Balajee Maram
@chitkarauniversity.edu.in
Associate Professor, CSE
Chitkara University
RESEARCH INTERESTS
Cryptography, Machine Learning, Deep Learning
Scopus Publications
Scopus Publications
Venkatesh Bhandage, Mallikharjuna Rao K, Satish Muppidi, and Balajee Maram
Elsevier BV
Ramachandro Majji, Balajee Maram, and R. Rajeswari
Elsevier BV
G. Nirmalapriya, Balajee Maram, Ramanathan Lakshmanan, and M. Navaneethakrishnan
Elsevier BV
Vimala Shanmugam, Telu Venkata Madhusudhana Rao, Hanumantu Joga Rao, and Balajee Maram
Wiley
Rice is the major crop in India. Early prevention and timely identification of plant leaf diseases are important for increasing production. Hence, an effective sunflower earthworm algorithm and student psychology based optimization (SEWA‐SPBO) based deep maxout network is developed to classify different types of diseases in rice plant leaf. The SEWA is the combination of sunflower optimization (SFO) and earthworm algorithm (EWA). Initially, the network nodes simulated in the environment capture the plant leaf images and are routed to the sink node for disease classification. After receiving the plant images at the sink node, the image is preprocessed using a Gaussian filter. Next to preprocessing, segmentation using the black hole entropic fuzzy clustering (BHEFC) mechanism is performed. Then, data augmentation is applied to segmented image results and disease classification is done by a deep maxout network. The training of the deep maxout network is done using the proposed SEWA‐SPBO algorithm. The proposed method detects the leaf disease more accurately with limited time and shows higher accuracy. Moreover, the proposed method attains higher performance with metrics, like accuracy, sensitivity, and specificity as 93.626%, 94.626%, and 90.431%, respectively.
S. Vimala, T. V. Madhusudhana Rao, A. Balaji, and Balajee Maram
Springer Science and Business Media LLC
Pendela Kanchanamala, Ramanathan Lakshmanan, B. Muthu Kumar, and Balajee Maram
World Scientific Pub Co Pte Ltd
Globally, traffic accidents are of main concern because of more death rates and economic losses every year. Thus, road accident severity is the most important issue of concern, mainly in the undeveloped countries. Generally, traffic accidents result in severe human fatalities and large economic losses in real-world circumstances. Moreover, appropriate, precise prediction of traffic accidents has a high probability with regard to safeguarding public security as well as decreasing economic losses. Hence, the conventional accident prediction techniques are usually devised with statistical evaluations, which identify and evaluate the fundamental relationships among human variability, environmental aspects, traffic accidents and road geometry. However, the conventional approaches have major restrictions based on the assumptions regarding function kind and data distribution. In this paper, Aquila Anti-Coronavirus Optimization-based Deep Long Short-Term Memory (AACO-based Deep LSTM) is developed for road accident severity detection. Spearman’s rank correlation coefficient and Deep Recurrent Neural Network (DRNN) are utilized for the feature fusion process. Data augmentation method is carried out to improve the detection performance. Deep LSTM detects the road accident and its severity, where Deep LSTM is trained by the designed AACO algorithm for better performance. The developed AACO-based Deep LSTM model outperformed other existing methods with the Mean Square Error (MSE), Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of 0.0145, 0.1204 and 0.075%, respectively.
Emil Selvan Gnanasigamani Samuel Raj, Issac Diana Jeba Jingle, Balajee Maram, and John Patrick Ananth
Wiley
The Internet of Things (IoT) has tremendously spread worldwide, and it influenced the world through easy connectivity, interoperability, and interconnectivity using IoT devices. Numerous techniques have been developed using IoT‐enabled health care systems for cancer detection, but some limitations exist in transmitting the health data to the cloud. The limitations can be accomplished using the proposed chronological‐based social optimization algorithm (CBSOA) that effectively transmits the patient's health data using IoT network, thereby detecting lung cancer in an effective way. Initially, nodes in the IoT network are simulated such that patient's health data are collected, and for transmission of such data, routing is performed in order to transmit the health data from source to destination through a gateway based on cloud service using CBSOA. The fitness is newly modeled by assuming the factors like energy, distance, trust, delay, and link quality. Finally, lung cancer detection is carried out at the destination point. At the destination point, the acquired input data is fed to preprocessing phase to make the data acceptable for further mechanism using data normalization. Once the feature selection is done using Canberra distance, then the lung cancer detection is performed using shepard convolutional neural network (ShCNN). The process of routing as well as training of ShCNN is performed using the CBSOA algorithm, which is devised by the inclusion of the chronological concept into the social optimization algorithm. The proposed approach has achieved a maximum accuracy of 0.940, maximum sensitivity of 0.941, maximum specificity of 0.928, and minimum energy of 0.452.
Balajee Maram, Jitendra Sahukari, and Tandra Lokesh
Springer Nature Singapore
Veerraju Gampala, Balajee Maram, and A. Suja Alphonse
Springer Nature Singapore
V. Srinadh, Balajee Maram, and T. Daniya
Springer Nature Singapore
Santhoshini Sahu, Balajee Maram, Veerraju Gampala, and T. Daniya
Springer Nature Singapore
R. Rajeswari, Veerraju Gampala, Balajee Maram, and R. Cristin
Springer Science and Business Media LLC
Muthuperumal Periyaperumal Ramkumar, Pauliah David Mano Paul, Balajee Maram, and John Patrick Ananth
Wiley
The Internet of Things (IoT) has appreciably influenced the technology world in the context of interconnectivity, interoperability, and connectivity using smart objects, connected sensors, devices, data, and appliances. The IoT technology has mainly impacted the global economy, and it extends from industry to different application scenarios, like the healthcare system. This research designed anti‐corona virus‐Henry gas solubility optimization‐based deep maxout network (ACV‐HGSO based deep maxout network) for lung cancer detection with medical data in a smart IoT environment. The proposed algorithm ACV‐HGSO is designed by incorporating anti‐corona virus optimization (ACVO) and Henry gas solubility optimization (HGSO). The nodes simulated in the smart IoT framework can transfer the patient medical information to sink through optimal routing in such a way that the best path is selected using a multi‐objective fractional artificial bee colony algorithm with the help of fitness measure. The routing process is deployed for transferring the medical data collected from the nodes to the sink, where detection of disease is done using the proposed method. The noise exists in medical data is removed and processed effectively for increasing the detection performance. The dimension‐reduced features are more probable in reducing the complexity issues. The created approach achieves improved testing accuracy, sensitivity, and specificity as 0.910, 0.914, and 0.912, respectively.
Telagarapu Prabhakar, Subbiah Vairamuthu, Balasubramaniam Selva Rani, and Balajee Maram
Wiley
Oncology treatment accuracy relies on providing information from a variety of sources to have a accurate assessment of a patient's health status and prediction. With the advancement in medical field, accurate prediction allows prescription of more effective treatments and customized medical services to individual patient's. Next generation sequencing has put pressure on cancer researchers in recent years by giving doctors access to vast amounts of data from RNA‐seq high‐throughput fields. Effectual survival prediction can save patient's life from threatening at earlier stage. In addition, traditional techniques of gene expression datasets failed to trade off balance among huge genes and low number of samples available, thereby resulting low level of survival prediction rate. Therefore, this research proposes an efficient model for survival prediction of cancer patients using proposed gray wolf‐student psychology optimization‐based deep long short term memory (GW‐SPO based deep LSTM). The proposed GW‐SPO is derived by incorporating gray wolf optimization (GWO) and student psychology based optimization (SPBO). However, survival prediction is performed effectively using deep LSTM and network classifier is trained using proposed GW‐SPO. Nevertheless, proposed GW‐SPO has achieved superior results with minimum RMSE of 0.325, and minimum prediction error of 0.110 for analysis with cluster size of 5.
P. Chinniah, Balajee Maram, P. Velrajkumar, and Ch. Vidyadhari
World Scientific Pub Co Pte Ltd
Lung cancer is an aggressive disease among all cancer-based diseases, because of causing huge mortality in humans. Thus, earlier discovery is a basic task for diagnosing lung cancer and it helps increase the survival rate. Computed tomography (CT) is a powerful imaging technique used to discover lung cancer. However, it is time-consuming for examining each CT image. This paper develops an optimized deep model for classifying the lung nodules. Here, the pre-processing is done using Region of Interest (ROI) extraction and adaptive Wiener filter. The segmentation is done using the DeepJoint model wherein distance is computed with a congruence coefficient for extracting the segments. The nodule identification is done by a grid-based scheme. The features such as Global Binary Pattern (GBP), Texton features, statistical features, perimeter and area, barycenter difference, number of slices, short axis and long axis and volume are considered. The lung nodule classification is done to classify part solid, solid nodules and ground-glass opacity (GGO) using Deep Residual Network (DRN), which is trained by the proposed Shuffled Shepard Sine–Cosine Algorithm (SSSCA). The developed SSSCA is generated by the integration of the Sine–Cosine Algorithm (SCA) and Shuffled Shepard Optimization Algorithm (SSOA). The proposed SSSCA-based DRN outperformed with the highest testing accuracy of 92.5%, sensitivity of 93.2%, specificity of 83.7% and [Formula: see text]-score of 81.5%.
R. Rajeswari, R. Ganeshan, Balajee Maram, and R. Cristin
World Scientific Pub Co Pte Ltd
The brain tumor is the most serious cancer among people of all ages, and recognition of its grade is a complex task for monitoring health. In addition, the earlier detection and classification of tumors into a particular grade are imperative for diagnosing the tumor effectively. This paper devises a novel method for multigrade tumor classification using deep architecture. First, the pre-processing is performed with the Region of interest (ROI) and Type 2 Fuzzy and Cuckoo Search (T2FCS) filter. After that, segmentation using a pre-processed image is carried out to generate segments, which is performed using a deep fuzzy clustering model. Then, the significant features are mined through segments that involve convolution neural network (CNN) features, Texton features, EMD features, and statistical features such as mean, variance, kurtosis, and entropy. The obtained features are subjected to Deep Residual Network for multigrade tumor classification. The Deep Residual Network training is done with the proposed Harmony search-based Feedback Artificial Tree (HSFAT) algorithm. The proposed HSFAT is devised by combining Harmony search and Feedback Artificial Tree (FAT) algorithm. The proposed HSFAT-based deep residual network provided superior performance with maximum accuracy of 94.33%, maximum sensitivity of 97.27%, and maximum specificity of 92.61%.
Veerraju Gampala, Balajee Maram, S. Vigneshwari, and R. Cristin
Hindawi Limited
Glaucoma represents dangerous ailment, which affected the nervous model and causes loss of vision. Several researchers developed automated discovery of glaucoma, but redundancy elimination is still challenging. Hence, this research study introduces an effective method for detecting glaucoma with deep neurofuzzy network (DNFN). Initially, the retinal image is input for preprocessing to remove the noises. Then, the optic disc (OD) detection and blood vessel segmentation are employed using the blackhole entropy fuzzy clustering algorithm and the DeepJoint model, respectively. Finally, the obtained OD and blood vessels are fed to the DNFN, wherein DNFN training is performed with newly devised MultiVerse Rider Wave Optimization (MVRWO). The newly developed MVRWO integrates the Water Wave Optimization, Rider Optimization Algorithm, and MultiVerse Optimizer. Finally, the output is classified based on the loss function of the DNFN. The developed MVRWO–DNFN obtained an elevated accuracy of 92.214%, a sensitivity of 93.422%, and a specificity of 92.34%.
Ganapathi Nalinipriya, Maram Balajee, Chittibabu Priya, and Cristin Rajan
Wiley
The emergence of networking systems and quick deployment of applications cause huge increase in cybercrimes which involves various applications like phishing, hacking, and malware propagation. However, the Ransomware techniques utilize certain device which may lead to undesirable properties which might shrink the paying‐victim pool. This paper devises a new method, namely Water Moth Flame optimization (WMFO) and deep recurrent neural network (Deep RNN) for determining Ransomware. Here, Deep RNN training is done with WMFO, and is developed by combining Moth Flame optimization (MFO) and Water wave optimization (WWO). Moreover, features are mined with opcodes and by finding term frequency‐inverse document frequency (TF‐IDF) amongst individual features. Moreover, Probabilistic Principal Component Analysis (PPCA) is adapted to choose significant features. These features are adapted in Deep RNN for classification, wherein the proposed WMFO is employed to produce optimum weights. The WMFO offered enhanced performance with elevated accuracy of 95.025%, sensitivity of 95%, and specificity of 96%.
J. Granty Regina Elwin, Jyothi Mandala, Balajee Maram, and R. Ramesh Kumar
Elsevier BV
Balajee Maram, Jyothi Mandala, and Aravapalli Rama Satish
Wiley
Intrusion detection system (IDS) is a robust model that plays an essential role in dealing with intrusion detection, especially in detecting abnormal anomalies and unknown attacks. The major challenges faced by IDS are the computation time required for analysis, and the exchange of a huge amount of data from one division of the network to another. For the sake of tackling such limitations, this probe proposes a multi‐agent enabled IDS for detecting intrusions using the Bat Sea Lion Optimization (BSLnO) algorithm. The proposed strategy consists of five phases, namely pre‐processor agent, reducer agent, augmentation agent, classifier agent, and decision agent. Initially, input data is subjected to pre‐processor agent, where pre‐processing is carried out using data normalization and missing value imputation. Thereafter, the pre‐processed result is fed up to the reducer agent, where dimension reduction is carried out using mutual information. The third step is data augmentation in which the dimensionality of data is enhanced. After that, the augmented result is subjected to classifier agent to classify intrusions or malicious activities present in the network based on hybrid deep learning strategies, namely deep maxout network and deep residual network. A developed BSLnO is implemented by incorporating Bat Algorithm (BA) and Sea Lion Optimization (SLnO) algorithm to train the hybrid classifier. The proposed scheme has acquired a higher precision of 0.936, recall of 0.904, and F‐measure of 0.920.
G. Neelima, Dhanunjaya Rao Chigurukota, Balajee Maram, and B. Girirajan
Elsevier BV
Geetha Mariappan, Aravapalli Rama Satish, P. V. Bhaskar Reddy, and Balajee Maram
Wiley
The emergence of photo editing applications, like Adobe Photoshop, has manipulated the operation of digital images into a simple task. However, these manipulations of images misrepresent the content of the original image for misleading the public. Various copy move forgery detection techniques are developed, but these show less robustness on the image with noise and blurring. This article develops an optimization‐driven deep learning technique for image forgery detection. The purpose is to develop a copy‐move image forgery detection technique using a deep neuro‐fuzzy network and a newly developed optimization algorithm. Here, adaptive partitioning is adapted using a rectangular search for splitting the image into different parts. In addition, the features like local Gabor XOR pattern and Texton features are extracted from the partition. Furthermore, the forgery is detected using the deep neuro‐fuzzy network. Finally, the deep neuro‐fuzzy network training is performed using the proposed multi‐verse invasive weed optimization (MVIWO) technique. The proposed MVIWO method will be newly designed by integrating the multi‐verse optimizer and invasive weed optimization technique. Thus, the copy‐move image forgery detection is effectively performed using the proposed MVIWO‐based deep neuro‐fuzzy network. The developed MVIWO‐based deep neuro‐fuzzy network offers superior performance with the highest specificity of 93.54%, highest accuracy of 94.01%, and highest sensitivity of 97.75%.
V. Agalya, Manivel Kandasamy, Ellappan Venugopal, and Balajee Maram
World Scientific Pub Co Pte Ltd
A brain tumor is a collection of irregular and needless cell development in the brain region, and it is considered a life-threatening disease. Therefore, early level segmentation and brain tumor detection with Magnetic Resonance Imaging (MRI) is more important to save the patient’s life. Moreover, MRI is more effective in identifying patients with brain tumors since the recognition of this modality is moderately larger than considering other imaging modalities. The classification of brain tumors is the most important, difficult task in medical imaging systems because of size, appearance and shape variations. In this paper, Competitive Poor and Rich Optimization (CPRO)-based Deep Quantum Neural Network (Deep QNN) is proposed for brain tumor classification. Additionally, the pre-processing process assists in eradicating noises and uses image intensity to eliminate the artifacts. The significant features are extracted from pre-processed image to perform a productive classification process. The Deep QNN classifier is employed for classifying the brain tumor regions. Besides, the Deep QNN classifier is trained by the developed CPRO approach, which is newly designed by integrating Poor and Rich Optimization (PRO) and Competitive Swarm Optimizer (CSO). The developed brain tumor detection model outperformed other existing models with accuracy, sensitivity and specificity of 94.44%, 97.60% and 93.78%.
R. Rajeswari, G. Neelima, Balajee Maram, and Anupama Angadi
World Scientific Pub Co Pte Ltd
Brain tumor is a severe nervous disorder that causes damage to health and often leads to death. Therefore, it is significant to classify the brain tumor at an early stage as it increases the survival rate of patients. One of the commonly employed imaging modalities for brain tumor classification is Magnetic Resonance Imaging (MRI). However, it is relatively complex to perform the brain tumor classification process due to the variations of type, shape, size and tumor location. To overcome such issues and classify the tumor more accurately, a deep learning classifier named Deep Maxout network is developed to classify the tumor into different grades. Based on the classification result, the features connected with the tumor grades are effectively acquired to make the survival prediction process. Deep learning is an effective and robust classifier model employed to perform the tumor classification or detection process with the MRI modality. Here, the survival prediction of tumor patients is carried out by the Deep Long Short-Term Memory (LSTM) classifier. Accordingly, the proposed method achieved higher performance using accuracy, sensitivity, specificity and prediction error with the values of 0.9434, 0.9324, 0.9202 and 0.0579.
Dr V Vasudha Rani, G. Vasavi, and Balajee Maram
IEEE
Skin is an extraordinary human structure. As a result of inherited traits and environmental variables, skin conditions are the most prevalent worldwide. People frequently neglect the effects of skin diseases in their initial stages. It commonly experienced both well-known and rare diseases. Identifying skin diseases and their kinds in the medical field is a very difficult process. It can be very challenging to identify the precise type of disease because of the intricacy of human skin complexion as well as the visual proximity effect of the conditions. As a result, it's critical to identify and categorize skin diseases as soon as they are discovered. The most ambiguous and challenging field in science is therefore the detection of human skin diseases. For segmentation and diagnosis, ML techniques are frequently employed in the biomedical industry. These techniques decide using features extracted from photos as their input. To obtain high classification accuracy, it is crucial to select appropriate feature extraction techniques along with appropriate Machine Learning (ML) approaches. The classification of skin diseases is discussed in this analysis using ensemble data mining approaches and ML algorithms. In this method, four distinct ML techniques are used to categorize the various kinds of diseases while ensemble approaches are used to increase the classification reliability of skin diseases.