Abstract

Machine learning methods have found applications in various branches of healthcare including disease diagnosis, disease progression and outcome prediction, drug design, etc. The computational nature of such methods democratizes medical expertise by enabling access to diagnosis and outcome prediction in the absence of a medical expert. In this thesis we present two different applications of AI in health care. We present LPGNet an efficient deep learning based model for diagnosis of Parkinson’s disease from Gait. Next, We present the statistical analysis and models built for severity and mortality prediction in hospitalized Indian COVID-19 patients. In the first part of the thesis we present LPGNet an efficient method for diagnosis of Parkinson’s Disease (PD) from gait. PD is a chronic and progressive neurological disorder that results in rigidity, tremors and postural instability. There is no definite medical test to diagnose PD and diagnosis is mostly a clinical exercise. Although guidelines exist, about 10-30% of the patients are wrongly diagnosed with PD. Hence, there is a need for an accurate, unbiased and fast method for diagnosis. In this study, we propose LPGNet, a fast and accurate method to diagnose PD from gait. LPGNet uses Linear Prediction Residuals (LPR) to extract discriminating patterns from gait recordings and then uses a 1D convolution neural network with depth-wise separable convolutions to perform diagnosis. We also undertake an analysis of various cross-validation strategies used in literature in PD diagnosis from gait and find that most methods are affected by some form of data leakage between various folds which leads to unnecessarily large models and inflated performance due to overfitting. The analysis clears the path for future works in correctly evaluating their methods. LPGNet achieves an AUC of 0.91 with a 21 times speedup and about 99% lesser parameters in the model compared to the state of the art which would enable the method to be implemented in embedded systems to enable automated Parkinson’s diagnosis to be cheap and widely accessible. In the second part of the thesis we present the statistical analysis to understand and build models for severity and mortality prediction in hospitalized Indian COVID-19 patients. The clinical course of coronavirus disease 2019 (COVID-19) infection is highly variable with the vast majority recovering uneventfully but a small fraction progressing to severe disease and death. Appropriate and timely supportive care can reduce mortality and it is critical to evolve better patient risk stratification based on simple clinical data, so as to perform effective triage during strains on the healthcare infrastructure. We analyse a diverse set of 70 bio-markers including physiological, haematological and genomic parameters from 544 COVID-19 patients from New Delhi, India to identify significant markers and build risk stratification and mortality prediction models. An XGboost classifier yielded the best performance on risk stratification (AUC score of 0.83). A logistic regression model yielded the best performance on mortality prediction (AUC score of 0.92) when tested on a holdout test set of 115 patients. Examination of the data in comparison to a similar dataset with a Wuhan cohort of 375 patients was undertaken to understand the much lower mortality rates in India and the possible reasons thereof. The comparison indicated higher survival rate in the Delhi cohort even when patients had similar parameters as the Wuhan patients who died. Our analysis of medications administered points to usage of steroids early on which may have helped in the survival of severe patients. This study helps in identifying high-risk patients and provides insights into the low mortality rate in India

Abstract

Use of Immersive Virtual Reality(VR) stack is expanding, as the organizations explore alternative methods to reduce the cost of training. The effectiveness of VR simulators has demonstrated significant gains in the transfer of training for domains such as surgical training, flight training, military reconnaissance, search and rescue operations, etc. However, despite significant advancements, there exists a substantial gap to study human cognition and behavior and to also train them for demanding tasks like military combat and reconnaissance. Many of the military systems in use today are robotic with a limited degree of decision-making ability and interaction with the environment where making mistakes isn’t an option and therefore requires human intervention to operate and command remotely. Thus, teleoperators need to be instructed and trained prior to perform such operations efficiently and effectively. For a better understanding, and increasing VR’s usability, we aim to identify the relevant constituent components of immersive technologies, and differentiate their roles. In this thesis, we aim to determine the role of two determinants (goal-specific instructions and display fidelity) of spatial knowledge that one acquires during spatial navigation in VR. We conducted two experiments to disentangle the contribution of the two factors individually. Literature has demonstrated the relative importance of instructions on task performance and having goals can help in focusing attention on relevant information in the environment. However, it is also dependent on the nature of goal or activity that one is asked to accomplish. From a navigation perspective, the simplest of the goal is to return to the starting point to end the activity. In the first study, we included this goal in the instructions for one group while the other group were only instructed to explore the virtual environment and build understanding. Acquired topological spatial knowledge was assessed from the drawn sketch-maps. Our results showed that participants with goal-specific instructions found it difficult to recall one out of three spatial relationships (Object Positioning). These results indicate that the presence of additional goals in the instruction can impact the spatial memory of the operators and can be detrimental to task performance. HMD VR compared to DT, provides a 3-D experience during the virtual exploration, it offers a sense of complete immersion in the virtual world but literature has shown mixed findings depending on the applications. To study the role of the second factor, we conducted a pilot study followed by a full-scale study to examine how two common VR display modes, head-mounted display (HMD) and desktop (DT), would affect spatial learning when we restrict ambulatory locomotion in HMD. The findings and shortcomings of the pilot study helped in the refinement of experimental design, protocols, and methodology. The two display modes were compared on spatial learning, time taken to complete the virtual exploration, motion sickness, workload, sense of presence, and spatial orientation ability. Our results highlight the differential effect of DT vs. HMD VR depends on the nature of cognitive and functional behavior that the user employs. We explain these results in terms of deficient idiothetic information in non-ambulatory HMD and lesser sensory conflicts in desktop mode. This thesis sets up the foundation stones and provides future research directions that could be used in developing more robust VR experiments and applications. It is intended to be useful for VR experimenters as well as end-users. These results are relevant for developing customized and sustainable virtual reality-based human-computer interactions.

Abstract

Online learning problems are among the most abundant machine learning problems, such as advertisement selection, movie recommendation, and node or link prediction in evolving networks. In online learning, one must make online, real-time decisions and continuously improve performance with the sequential arrival of data. The multi-armed bandit (MAB) problem is one of the fundamental online learning problems that captures the classic exploration vs. exploitation dilemma. As per the applications’ requirements, new variants of MAB have been developed to tackle the problem. For example, strategic bidding in the online learning environment (MAB mechanism); availability of extra information to make the decision (Contextual MAB); selecting multiple arms instead of the single-arm (Combinatorial MAB); a varying set of available arms (Sleeping MAB), etc. We identify some of the problems and appropriately address them by proposing efficient algorithms. We provide theoretical guarantees for the performance of the proposed algorithms and showcase their efficacy through simulations. Following are the problems we address in this thesis: • Designing truthful contextual multi-armed bandits with the backdrop of sponsored search auction [3]. We tackle the drawbacks of the current state-of-the-art. • Propose an efficient algorithm for sleeping combinatorial multi-arm bandit problem when we consider general reward function (satisfying mild smoothness conditions) [2]. • We take a multi-arm bandit approach to subset selection under constraints in an unknown environment [46].

Abstract

This thesis adds to the knowledge of a chemical named entity detection and chemical role labeling. We present guidelines for chemical named entity detection in the Examples section of patents. In the process, we created the second-largest chemical NER dataset, the WEAVE corpus. We also developed methods for computational named entity recognition. We describe the models along with their features and gaps. We simultaneously extend the WEAVE corpus with chemical role labels in the Examples section of patents. Chemical role labels tend to change temporally as the reaction proceeds step by step. A chemical that has the product’s role label in the first step would have a reactant role label in the next step. This fluid nature of labeling the same chemical in a coherent reaction discourse multiple times is yet to be computationally modeled. The dataset developed in this thesis would be the first step in that direction. We evaluated the baseline performance of the WEAVE corpus using Deep Affix-base BiLSTM-CRF based neural architecture. The model achieves an F-score of 91.37%, near state-of-the-art result. We also compare the baseline performance with BioCreative-V CHEMDNER-patents corpus. It was found to be complementary in nature, hence could be used for transfer learning. In future work, we suggest how this corpus could build systems that could automatically select the chemical reaction from literature and infer the reaction rule for a noninteractive organic synthesis design system.

Abstract

Music is omnipresent and has existed since time immemorial. In general, individuals’ response to music, be it in terms of movements or listening strategies, tend to be dictated by features intrinsic to music such as rhythmic structure, genre, in addition to extrinsic factors such as mood states, socio-cultural norms, amongst others. However, at the same time, human differences in physical, psychological, and behavioural traits affect the decisions we make and the experiences we pursue in daily life. Individual differences can refer to gender, cognitive styles of thinking, personality, amongst others. In this thesis, we try to expand our understanding of how individual characteristics modulate our musical behaviour. The study and analyses presents various angles of approaching musical behaviour in an interdisciplinary manner, particularly in the areas of embodied music cognition and music consumption. We particularly investigate musical behaviour in two broader sub-domains: Active Music Engagement, which entails corporeal involvement and Passive Music Engagement, which involves listening to music. In the first part, we look at decoding individual differences via active music engagement. The paradoxical balance between universality and individuality in human motoric responsiveness to music makes it interesting to note that all the individuals’ traits are encoded in free dance movement. The study addresses this by identifying individual differences, specifically gender, cognitive styles, Big Five personality traits, and music preferences using free dance movements via our proposed Machine Learning model. We further demonstrate the robustness of the proposed model by testing its efficacy on another data set and further providing conclusive evidence about the learned models’ generalizability. The results of the study support theories of embodied music cognition and the role of bodily movement in musical experiences by demonstrating the influence of gender, personality, and music preferences on embodied responses to heard music. In the second study, which deals with passive engagement, we investigate out how individual differences, specifically personality, are associated with the kind of emotional experiences one seeks from lyrics. We chose to investigate lyrics specifically because they are under-explored in the field of music information retrieval despite them playing a crucial role in eliciting emotions. Firstly, we sought to identify emotional connotations of music based on lyrics following which we associate them with individual differences represented by personality traits. To this end, first we propose a novel deep learning architecture to identify emotional connotations of music based on lyrics, which we further compare with the existing deep learning and even traditional methods on relevant datasets and show state-of-the-art performance. Subsequently, we use this model to extract emotional preferences of individuals mined via online music streaming platforms and associate them with inherent personality traits. Our findings validate our theory that various types of emotions conveyed by songs have a unique relationship with individual traits. This study contributes to a better understanding of the relationship between broad personality dimensions and the emotional experiences one naturally seeks on online music streaming platforms. Both of our studies corroborate previous research in the field in addition to providing novel findings in both the aspect of musical behaviour being modulated by individual differences.

Abstract

Next generation wireless networks (5G and beyond) present the challenges of serving clients via channels which are not traditional point-to-point communication channels. The study of efficient high throughput communication techniques for broadcast channels, interference channels, multiple access channels, device-to-device (D2D) communication, all obtain relevance in the present wireless communication scenario. The technique of utilizing local storage (which has become quite affordable, thanks to advances in hardware design), either on the client’s device or in a nearby location, for aiding communication services on a broadcast channel was introduced formally in the landmark paper by Maddah-Ali and Niesen, under the title of Coded Caching. In this paper, it was shown that a combination of (a) carefully utilizing the local storage or cache available to individual clients, and (b) coded transmissions during the delivery phase, brings tremendous gains in the rate of delivery of information of a broadcast channel. To obtain this large gain in the rate, the coded caching schemes in the landmark paper and state of the art literature, require that the files at the server be divisible into a large number of parts (this number is called subpacketization). In fact, most schemes require the subpacketization to be growing asymptotically as exponential in some r th root of the number of users K, with r being some positive integer. On the other extreme, few schemes having subpacketization linear in K are known; however they require extremely large number of users to exist, or they offer only little gain in rate. Because of these reasons the state of the art coded caching schemes are impractical for applications, even though they are theoretically sound. The first objective of this thesis is to propose a new coded caching scheme with low subpacketization and moderate rate gains utilizing projective geometries over finite fields. The proposed scheme achieve the asymptotic subpacketization, which is subexponential in K or exponential in O((log K) 2 ) (thus improving on the root-of-K exponent) and has a small cache requirement. As a special case of our proposed scheme, we get a new linear subpacketization scheme, which has a more flexible range of parameters than existing linear subpacketization schemes. Extending our techniques, we also obtain low subpacketization schemes for other multi-receiver settings such as D2D communications and distributed computing. We show numerical comparisons of our scheme with existing popular and state-of-the-art schemes, and see that our schemes achieve lower and practically relevant levels of subpacketization in many situations at the cost of higher rate (or equivalently, a lesser caching gain). In another line of work, secretive coded caching was proposed in the literature, where the cache content and the transmissions are required to be such that each user can decode only the requested file by that user, and no information about other files. In non-secretive coded caching, it was shown in the literature that commonality among the user demands help in reducing the rate of communication. The direct adaptation of this result to secretive coded caching is not possible because of the presence of an unique key in each transmission. The second objective of this thesis is to propose an improved (reduced rate) perfect secretive coded caching scheme by exploiting commonality among the user demands. The issues addressed in this thesis are summarized as follows: • Based on the ideas from Projective geometries over finite fields, this thesis leverages the set containment and the subspace containment properties, to develop subexponential and linear subpacketization coded caching schemes for practical number of users and low cache sizes. • This thesis extends the low subpacketization coded caching scheme developed (in this thesis) for the broadcast channel to – Coded caching in D2D networks. – Distributed computing framework. vii • By studying the precise leakage properties of a modified scheme of a state of the art secretive coded caching scheme, this thesis develops an improved (reduced rate) secretive coded caching scheme by exploiting commonality of the user demands

Abstract

In this work, we present a decentralized collision avoidance and motion planning algorithm for multirobot payload transport systems (PTS) navigating through dynamic environments that has applications in various industries, assembly lines, and warehouses for transporting several payloads simultaneously by multiple PTS from one place to another. A PTS is a formation of loosely coupled non-holonomic robots that cooperatively transport a rigid or deformable payload. Each PTS has one leader and multiple followers where the followers maintain a desired orientation with respect to the leader. Each PTS navigates through an environment having several other PTS and static obstacles. The objective of each PTS is to (a) Remain in formation all the time, (b) Reach the desired destination in minimum time and with minimum deviation from the desired path, (c) Avoid collisions with other PTS and static obstacles and, (d) Navigate through narrow spaces by respecting deformability constraints of the payload. The terms PTS and formation are used interchangeably in this thesis. Real-time collision avoidance for such systems is challenging due to the deformability of formations, high dimensional multi-robot non-convex workspaces, and heterogeneity in the type of PTS. We resolve the above challenges by embedding workspaces defined by a multi-robot collision avoidance algorithm and multi-scale repulsive potential fields as constraints within a decentralized convex optimization problem. We initially address the problem for rigid formations where we assume the payloads being carried to be rigid. We propose the Formation-ORCA (FORCA) algorithm for decentralized collision avoidance and motion planning of rigid payload transport systems. FORCA is a combination of optimal reciprocal collision avoidance (ORCA) and decentralized leader-follower formation control algorithms. We later address the problem for deformable formations where the formations can be deformed based on the deformability limits of the payload being carried. We present the PotentialField-Formation-ORCA-MPC (PF-ORCA-MPC) for decentralized collision avoidance and motion planning of deformable payload transport systems and has primarily two main steps to plan the motion of each formation. First, we compute collision-free multiscale convex workspaces over a planning horizon using a combination of ORCA and repulsive potential fields. Subsequently, we compute the motion plans of formation over a horizon by proposing a novel formulation for collision avoidance, and we leverage a model predictive controller (MPC) to solve the problem. FORCA and PF-FORCA-MPC algorithms are validated through extensive simulations varying in i) number of formations, ii) configuration of formations, iii) number of static obstacles, iv) density and complexity of environments. Through a narrow corridor simulation, we show the ability of formations to deform themselves in order to squeeze in through tight spaces. Through an antipodal simulation, we show that there is roughly 10% decrease in time taken for the formations to reach their destinations when deformability could be exploited using PF-FORCA-MPC. Additionally, we show that we attain a low net average execution time of 0.025(s) per iteration, even in high-density cases with around 60 robots in the vicinity. The results validate that our solution facilitates real-time navigation of multiple formations allowing the formations to successfully reach their destinations while remaining in formation and also avoiding collisions. The proposed approach applies to holonomic robots as well, provided a holonomic version of formation controller is used. The algorithm does not make any assumptions regarding the size and number of robots in the formation. Hence, it can be incorporated in any general payload transport system of arbitrary size and configuration, as shown in the simulations. Being a decentralized method, our algorithms computationally scale well with an increase in the number of robots and formations used. We additionally conduct experiments in the gazebo simulator and present a proof of concept using real robots.

Abstract

“We, the people” is the most popular phrase from constitutions. Constitutional preambles play a significant role in establishing national identity. In spite of the fact that the number of countries including preamble as part of their constitutions has been on a rise, preambles have received scant attention in academia. Most constitutional studies either completely leave out preambles or study an individual preamble in isolated space. Also, the comparative studies of the preambles are difficult to achieve because of the diverse backgrounds nations emerge from. This thesis focuses on studying preambles across the world under a single umbrella. We propose a meta-structure for preambles to facilitate their studies that encompass spatial and temporal differences. The proposed meta-structure is built on the foundations of the elements constituting a nation-state as well as the active preambles of the world. As part of the meta-structure, we present seven categories - ‘People’, ‘State’, ‘Obligations’, ‘Aspirations’, ‘Principles’, ‘Source of Sovereignty’ and ‘Constitution Adoption Event’. One of the most important functions of a preamble is to establish the collective identity of a nation. Nations establish a politically functional identity of people prior to detailing the clauses of state functioning as part of their constitutions. We dwell deeper into the ’People’ category, which refers to the identity elements in preambles. This study presents various categories which are instrumental in establishing collective identity in preambles. Further, we study the thread connecting preamble, conflict, and identity. Our study shows that the way preambles imagine and narrate identity can turn preambles into instruments of national consolidation or arenas of conflict. Finally, we show that the meta-structure can be used to understand the impact of the preamble on external aspects. We ride on the proposed meta-structure to study the correlation between the progress of a nation and its preamble. We present a statistical study between various indicators that reflect the growth of a nation and the presence or absence of various elements in preambles. We use the study to present an analysis of the impact of meta-categories of preambles on various growth indicators in economic, social, and infrastructural domains.

Abstract

Sequence learning plays a central role in the acquisition of many daily life motor skills such as typing or playing the piano. Several canonical experimental paradigms such as the serial reaction time task, discrete sequence production task and m × n task have been proposed to study the typical behavioral phenomenon in sequencing tasks. Such paradigms are externally-specified, where the environment or the task paradigm extrinsically provides the sequence of stimuli that guides the motor actions. Such paradigms differ from a class of more realistic motor tasks that are internally-guided, where the sequence of motor actions is self-generated or internally-specified. Most previous studies on discrete sequencing have employed externally-specified paradigms and therefore, the cognitive mechanisms underlying skill learning in internally-guided sequencing paradigms remain largely unexplored. This thesis presents an empirical and computational investigation of skill learning in internally- guided sequencing. We employ the Grid-Sailing Task (GST) as a canonical paradigm to study internally- guided sequence learning. The GST requires navigating by executing sequential keypresses, a n × n grid from start to goal (SG) position while using a particular key-mapping (KM) among the three cursor- movement directions and the three keyboard buttons. In the first study, we investigate the learning processes involved in internally-guided sequencing. The participants performed two behavioral experiments – Single-SG and Mixed-SG condition. The partici- pants first completed the Single-SG condition, which required performing GST on a single SG position repeatedly. By showing performance-related improvements in various behavioral measures such as the execution time and reward score, we show that motor learning contributes to the trajectory-specific learning in GST with the repeated execution of the same keypress sequences. The Mixed-SG condition involved performing GST using the same KM (from Single-SG condition) on two novel SG positions presented in a random, inter-mixed manner. Since the participants utilize the previously learned KM, we anticipate a transfer of learning from the Single-SG condition. The acquisition and transfer of a KM-specific internal model facilitate efficient trajectory planning on novel SG conditions. The acquisi- tion of such a KM-specific internal model amounts to trajectory-independent cognitive learning in GST. We provide evidence for the role of cognitive learning in GST by showing transfer-related performance improvements in the Mixed-SG condition. In a subsequent study, we probe the involvement of a particular motor learning process called motor chunking. Motor chunking is a phenomenon which enables efficient execution of the motor sequences by chaining several elementary actions into sub-sequences called motor chunks. The participants per-formed GST on a 10 × 10 grid, executing the same trajectory repeatedly throughout the experiment. We provide empirical evidence for motor chunking by showing the emergence of subject-specific, unique temporal patterns in response times. Our findings show spontaneous chunking without pre-specified or externally guided structures while replicating the earlier results with a less constrained, internally guided sequencing paradigm. In another study, we employ an inter-manual transfer task to examine the stage-wise transitions in motor sequence learning. The participants performed GST on inter-leaved normal and transfer blocks. The dominant hand was used on the normal block and the non-dominant hand was used on the transfer block. The length of the first normal block varied across days. We found increasing differences in execution time between the normal and transfer blocks across days as the effector-dependent learning consolidated. Our findings confirm a switch from the effector-independent cognitive learning phase to the effector-dependent motor learning phase after substantial practice. We then situate internally-guided sequencing in a dual-process account of skill learning and propose computational analogues for the goal-directed and the habitual controller. We propose two hybrid rein- forcement learning frameworks that integrate model-based and model-free mechanisms to account for the dual learning processes. Using simulations and model-fitting experiments, we compare the proposed hybrid frameworks, namely, value-of-information based arbitration and weighted-hybrid arbitration. We show that weighted-hybrid arbitration describes the empirical data better than other models. Our pro- posed framework gives a computational account of the learning in internally-guided sequencing.

Abstract

The structure of vegetation changes rapidly due to various reasons influenced by both anthropogenic and natural factors, potentially resulting in degradation and deforestation. Understanding and analyzing the changes in vegetation cover is very important in several aspects including climatic changes, water budget, ecological balance and especially to undertake necessary conservation measures. Spatiotemporal mapping and monitoring of vegetation using remote sensing satellite data help in assessing the health of vegetation and imply better management practices to safeguard them. Further, the development of vegetation indices using remote sensing satellite data provides better insights about the growth patterns, seasonal changes and health conditions of the vegetation. Normalized Difference Vegetation Index (NDVI) is one of the most widely acknowledged indices for vegetation related studies. The generalized annual NDVI profile for vegetation increases with the plant growth and reaches a peak or plateau. Later the profile falls off eventually with plant death or leaf senescence / seasonal changes. Thus, the NDVI series provides a means to describe plant phenology. To interpret changes in vegetation along with the seasonal and inter-annual changes, time series of NDVI data is used in the current study. The concept of neural network has gained much significance in the analysis of vegetation dynamics using remote sensing satellite data. After exploring various approaches available for prediction of vegetation dynamics in the literature and their drawbacks this study has proposed the application of Long-short term memory (LSTM) network, a variant of Recurrent Neural Network using MODIS NDVI time series data sets to assess vegetation dynamics. In the current study various experiments were carried out to demonstrate the potentiality of LSTM network in predicting vegetation dynamics. The first experiment includes a dataset of 861 7-day composite MODIS NDVI images from January 2000 to June 2016 for making the time series. The data is segregated into three sets as the training set (70%), validation set (20%), and testing set (10%). To check the reliability of the experiment two different regions were selected after extensive research in the current investigation. These include different terrains in the Great Nicobar Island, one region along the coast where vegetation has severe ecological damage due to the 2004 Indian Ocean tsunami and the other, an interior region that remained imperturbable during the tsunami. LSTM network is trained with the NDVI values for both the regions separately to predict the future vegetation dynamics. To measure the accuracy of the LSTM network, root mean square error is calculated. The resulting plots for both the experiments indicate that the LSTM neural network follows the series in addition to coinciding with the required time series. Also, an unanticipated change in the trend of the NDVI series was well adapted by the network and was able to predict future NDVI values with good accuracy maintaining RMSE less than 0.03 without providing any supplementary data. To further investigate the robustness of the proposed prediction technique to variations in data composite periods, noise and the study area two additional experiments are executed. The results obtained by using 16-day composite MOD13Q1 datasets containing 455 images of Great Nicobar Island suggested that the composite period has no effect on the performance of LSTM network when there is fairly enough data to train the network. To analyze the influence of noise in the time series on LSTM network, three noise reduction techniques (Fast Fourier Transform, Mean Value Iteration and Whittaker smoother) which have different noise reduction levels are selected. The results have indicated that FFT based approach had the least error rate as it is best at smoothing the time series of all the three methods and MVI had high error rates. Although FFT had least error rate, the original time series was not preserved well by the approach and the series was over smoothed. MVI performance was relative to the noise levels in the original series as this approach is based on the mean of adjacent values. Whittaker smoother on the other hand was able to smooth the series reasonably well and preserve true values. The RMSE values were observed to be around 0.02 for this approach in general. It is further understood that LSTM is independent of the properties of the study area by conducting the above experiments in Uttarakhand region that has different climatic, vegetation and topological conditions from. Further, to comprehend if a complex model like LSTM is required for vegetation studies and whether LSTM performs better than the vegetation predictions techniques in practice, all the above experiments are computed with two popularly used prediction techniques Artificial Neural Networks (ANN) and Support Vector Regr