input data is pre-processed. From the pre-processed data, the features are extracted using Haar Wavelet mother- Symlet wavelet coefficient scattering feature extraction (HS-WSFE). After that, the interest features are selected from the extracted features using (Binomial Distribution integrated-Golden Eagle Optimization) BD-GEO. The important features are then post-processed using the scatter plot matrix method. Obtained post-processing features are finally given into the WGF-LN for classifying human activities. From these experiments, the results can be obtained and showed the efficacy of the proposed model.
Collapse
Affiliation(s)
- S R Menaka
- Department of Information Technology, KSR College of Engineering, Tiruchengode, India
| | - M Prakash
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - S Neelakandan
- Department of Computer Science and Engineering, R.M.K. Engineering College, Kavaraipettai, Tamil Nadu, India
| | - Arun Radhakrishnan
- Faculty of Electrical and Computer Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia.
| |
Collapse
|
2
|
Shadrach FD, Kandasamy G, Neelakandan S, Lingaiah TB. Optimal transfer learning based nutrient deficiency classification model in ridge gourd (Luffa acutangula). Sci Rep 2023; 13:14108. [PMID: 37644146 PMCID: PMC10465599 DOI: 10.1038/s41598-023-41120-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 08/22/2023] [Indexed: 08/31/2023] Open
Abstract
The efficient detection of nutrient deficiency and proper fertilizer for that deficiency becomes the critical challenges various farmers face. The family Cucurbitaceae includes members cultivated globally as a source of indigenous medicines, food, and fiber. Luffa acutangula (L.) Roxb, generally called Ridge gourd, belongs to the Cucurbitaceae family and is an annual herb originating in several areas of India, particularly in the coastal regions. Nutrient deficiency detection in ridge gourd is essential to improve crop productivity. In agricultural practises, the early identification and categorization of nutrient deficiencies in crops is essential for sustaining optimal growth and production. Addressing these nutrient deficiencies, we applied the Ring Toss Game Optimization with a Deep Transfer Learning-based Nutrient Deficiency Classification (RTGODTL-NDC) to Ridge Gourd (Luffa acutangula). This research proposes a new ring toss game optimization with a deep transfer learning-based nutrient deficiency classification (RTGODTL-NDC) method. The RTGODTL-NDC technique uses pre-processing, segmentation, feature extraction, hyperparameter tuning, and classification. The Gabor filter (GF) is mainly used for image pre-processing, and the Adam optimizer with SqueezeNet model is utilized for feature extraction. Finally, the RTGO algorithm with the deep hybrid learning (HDL) model is applied to classify nutrient deficiencies. The suggested framework has the potential to improve crop management practises by allowing for proactive and targeted interventions, which will result in improved agricultural health, production, and resource utilisation. The outcomes represented by the RTGODTL-NDC method have resulted in improved performance. For example, based on accuracy and specificity, the RTGODTL-NDC methodology rendered maximum [Formula: see text] of 97.16% and specificity of 98.29%. The outcomes show how effective the transfer learning-based model is in identifying nutrient deficits in ridge gourd plants, as seen by its high level of accuracy.
Collapse
Affiliation(s)
- Finney Daniel Shadrach
- Department of Electronics and Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, India
| | - Gunavathi Kandasamy
- Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore, India
| | - S Neelakandan
- Department of Computer Science and Engineering, R.M.K Engineering College, Tiruvallur, India
| | - T Bheema Lingaiah
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma, Ethiopia.
| |
Collapse
|
3
|
Mohan P, Neelakandan S, Mardani A, Maurya S, Arulkumar N, Thangaraj K. Eagle Strategy Arithmetic Optimisation Algorithm with Optimal Deep Convolutional Forest Based FinTech Application for Hyper-automation. ENTERP INF SYST-UK 2023. [DOI: 10.1080/17517575.2023.2188123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Affiliation(s)
- Prakash Mohan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - S. Neelakandan
- Department of Computer Science and Engineering, R.M.K Engineering College, Chennai, India
| | - Abbas Mardani
- Business School, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Sudhanshu Maurya
- School of Computing, Graphic Era Hill University, Dehradun, India
| | - N. Arulkumar
- Department of Statistics and Data Science, CHRIST (Deemed to be University), Bangalore, India
| | - K. Thangaraj
- Department of Information Technology, Sona College of Technology, Salem, India
| |
Collapse
|
4
|
Saravanan G, Neelakandan S, Ezhumalai P, Maurya S. Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing. J Cloud Comp 2023. [DOI: 10.1186/s13677-023-00401-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
AbstractCloud Computing, the efficiency of task scheduling is proportional to the effectiveness of users. The improved scheduling efficiency algorithm (also known as the improved Wild Horse Optimization, or IWHO) is proposed to address the problems of lengthy scheduling time, high-cost consumption, and high virtual machine load in cloud computing task scheduling. First, a cloud computing task scheduling and distribution model is built, with time, cost, and virtual machines as the primary factors. Second, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; to better find the optimal individual, we use the inertial weight strategy for the Improved whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence. To deliver services and access to shared resources, Cloud Computing (CC) employs a cloud service provider (CSP). In a CC context, task scheduling has a significant impact on resource utilization and overall system performance. It is a Nondeterministic Polynomial (NP)-hard problem that is solved using metaheuristic optimization techniques to improve the effectiveness of job scheduling in a CC environment. This incentive is used in this study to provide the Improved Wild Horse Optimization with Levy Flight Algorithm for Task Scheduling in cloud computing (IWHOLF-TSC) approach, which is an improved wild horse optimization with levy flight algorithm for cloud task scheduling. Task scheduling can be addressed in the cloud computing environment by utilizing some form of symmetry, which can achieve better resource optimization, such as load balancing and energy efficiency. The proposed IWHOLF-TSC technique constructs a multi-objective fitness function by reducing Makespan and maximizing resource utilization in the CC platform. The IWHOLF-TSC technique proposed combines the wild horse optimization (WHO) algorithm and the Levy flight theory (LF). The WHO algorithm is inspired by the social behaviours of wild horses. The IWHOLF-TSC approach's performance can be validated, and the results evaluated using a variety of methods. The simulation results revealed that the IWHOLF-TSC technique outperformed others in a variety of situations.
Collapse
|
5
|
Paulraj D, Sethukarasi T, Neelakandan S, Prakash M, Baburaj E. An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment. PLoS One 2023; 18:e0282600. [PMID: 36913423 PMCID: PMC10010551 DOI: 10.1371/journal.pone.0282600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/21/2023] [Indexed: 03/14/2023] Open
Abstract
Cloud computing has now evolved as an unavoidable technology in the fields of finance, education, internet business, and nearly all organisations. The cloud resources are practically accessible to cloud users over the internet to accomplish the desired task of the cloud users. The effectiveness and efficacy of cloud computing services depend on the tasks that the cloud users submit and the time taken to complete the task as well. By optimising resource allocation and utilisation, task scheduling is crucial to enhancing the effectiveness and performance of a cloud system. In this context, cloud computing offers a wide range of advantages, such as cost savings, security, flexibility, mobility, quality control, disaster recovery, automatic software upgrades, and sustainability. According to a recent research survey, more and more tech-savvy companies and industry executives are recognize and utilize the advantages of the Cloud computing. Hence, as the number of users of the Cloud increases, so did the need to regulate the resource allocation as well. However, the scheduling of jobs in the cloud necessitates a smart and fast algorithm that can discover the resources that are accessible and schedule the jobs that are requested by different users. Consequently, for better resource allocation and job scheduling, a fast, efficient, tolerable job scheduling algorithm is required. Efficient Hybrid Job Scheduling Optimization (EHJSO) utilises Cuckoo Search Optimization and Grey Wolf Job Optimization (GWO). Due to some cuckoo species' obligate brood parasitism (laying eggs in other species' nests), the Cuckoo search optimization approach was developed. Grey wolf optimization (GWO) is a population-oriented AI system inspired by grey wolf social structure and hunting strategies. Make span, computation time, fitness, iteration-based performance, and success rate were utilised to compare previous studies. Experiments show that the recommended method is superior.
Collapse
Affiliation(s)
- D Paulraj
- Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India
| | - T Sethukarasi
- Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India
| | - S Neelakandan
- Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India
| | - M Prakash
- School of Computing Science and Engineering, VIT University, Chennai, India
| | - E Baburaj
- Department of Electrical and Computer Engineering, Bule Hora University, Bule Hora, Ethiopia
| |
Collapse
|
6
|
Shanmugavadivel K, Sathishkumar VE, Raja S, Lingaiah TB, Neelakandan S, Subramanian M. Deep learning based sentiment analysis and offensive language identification on multilingual code-mixed data. Sci Rep 2022; 12:21557. [PMID: 36513786 PMCID: PMC9748029 DOI: 10.1038/s41598-022-26092-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Sentiment analysis is a process in Natural Language Processing that involves detecting and classifying emotions in texts. The emotion is focused on a specific thing, an object, an incident, or an individual. Although some tasks are concerned with detecting the existence of emotion in text, others are concerned with finding the polarities of the text, which is classified as positive, negative, or neutral. The task of determining whether a comment contains inappropriate text that affects either individual or group is called offensive language identification. The existing research has concentrated more on sentiment analysis and offensive language identification in a monolingual data set than code-mixed data. Code-mixed data is framed by combining words and phrases from two or more distinct languages in a single text. It is quite challenging to identify emotion or offensive terms in the comments since noise exists in code-mixed data. The majority of advancements in hostile language detection and sentiment analysis are made on monolingual data for languages with high resource requirements. The proposed system attempts to perform both sentiment analysis and offensive language identification for low resource code-mixed data in Tamil and English using machine learning, deep learning and pre-trained models like BERT, RoBERTa and adapter-BERT. The dataset utilized for this research work is taken from a shared task on Multi task learning DravidianLangTech@ACL2022. Another challenge addressed by this work is the extraction of semantically meaningful information from code-mixed data using word embedding. The result represents an adapter-BERT model gives a better accuracy of 65% for sentiment analysis and 79% for offensive language identification when compared with other trained models.
Collapse
Affiliation(s)
- Kogilavani Shanmugavadivel
- grid.252262.30000 0001 0613 6919Department of Artificial Intelligence, Kongu Engineering College, Perundurai, Erode, 638060 India
| | - V. E. Sathishkumar
- grid.49606.3d0000 0001 1364 9317Department of Industrial Engineering, Hanyang University, 222 Wangsimini-ro, Seongdong-gu, Seoul, 04763 Republic of Korea
| | - Sandhiya Raja
- grid.252262.30000 0001 0613 6919Department of Information Technology, Kongu Engineering College, Perundurai, Erode, 638060 India
| | - T. Bheema Lingaiah
- Departmemt of Biomedical Engineering, Jimma Institute of Technology, Jimma, Ethiopia
| | - S. Neelakandan
- grid.252262.30000 0001 0613 6919Department of Computer Science and Engineering, R.M.K Engineering College, Chennai, Tamilnadu India
| | - Malliga Subramanian
- grid.252262.30000 0001 0613 6919Department of Computer Science and Engineering, Kongu Engineering College, ERODE, 638060 India
| |
Collapse
|
7
|
Neelakandan S, Prakash M, Geetha BT, Nanda AK, Metwally AM, Santhamoorthy M, Gupta MS. Metaheuristics with Deep Transfer Learning Enabled Detection and classification model for industrial waste management. Chemosphere 2022; 308:136046. [PMID: 36007730 DOI: 10.1016/j.chemosphere.2022.136046] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/04/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
Rapid industrialization has led to the generation of a considerable amount of waste, both solid and liquid, in industrial fields like food processing, sugar, pulp, sago or starch, dairies, paper, fruit processing, poultry, distilleries, slaughterhouses, tanneries, and so forth. Despite the requirement for pollution control measures, the waste is discharged into water bodies or generally dumped on land without appropriate management, and thus becomes a significant source of environmental pollution and health hazards. The most essential step of waste management is the segregation of waste into the various elements, and normally this process is done automatically by hand-picking. A smart waste material classification technique is required to simplify the procedures. Therefore, the study presents a new Metaheuristics with Deep Transfer Learning Enabled Detection and Classification Methods for Industrial Waste Management (MDTLDC-IWM) method. The presented MDTLDC-IWM model facilitates the use of DL models for the identification and classification of waste materials in the IWM system. To accomplish this, the presented MDTLDC-IWM model follows two key phases, namely waste object recognition and waste object classification. At the initial stage, the YOLO-v5 object detector with the Harris Hawks Optimization (HHO) algorithm is used. Next, in the second stage, the stacked sparse auto encoder (SSAE) model is applied for the waste object classification method. The SSAE model is effectively optimized using the Aquila Optimization Algorithm (AOA), which helps to accomplish maximum classification of waste objects. The MDTLDC-IWM model has achieved a precision of 96.84 percent and an F score of 96.71 percent. A benchmark dataset is used to test the experimental validity of the presented MDTLDC-IWM model. Extensive comparative analysis reported the enhanced performance of the MDTLDC-IWM model over recent state-of-the-art approaches.
Collapse
Affiliation(s)
- S Neelakandan
- Department of Computer Science and Engineering, R.M.K Engineering College, Chennai, India.
| | - M Prakash
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - B T Geetha
- Department of ECE, Saveetha School of Engineering, SIMATS, Saveetha University, Chennai, India
| | - Ashok Kumar Nanda
- Department of CSE, B V Raju Institute of Technology, Narsapur, Medak, Telangana, India
| | - Ahmed Mohammed Metwally
- Department of Mathematics, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
| | | | - M Satyanarayana Gupta
- Department of Aeronautical Engineering, MLR Institute of Technology, Hyderabad, Telangana, India.
| |
Collapse
|
8
|
AI-Atroshi C, Rene Beulah J, Singamaneni KK, Pretty Diana Cyril C, Neelakandan S, Velmurugan S. Automated speech based evaluation of mild cognitive impairment and Alzheimer’s disease detection using with deep belief network model. International Journal of Healthcare Management 2022. [DOI: 10.1080/20479700.2022.2097764] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Chiai AI-Atroshi
- Department of Educational Counseling, College of Basic Education, University of Duhok, Dahuk, Iraq
| | - J. Rene Beulah
- Department of Computing Technologies, Faculty of Engineering and Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Chennai, India
| | | | - C. Pretty Diana Cyril
- Department of Computing Technologies, Faculty of Engineering and Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Chennai, India
| | - S. Neelakandan
- Department of CSE, R.M.K Engineering College, Chennai, India
| | - S. Velmurugan
- Department of CSE, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, India
| |
Collapse
|
9
|
Asha P, Natrayan L, Geetha BT, Beulah JR, Sumathy R, Varalakshmi G, Neelakandan S. IoT enabled environmental toxicology for air pollution monitoring using AI techniques. Environ Res 2022; 205:112574. [PMID: 34919959 DOI: 10.1016/j.envres.2021.112574] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 11/09/2021] [Accepted: 12/12/2021] [Indexed: 06/14/2023]
Abstract
In past decades, the industrial and technological developments have increased exponentially and accompanied by non-judicial and un-sustainable utilization of non-renewable resources. At the same time, the environmental branch of toxicology has gained significant attention in understanding the effect of toxic chemicals on human health. Environmental toxic agents cause several diseases, particularly high risk among children, pregnant women, geriatrics and clinical patients. Since air pollution affects human health and results in increased morbidity and mortality increased the toxicological studies focusing on industrial air pollution absorbed by the common people. Therefore, it is needed to design an automated Environmental Toxicology based Air Pollution Monitoring System. To resolve the limitations of traditional monitoring system and to reduce the overall cost, this paper designs an IoT enabled Environmental Toxicology for Air Pollution Monitoring using Artificial Intelligence technique (ETAPM-AIT) to improve human health. The proposed ETAPM-AIT model includes a set of IoT based sensor array to sense eight pollutants namely NH3, CO, NO2, CH4, CO2, PM2.5, temperature and humidity. The sensor array measures the pollutant level and transmits it to the cloud server via gateways for analytic process. The proposed model aims to report the status of air quality in real time by using cloud server and sends an alarm in the presence of hazardous pollutants level in the air. For the classification of air pollutants and determining air quality, Artificial Algae Algorithm (AAA) based Elman Neural Network (ENN) model is used as a classifier, which predicts the air quality in the forthcoming time stamps. The AAA is applied as a parameter tuning technique to optimally determine the parameter values of the ENN model. In-order to examine the air quality monitoring performance of the proposed ETAPM-AIT model, an extensive set of simulation analysis is performed and the results are inspected in 5, 15, 30 and 60 min of duration respectively. The experimental outcome highlights the optimal performance of the proposed ETAPM-AIT model over the recent techniques.
Collapse
Affiliation(s)
- P Asha
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, India
| | - L Natrayan
- Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, India
| | - B T Geetha
- Department of ECE, Saveetha School of Engineering, SIMATS, Saveetha University, India
| | - J Rene Beulah
- Department of CSE, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, India
| | - R Sumathy
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India
| | - G Varalakshmi
- Lecturer in Computer Science, Telangana Social Welfare Degree College for Womens, Siddipet, India
| | - S Neelakandan
- Department of CSE, R.M.K Engineering College, India.
| |
Collapse
|
10
|
Kavitha T, Mathai PP, Karthikeyan C, Ashok M, Kohar R, Avanija J, Neelakandan S. Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images. Interdiscip Sci 2021; 14:113-129. [PMID: 34338956 DOI: 10.1007/s12539-021-00467-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 07/14/2021] [Accepted: 07/23/2021] [Indexed: 02/07/2023]
Abstract
Breast cancer is a commonly occurring disease in women all over the world. Mammogram is an efficient technique used for screening and identification of abnormalities over the breast region. Earlier identification of breast cancer enhances the prognosis of patients and is mainly based on the experience of the radiologist in interpretation of mammogram with quality of image. The advent of Deep Learning (DL) and Computer Vision techniques is widely used to perform breast cancer diagnosis. This paper presents a new Optimal Multi-Level Thresholding-based Segmentation with DL enabled Capsule Network (OMLTS-DLCN) breast cancer diagnosis model utilizing digital mammograms. The OMLTS-DLCN model involves an Adaptive Fuzzy based median filtering (AFF) technique as a pre-processing step to eradicate the noise that exists in the mammogram images. Besides, Optimal Kapur's based Multilevel Thresholding with Shell Game Optimization (SGO) algorithm (OKMT-SGO) is applied for breast cancer segmentation. In addition, the proposed model involves a CapsNet based feature extractor and Back-Propagation Neural Network (BPNN) classification model is employed to detect the existence of breast cancer. The diagnostic outcomes of the presented OMLTS-DLCN technique is examined by means of benchmark Mini-MIAS dataset and DDSM dataset. The experimental values obtained highlights the superior performance of the OMLTS-DLCN model with a higher accuracy of 98.50 and 97.55% on the Mini-MIAS dataset and DDSM dataset, respectively.
Collapse
Affiliation(s)
- T Kavitha
- Department of Computer Applications, Kongu Engineering College, Perundurai, Erode, India
| | - Paul P Mathai
- Department of CSE, Federal Institute of Science and Technology (FISAT), Angamaly, Ernakulam, Kerala, India
| | - C Karthikeyan
- Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
| | - M Ashok
- Department of CSE, Rajalakshmi Institute of Technology, Chennai, India
| | - Rachna Kohar
- School of CSE, Lovely Professional University, Punjab, 144411, India
| | - J Avanija
- Department of CSE, Sree Vidyanikethan Engineering College, Tirupati, India
| | - S Neelakandan
- Department of IT, Jeppiaar Institute of Technology, Sriperumbudur, India.
| |
Collapse
|
11
|
Neelakandan S, Berlin MA, Tripathi S, Devi VB, Bhardwaj I, Arulkumar N. IoT-based traffic prediction and traffic signal control system for smart city. Soft comput 2021. [DOI: 10.1007/s00500-021-05896-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
12
|
Neelakandan S, K NJ, Kanagaraj P, Sabarathinam RM, Muthumeenal A, Nagendran A. Effect of sulfonated graphene oxide on the performance enhancement of acid–base composite membranes for direct methanol fuel cells. RSC Adv 2016. [DOI: 10.1039/c5ra27655a] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Sulfonated poly(1,4-phenylene ether ether sulfone) (SPEES)/poly(ether imide) (PEI)/sulfonated graphene oxide (SGO) based proton exchange membranes (PEMs) were prepared by a solution casting method.
Collapse
Affiliation(s)
- S. Neelakandan
- PG & Research Department of Chemistry
- Polymeric Materials Research Lab
- Alagappa Government Arts College
- Karaikudi – 630 003
- India
| | - Noel Jacob K
- Membrane Laboratory
- Department of Chemical Engineering
- ACT
- Anna University
- Chennai-600025
| | - P. Kanagaraj
- PG & Research Department of Chemistry
- Polymeric Materials Research Lab
- Alagappa Government Arts College
- Karaikudi – 630 003
- India
| | - R. M. Sabarathinam
- Functional Material Division
- Central Electrochemical Research Institute
- Karaikudi – 630 006
- India
| | - A. Muthumeenal
- PG & Research Department of Chemistry
- Polymeric Materials Research Lab
- Alagappa Government Arts College
- Karaikudi – 630 003
- India
| | - A. Nagendran
- PG & Research Department of Chemistry
- Polymeric Materials Research Lab
- Alagappa Government Arts College
- Karaikudi – 630 003
- India
| |
Collapse
|
13
|
Kanagaraj P, Neelakandan S, Nagendran A, Rana D, Matsuura T, Shalini M. Removal of BSA and HA Contaminants from Aqueous Solution Using Amphiphilic Triblock Copolymer Modified Poly(ether imide) UF Membrane and Their Fouling Behaviors. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b03290] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- P. Kanagaraj
- PG & Research Department of Chemistry, Polymeric Materials Research Lab, Alagappa Government Arts College, Karaikudi-630 003, India
| | - S. Neelakandan
- PG & Research Department of Chemistry, Polymeric Materials Research Lab, Alagappa Government Arts College, Karaikudi-630 003, India
| | - A. Nagendran
- PG & Research Department of Chemistry, Polymeric Materials Research Lab, Alagappa Government Arts College, Karaikudi-630 003, India
| | - D. Rana
- Department
of Chemical and Biological Engineering, Industrial Membrane Research Institute, University of Ottawa, 161 Louis Pasteur Street, Ottawa, ON K1N
6N5, Canada
| | - T. Matsuura
- Department
of Chemical and Biological Engineering, Industrial Membrane Research Institute, University of Ottawa, 161 Louis Pasteur Street, Ottawa, ON K1N
6N5, Canada
| | - M. Shalini
- PG & Research Department of Chemistry, Polymeric Materials Research Lab, Alagappa Government Arts College, Karaikudi-630 003, India
| |
Collapse
|
14
|
Kanagaraj P, Nagendran A, Rana D, Matsuura T, Neelakandan S, Malarvizhi K. Effects of Polyvinylpyrrolidone on the Permeation and Fouling-Resistance Properties of Polyetherimide Ultrafiltration Membranes. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b00432] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- P. Kanagaraj
- PG & Research Department of Chemistry, Polymeric Materials Research Lab, Alagappa Government Arts College, Karaikudi, 630 003, India
| | - A. Nagendran
- PG & Research Department of Chemistry, Polymeric Materials Research Lab, Alagappa Government Arts College, Karaikudi, 630 003, India
| | - D. Rana
- Department
of Chemical and Biological Engineering, Industrial Membrane Research
Institute, University of Ottawa, 161 Louis Pasteur Street, Ottawa, Ontario K1N 6N5, Canada
| | - T. Matsuura
- Department
of Chemical and Biological Engineering, Industrial Membrane Research
Institute, University of Ottawa, 161 Louis Pasteur Street, Ottawa, Ontario K1N 6N5, Canada
| | - S. Neelakandan
- PG & Research Department of Chemistry, Polymeric Materials Research Lab, Alagappa Government Arts College, Karaikudi, 630 003, India
| | - K. Malarvizhi
- PG & Research Department of Chemistry, Polymeric Materials Research Lab, Alagappa Government Arts College, Karaikudi, 630 003, India
| |
Collapse
|
15
|
Kanagaraj P, Neelakandan S, Nagendran A, Rana D, Matsuura T, Muthumeenal A. Performance studies of PEI/SPEI blend ultra-filtration membranes via surface modification using cSMM additives. RSC Adv 2015. [DOI: 10.1039/c4ra17097k] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Sulfonated poly(ether imide) and charged surface modifying macromolecules were synthesized, characterized and blended into a casting solution of poly(ether imide) in different amounts to develop surface modified ultra-filtration membranes by means of improved hydrophilicity.
Collapse
Affiliation(s)
- P. Kanagaraj
- PG & Research Department of Chemistry
- Polymeric Materials Research Lab
- Alagappa Government Arts College
- Karaikudi – 630 003
- India
| | - S. Neelakandan
- PG & Research Department of Chemistry
- Polymeric Materials Research Lab
- Alagappa Government Arts College
- Karaikudi – 630 003
- India
| | - A. Nagendran
- PG & Research Department of Chemistry
- Polymeric Materials Research Lab
- Alagappa Government Arts College
- Karaikudi – 630 003
- India
| | - D. Rana
- Department of Chemical and Biological Engineering
- Industrial Membrane Research Institute
- University of Ottawa
- Ottawa
- Canada
| | - T. Matsuura
- Department of Chemical and Biological Engineering
- Industrial Membrane Research Institute
- University of Ottawa
- Ottawa
- Canada
| | - A. Muthumeenal
- PG & Research Department of Chemistry
- Polymeric Materials Research Lab
- Alagappa Government Arts College
- Karaikudi – 630 003
- India
| |
Collapse
|