Papers by Baijnath Kaushik
Diagnostics
Alzheimer’s is one of the fast-growing diseases among people worldwide leading to brain atrophy. ... more Alzheimer’s is one of the fast-growing diseases among people worldwide leading to brain atrophy. Neuroimaging reveals extensive information about the brain’s anatomy and enables the identification of diagnostic features. Artificial intelligence (AI) in neuroimaging has the potential to significantly enhance the treatment process for Alzheimer’s disease (AD). The objective of this study is two-fold: (1) to compare existing Machine Learning (ML) algorithms for the classification of AD. (2) To propose an effective ensemble-based model for the same and to perform its comparative analysis. In this study, data from the Alzheimer’s Diseases Neuroimaging Initiative (ADNI), an online repository, is utilized for experimentation consisting of 2125 neuroimages of Alzheimer’s disease (n = 975), mild cognitive impairment (n = 538) and cognitive normal (n = 612). For classification, the framework incorporates a Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (K-NN)...
Computational Intelligent Models for Alzheimer's Prediction Using Audio Transcript Data
Computing and Informatics
Comparative evaluation of deep dense sequential and deep dense transfer learning models for suicidal emotion prediction
Concurrency and Computation: Practice and Experience
A systematic literature review for the prediction of anticancer drug response using various machine‐learning and deep‐learning techniques
Chemical Biology & Drug Design
Anti-Drug Response and Drug Side Effect Prediction Methods: A Review
Lecture notes on data engineering and communications technologies, Sep 2, 2022
IEEE Access
Alzheimer's dementia (AD) affects memory, language, and cognition and worsens over time. Therefor... more Alzheimer's dementia (AD) affects memory, language, and cognition and worsens over time. Therefore, it is critical to develop a reliable method for early detection of permanent brain atrophy and cognitive impairment. This study used clinical transcripts, a text-based adaptation of the original audio recordings of Alzheimer's patients. This audio transcript data were taken from DementiaBank, which is the largest public dataset of AD transcripts. This study aims to show how Transfer Learning-based models and swarm intelligence optimization techniques can be used to predict Alzheimer's disease. To enhance the prediction performance for Alzheimer's disease, a hybrid swarm intelligence linguistic feature selection (HSI-LFS) approach is proposed that extracts a combined feature set using Particle Swarm Optimization (PSO), Dragonfly Optimization (DO), and Grey Wolf Optimization (GWO) algorithms. In addition, a transfer learning-based model called HSI-LFS-BERT, a combination of the HSI-LFS feature selection method and Bidirectional Encoder Representations from Transformer (BERT) algorithm, is proposed. The proposed model was compared using two feature sets: the first set consisted of the initial feature set and the second set contained a hybrid feature set that was extracted using the suggested HSI-LFS method. BERT embedding with HSI-LFS outperformed the conventional feature set, providing the most accurate modeling parameters while reducing the computations by 27.19%. The proposed HSI-LFS-BERT model outperformed state-ofthe-art models, achieving 98.24% accuracy, 91.56% precision, and 98.78% recall.
DWUT-MLP: Classification of anticancer drug response using various feature selection and classification techniques
Chemometrics and Intelligent Laboratory Systems
A Simple and Fast Technique for Takri Touching Text Segmentation Using Local and Non-Local Features
SSRN Electronic Journal
Machine Learning based Dataset for Finding Suicidal Ideation on Twitter
2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 2021
Suicidal ideation is a major health issue nowadays. This may lead to death of various people. Sui... more Suicidal ideation is a major health issue nowadays. This may lead to death of various people. Suicide is also one of the major reason of death in many of the countries [9] [14]. Automatically finding people having suicidal ideation on social media is a major concern and a lot of people are working in this direction [10] [11]. There are many risk factor associated with suicidal ideation such as anxiety, depression, mental disorder etc. [13] [15]. A number of methods have been made to prevent deaths because of suicide. With the advent of social networking site, people have started expressing their feelings more on social media rather than someone in personal [6] [12]. Text classification has proven to be a successful method to prevent suicides [8]. This article describes a dataset of people having suicidal ideation on twitter. The data was extracted from an Application Programming Interface provided by Twitter. Various features/keywords related to suicidal ideation shown in table 2 were used to identify persons having such ideation. These keywords have been gathered from various web forums and previous year papers [7]. Initially the dataset have been taken from Twitter public application programming interface using its access key and access token. The raw data comprises of various fields such as: user__id, user__name, created_at, text, user__screen_name, user__friends_count, user__listed_count, user__favourites_count, user__followers_count, user__statuses_count, user__created_at, user_location with around 14202 tweets a part of which is shown in table 3. After that a sample of 1897 tweets were extracted depending upon the keywords selected and merely the text and class fields are set aside as needed to be given as input to any of the algorithms as shown in table 4. The class consists of binary values having either value 0 (non-suicidal) or 1 (suicidal) based on whether the tweet is related to suicidal ideation or not. This is done by a manual annotation by a human annotator and a psychiatric expert as shown in table 4. In the final step the preprocessing of the tweets are done based on the semantics of the keywords recognized and then based on the text fileld new colums are added to the table which contains all the keywords and the table is altered into the probabilistic values i.e. either 0 or 1. Based on the occurrence/non-occurrence of the keyword, a value 0 or 1 is assigned to each keyword and tweet in the particular record. We have given a value 1 if the specific keyword exists in that particular tweet and we have given a value 0 if a keyword doesn’t exist in the particular tweet and hence the resultant dataset consists of only binary (0 or 1) values as given in table 5 [1]. The resultant dataset consists of 1897 tweets and 34 features. A number of machine learning algorithms like Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, Voting Ensemble and AdaBoost are then used on this dataset for testing the dataset and finding the accuracy, recall and precision.
A Comprehensive Review of Nature-inspired Algorithms for Internet of Vehicles
2020 International Conference on Emerging Smart Computing and Informatics (ESCI), 2020
Internet of Vehicles is an integration of VANETs and IoT to enhance the proficiency of VANETs by ... more Internet of Vehicles is an integration of VANETs and IoT to enhance the proficiency of VANETs by incorporating smartness. Due to its numerous characteristics, it has gained lot of attention among researchers. Nature inspired algorithms are inspired from nature's strategy to cope with all day to day problems. In this review, main focus is to explore nature-inspired algorithms as these are quite beneficial in optimization. Nature-inspired algorithms are capable to deal with all kind of complex problems so in this paper, its applicability in internet of vehicles has been explored. In internet of vehicles, nature –inspired algorithms can be applied mainly in two aspects-Routing and Security. It aims to optimize all routing issues among vehicles as delay and timely information cannot be tolerated in real-time applications. On the other hand, security is of major concern in vehicular networks thus, nature inspired algorithms are able to prevent various attacks. In this paper, we have reviewed both routing and security applications of nature-inspired algorithms.
A Comparative Study and Implementation of Neuro-Fuzzy and Decision Tree for Malignant Tumor Detection System
International Journal of Advanced Intelligence Paradigms, 2019
Suicidal Ideation from the Perspective of Social and Opinion Mining
Lecture Notes in Electrical Engineering, 2019
Social media is a way of communicating with others and its popularity is growing worldwide. It ha... more Social media is a way of communicating with others and its popularity is growing worldwide. It has a lot of influence on its users. People read various posts and get affected by it. Suicide is one of the major health issues on social media which influence others to do the same. The number of suicides is increasing day by day. Thus, a need arises to find or develop a way to control suicides through social media. Machine learning is being widely used by many researchers for this purpose, with the help of psychiatrists. A lot of studies have been done in this field. In this paper, we have reviewed the existing work in this field inferring their limitations so that further work can be carried out.
Journal of Artificial Intelligence, 2018
IEEE Access
Alzheimer's disease (AD) is caused by cortical degeneration leading to memory loss and dementia. ... more Alzheimer's disease (AD) is caused by cortical degeneration leading to memory loss and dementia. A possible criterion for the early identification of Alzheimer's dementia is to identify the difference between positive and negative linguistic and cognitive abilities of the patients. This study involves the use of Convolutional Neural Network (CNN), designed a hybrid model with CNN & Bidirectional Long-Short Term Memory (Bidirectional LSTM), and proposed a Stacked Deep Dense Neural Network (SDDNN) model for text classification and prediction of Alzheimer's dementia. These models were trained end-to-end using DementiaBank clinical transcript dataset. The transcripts consisted of recorded interviews of Alzheimer's patients with clinical experts. The models were investigated under two settings: Randomly initialized and Glove embedding. Further, hyperparameter optimization was accomplished using GridSearch, which yielded optimal parameters for the design of suitable learning models for most accurate predictions. Other parameters were computed and compared based on AUC, accuracy, specificity, precision, F1 score, and recall. To ensure performance generalization, the classification accuracy was tested using 10-fold cross-validation approach. The performance and classification accuracy of the proposed model was significantly improved to 93.31% when applied with Glove embedding and hyperparameter tuning. This research work will considerably help the clinical experts in early detection and diagnosis of AD. INDEX TERMS Dementia, audio transcript data, deep learning, convolutional neural network, Bi-LSTM, stacked deep dense neural network.
IEEE Access
Alzheimer's disease (AD) is caused by cortical degeneration leading to memory loss and dementia. ... more Alzheimer's disease (AD) is caused by cortical degeneration leading to memory loss and dementia. A possible criterion for the early identification of Alzheimer's dementia is to identify the difference between positive and negative linguistic and cognitive abilities of the patients. This study involves the use of Convolutional Neural Network (CNN), designed a hybrid model with CNN & Bidirectional Long-Short Term Memory (Bidirectional LSTM), and proposed a Stacked Deep Dense Neural Network (SDDNN) model for text classification and prediction of Alzheimer's dementia. These models were trained end-to-end using DementiaBank clinical transcript dataset. The transcripts consisted of recorded interviews of Alzheimer's patients with clinical experts. The models were investigated under two settings: Randomly initialized and Glove embedding. Further, hyperparameter optimization was accomplished using GridSearch, which yielded optimal parameters for the design of suitable learning models for most accurate predictions. Other parameters were computed and compared based on AUC, accuracy, specificity, precision, F1 score, and recall. To ensure performance generalization, the classification accuracy was tested using 10-fold cross-validation approach. The performance and classification accuracy of the proposed model was significantly improved to 93.31% when applied with Glove embedding and hyperparameter tuning. This research work will considerably help the clinical experts in early detection and diagnosis of AD.
Computer Vision Technique for Neuro-image Analysis in Neurodegenerative Diseases:A survey
2020 International Conference on Emerging Smart Computing and Informatics (ESCI)
Computer vision is a set of sophisticated algorithm meant to enable machines to infer important i... more Computer vision is a set of sophisticated algorithm meant to enable machines to infer important information from high dimensional digital images and videos. It enables machine to analyze the hidden patterns and behavior in the high dimensional data more accurately than humans to extract useful information for better and accurate classification of data. Medical image data like neuro-imaging captured by different imaging modalities like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET) and Single Photo Emission Computed Tomography (SPECT) is a high resolution data. Neuro-image classification addresses the problem of detection, prediction and diagnosis of various neuro-degenerative diseases like Alzheimer's disease (AD), Parkinson's disease (PD) and other forms of dementia. Neurodegeneration is progressive deterioration of neuronal cells of human brain leading to devastating consequences characterized by brain atrophy. Therefore, early and accurate detection of neurodegenerative diseases(ND) like Alzheimer's disease is important for better treatment outcomes for the patients having neurodegenerative diseases. In this article we are discussing the application of computer vision in the detection and diagnosis of neurodegenerative diseases.
Transfer Learning-Assisted Prognosis of Alzheimer's Disease and Mild Cognitive Impairment Using Structural-MRI
2022 10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing (ICETET-SIP-22)
Computers, Materials & Continua, 2022
Even though several advances have been made in recent years, handwritten script recognition is st... more Even though several advances have been made in recent years, handwritten script recognition is still a challenging task in the pattern recognition domain. This field has gained much interest lately due to its diverse application potentials. Nowadays, different methods are available for automatic script recognition. Among most of the reported script recognition techniques, deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms. However, the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error, which renders them unfeasible. This approach often requires manual intervention with domain expertise which consumes substantial time and computational resources. To alleviate this shortcoming, this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization (QPSO), which is capable of automatically evolving the meaningful convolutional neural network (CNN) topologies. The computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts, namely Bangla, Devanagari, and Dogri, consisting of handwritten characters and digits. Empirically, the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.
Handwritten Indic scripts recognition using neuro-evolutionary adaptive PSO based convolutional neural networks
Sādhanā, 2022
Although offline handwritten Indic script recognition has been explored for decades, it is still ... more Although offline handwritten Indic script recognition has been explored for decades, it is still a challenging task to recognize handwritten characters and digits accurately because of complex structure and similar shaped characters in Indic scripts. Like other computer vision problems, handwritten Indic scripts recognition has achieved impressive state-of-the-art results using deep learning-based techniques. However, designing a successful handcrafted Deep Neural Network (DNN) right from scratch requires a lot of problem domain knowledge and involves a significant amount of trial and error. This approach intuitively appears to consume substantial time and computational resources. To solve this problem, we simplified the search process by using a meta-heuristics evolutionary technique to automatically evolve the optimal Convolutional Neural Network (CNN) architecture. More specifically, this work proposes a novel framework based on improved and fast converging Adaptive Particle Swarm Optimization (APSO) to design CNN architecture without manual intervention. The computational experiments are subsequently carried out on eight handwritten isolated characters and digits datasets belonging to three popular Indic scripts, namely Bangla, Devanagari, and Dogri. The experimental results clearly show that the proposed APSO-CNN technique yields better performance than the state-of-the-art methods for all the datasets.
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Papers by Baijnath Kaushik