Browse
Recent Submissions
Item An investigation on the role of iot in the advancement of healthcare services(UMT Lahore, 2022) GOHAR SARWAR HAMEEDSmart objects are the final foundations in the evolution of technologically intelligent frameworks thanks to the Internet of Things. The Internet of Things has a wide range of application areas, such as health care. The Internet of Things revolution is profoundly changing advanced health care, with encouraging technological, financial, and communal outcomes. This paper examines the latest network frameworks, software solutions, and market trends in Internet of Things healthcare solutions, as well as improvements in electronic health. Furthermore, from a healthcare perspective, this paper examines various IoT data security and privacy protection features, such as safety protocols, threat prevention models, and attack categorizations. Furthermore, this paper offers a smart cooperative security framework to reduce potential threats; addresses how various innovations, including data warehousing, embedded systems, and wearable electronics, can be utilized in a health care context; discusses emerging Internet of Things and electronic health rules and regulations around the globe to find how they can enhance economic growth in terms of durable development; and indicates several future research directions for Internet of Thingsbased health care, consisting of a set of problems and concerns. A host of health challenges confront the world today, all of which necessitate personal action. The most significant health challenges now are chronic conditions found in youngsters and the elderly. Children in orphanages and older residents in healthcare facilities suffer from various ailments, and basic health checks are not undertaken regularly or until immediate medical treatment is required. Their medical screening could be done more often on a daily or monthly basis if they used an IoT gadget with sensing devices. The records from the device will be sent to the doctor, who will analyze them. If a severe problem is discovered, the patient will receive the best possible care. It will also produce a medical report. This research examines the role of the Internet of vii Things in attaining this vision of the healthcare platform vision. Healthcare is one of the most valuable and important domains in which automated systems are being established and enhanced.Item Forecasting covid-19 vaccination trends using time series analysis based on hybrid harvest model(UMT Lahore, 2022) AMNA KHALILThere are numerous valuable worldwide works to develop and distribute vaccination. Vaccination is a huge region of the world’s populace, which is critical to controlling pestilence, presently faces another arrangement of boundaries, worldwide rivalry due to congestion, and antibodies. COVID-19 vaccination data using time series analysis techniques. A time series analysis conducted a COVID-19 vaccination dataset to forecast future trends. Four models were used, including ARIMA, Prophet, LSTM, and the proposed Hybrid Harvest model. Proposed Hybrid Harvest model, a combination of ARIMA and Prophet, was found to be the most accurate in terms of prediction accuracy with a RMSE of 0.0305 and an MSE of 0.1323. The LSTM model was not suitable for this type of data with a high RMSE and MSE. This study highlights the potential of combining models in time series analysis and the importance of considering multiple models in forecasting future trends. This Work provide new insights into the temporal patterns of COVID-19 vaccination data and can contribute to the development of more effective vaccination strategies. The results are limited to the specific dataset used and further research is needed in this area.Item Phosphotyrosine site prediction using deep learning(UMT Lahore, 2022) FATIMA AABIDPost-translational modification is one of the ways to enhance the genetic coding capacity to generate diversity in the respective proteomes. The phosphorylation of proteins intrigued computer scientists. Prediction of the protein site is simple but challenging to make an accurate prediction using it. Protein phosphorylation is a vital source for various cellular processes and one of the most fundamental types of a posttranslational modification. It is the addition of a phosphate group to the target residue. Proteins serine, threonine, and tyrosine are the most common phosphorylation; amongst these, tyrosine phosphorylation is the most frequent one and important for signal transduction in eukaryotic cells. One common approach is mass spectrometry. Another way for identification is the computational approach. Here, this research presents a novel computational predictor of phosphoTyrosine sites, adopting Chou’s 5-step rule. Different deep neural networks were used to train the model and then compared.Item Deep convolution neural network (pooling functions for object detection and segmentation)(UMT Lahore, 2022) MUHAMMAD DANISHDeep Convolutional Neural Networks (DCNN) delivers state-of-the-art performance in Object Detection, Classification, and Segmentation and are utilized for a range of Computer Vision applications. Pooling is an important component of many DCNN architectures. Pooling layers decrease the input's spatial size to lower the architecture's resource consumption. Commonly used pooling functions in DCNN are static and do not adapt to the feature maps that are being pooled. Investigating the impact of adaptive pooling functions in a DCNN for detection and segmentation is the goal of this work. This paper explains the theoretical foundations of DCNN, typical modifications, and prominent architectural designs. The flexible pooling algorithms Gated and Tree Pooling are then implemented as Keras layers. Gated Pooling achieves this by learning a combination of Max and Average Pooling and Tree Pooling learns pooling functions and how to combine them. Additionally, it is explained how to develop the MaskX R-CNN architecture by extending an existing Mask R-CNN implementation. This entails constructing a weight transfer function from the bounding box head to the mask head and combining mask predictions that are both class-specific and class agnostic. Following this approach, several MaskX R-CNN configurations are trained on all 80 categories of the Microsoft COCO dataset using these pooling methods. This thesis demonstrates that, when trained and tested on CIFAR-10 and CIFAR-100, utilizing these flexible pooling functions in tiny CNN results in better performance than Max Pooling. Analysis of the Mask and MaskX R-CNN models reveals that the performance of the basic Mask R-CNN implementation produces somewhat superior results. The performance of a MaskX R-CNN architecture trained and evaluated utilizing Tree Pooling within the ResNet backbone is marginally worse than the MaskX R-CNN baseline. The MaskX R-network CNN's heads that used Gated Pooling had the worst performance of all the trained models. However, the performance of the MaskX R-CNN implementations may be explained by the fact that they have around 4.5 times as many parameters as the Mask R-CNN baseline while adhering to the same training regimen. Because the MaskX R-CNN architecture is more complex, it is expected that: (1) using a prolonged iv training schedule will improve the performance of the baseline and the implementation using Tree Pooling; and (2) Tree Pooling with further training will eventually achieve a better performance than the baselines, as seen on the performance increase on CIFAR-10 and CIFAR-100.Item Blockchain-based iot devices in supply chain management(UMT Lahore, 2022) Waheed JavedRecent progress in shape of modern supply chain have evolved to complex networks. It remembers information for deals, arrangements, and distribution controls. But the previous supply chain management system faces a variety of challenges, including: lack of visibility of the upstream party (Provider) to the downstream party (Client), lack of flexibility in the face of sudden variations in demand and control of operating costs, lack of reliance on safety stakeholders, ineffective management of supply chain risks, etc. Blockchain (BC) uses in the supply chain to overcome the challenges because the growing demands of items have been increased. Internet of things (IoT) is a profoundly encouraging innovation that can help companies to observe, track, and monitor products, activities, and processes within their respective value chain networks. Research establishments and logical gatherings are ceaselessly attempting to convey answers for IoT gadgets in supply chain management. This paper speaks to an orderly writing audit by directing a review of Blockchain-based IoT advances and their present usage themes in gracefully chain the board framework. We discuss the smart devices used in this system and which device is the most appropriate in SCM. It also examines the future examination themes in blockchain-based IoT alluded to as the executive's framework production network. The essential deliberate writing audit has been consolidated by surveying research articles circulated in highly reputable—scenes someplace in the scope of 2016 and 2021. Lastly, open issues and challenges have been presented to provide the researchers with promising future directions in the domain of the IoT supply chain management system.Item Computational predictors for lysine post-translational modification sitesto prevent sars-cov-2 and iav infection.(UMT Lahore, 2022) AGHA WAFA ABBASLysine post-translational modifications (PTMs) are indispensable in manipulating protein activities as well as biological mechanisms. Notwithstanding, due to the brobdingnagian amounts of sequencing data elicited by genome-sequencing endeavors, rigorous unearthing of distinct kinds of lysine PTM substrates and PTM sites across the whole proteome poses a substantial obstacle. We utterly flabbergast to probe that a plethora of computational approaches in determining lysine PTMs have been contrived throughout current times. Ergo, here is an imperative necessity to scrutinize such approaches and amalgamate their methodologies in order to ameliorate and subsequently burgeon computational strategies for accurately recognizing lysine PTMs utilizing gargantuan volumes of sequence data. In this research, I utilize a maximum of 240 research articles.Item Resilience and cyber-security enhancement of smart grid by using machine learning(UMT Lahore, 2022) ZUNAIRA NAWAZThe evolution of the Smart Grid (SG) system enhanced the control and monitoring systems. The smart grid integrates data flows through power lines, intelligent metering, renewable and distributed energy sources, and a monitoring and control infrastructure. Because of the unprecedented complexity and heterogeneity of dynamic smart grid networks, they are more vulnerable to threats including Natural threats and Man-made threats. Resilience has become desired attribute to mitigate these threats. Threats caused disruptions such as blackouts, outages and power failure, etc. In this context, this paper presents a comprehensive literature review of natural and man-made threats and their regarding solutions as well as the evaluation and restoration techniques of resilience. In addition, the properties of the resilience metrics, qualitative and quantitative approaches are also highlighted in this review.Item SMART METER DATA ANALYSIS AND SECURITY ISSUES/SOLUTIONS BY USING MACHINE LEARNING(UMT Lahore, 2022) KHADIJA AMEENSmart metering systems are being implemented to improve grid reliability and energy efficiency whereas also improving customer service. Smart meters are gradually replacing traditional energy meters due to their numerous advantages, including faster two-way communication between electricity providers and end-users, enabling direct load control for demand response, energy savings, and so on. Fraudulent consumers, on the other hand, commit electricity theft and a variety of other cyber-attacks by modifying and reporting fake readings in order to reduce their bills illegally. Because the readings are used for grid management, these attacks not only inflict financial losses, but they also have the potential to degrade the system's functioning. In this study, a review of smart meter’s data analysis techniques and security issues was presented. Afterward, a structured overview and recommendations of security solutions via machine learning techniques were provided that are needed for security of smart meter’s data delivery and management.Item Analysis of covid 19 clinical data through machine learning techniques(UMT Lahore, 2022) Analysis of covid 19 clinical data through machine learning techniquesAnalysis of COVID-19 related data has been a hot topic of research. Many researchers have been working hard to play their part to take the research forward. In this research we have used different machine learning techniques to perform analysis on the Covid clinical data repository taken from carbon health team. The objective of this research was to train a model which can diagnose and predict result with best accuracy. This research helps to study what Covid is at early stage. The dataset used in this study corresponds to front lines ground realities. This Research helps in-patients and out-patients to know their vitals through symptoms along with Covid Test results. The data we used here required pre-processing. In the first phase we pre-processed the data and brought it in such a shape which could be used to perform different analytical operations. We selected the most important features by evaluating their scores. In the second phase we used multiple classification methods including decision tree classifier, random forest classifier, SVM, etc. for data classification. We experimented with different validation techniques like train test split, K fold cross validation, etc. Performance of the classifiers have been reported using confusion matrices. We also report other evaluation measures including accuracy, precision, and recall, etc. Finally, we are able to successfully train a classification model that has high accuracy, ROC AUC curve and Matthew correlation coefficient values. Hence the proposed method can find its application in the real-world scenarios of COVID-19 to diagnose the disease with better accuracy.Item Software measurement in mental health illness(UMT Lahore, 2022) Aliza TariqThe COVID-19 epidemic has posed a significant barrier to maintaining a sustainable strategy for mental health support clients with major mental illnesses. The study found that every sector operating the industry have required to align its business operations with the effective implementation of automated software and applications. The systematicliterature review aim was to analyze software measurement of mental health illness in terms of previous literature. This study screened out of 28 research papers out of 1076 initialsearches. We used Science Direct, IEEE Xplore, Springer Link, ACM, and Hindawi as database search engines. The research objective was to explore the needs of software applications and automation in the mental health care sector to bring efficiency to the systems. As a result, the study was able to achieve above discussed objective. The research concluded that the health care setting crucially requires the implementation of software automation.Item Image steganography(UMT Lahore, 2022) ANSA SHABBEERIn the modern era, the exploration of digital multimedia content has led to its use as a safe and secure communication medium. Steganography is a method of communicating secretly using a cover object such as image, audio, video, text, and packets whereas steganalysis is used to extract embedded data from the cover object. We explore the various image steganography techniques used by various researchers in their research. Image steganography is used to transmit encrypted secure data to a destination place without being discovered by an unauthorized user. The taxonomy of existing image steganography techniques has been described in this work. Different strategies for hiding secret data in images as well as extracting that data from carrier images have been discussed. This study first systematically reviews different image steganography techniques such as spatial domain, transform domain, spread spectrum, statistical methods, distortion technique, masking and filtering, and cover generation method, and then compares these techniques. This study intends to identify the requirements of good image steganography techniques and briefly reflects on which technique is most suitable for image steganography. We believe our study will insight more researchers and industry in many aspectsItem Empirical analysis of voice over internet protocol (voip) for 4g in pakistan(UMT Lahore, 2022) ALI SHAN AKRAMVoice over Internet Protocol techniques is used to make local, long-distance, and international voice phone calls via the Internet. We can make phone calls over the Internet with VoIP, which includes signaling, managing, and transport techniques. All telecom companies use a 4G network, and an enhanced variant of 4G that can achieve a bit rate marketed as 4G. Telephony companies came to notice the pros of sending data through IP in the twenty-first century, with increased speed, lower costs, and higher quality of calls. In this study, we provide a complete performance evaluation of VoIP traffic via experimental trials in a real 4G environment. Two stages make up the campaign. First, we define VoIP based on a dataset of over 123,152 packets of data-voice calls between fixed and mobile terminals. We empirically analyze the performance of different VoIP codecs. The network and terminals offer a variety of compensating mechanisms for dealing with jitter and packet loss. This study explores experiments that include two scenarios on 4G Networks. Node (x, y) experiment is several voice flow available that take into account a range of audio codecs and experiment is based on statistical analysis and involved the two most important parameters (jitter, RTT) respectively. We looked upon VoIP flows across several codecs in terms of quality and bandwidth usage in the second phase. The use of the maximum likelihood requirement to estimate parameters indicates both behaviors for jitter and RTT. Our study outclasses all the existing state-of-theart codecs and researchers in the telecom Industry. Aim to achieve the best codecs for VoIP communication and provide a suitable method to improve the quality of services in VoIP.Item Lora wan based smart farming system using temperature, humidity, soil moisture sensors for drip irrigation to save the water to increase the yield of corn, sugarcane, lady finger(UMT Lahore, 2022) MUHAMMAD ASIFSmart agriculture is the use of technology like (IoT) Internet of things, LoRa, sensors, location system, and robots on your farm. The ultimate goal is to increase the yield and quality of crops while minimizing theβlabor cost. In smart farming use of IoT-related technology is rapidly increasing. In this thesis, a long-range, βlow-power, βand low-cost LoRa-based wide area network in a smart agriculture platform is introduced. Development of this platform includes a LoRaWAN gateway system which can be used to increase smart farm yield, accuracy, and quality. This smart agriculture farm development includes RAK Lora wan Gateway controlled by Raspberry pi. It will be poweredβby an external battery and use a requiredβnumber of LoRa Nodes with sensors. Different types of sensors will be available at different nodes to determine Soil Moisture, Nitrogen, phosphorous, and potassium in the soil. This proposed smart farming system has been evaluated on a real farm located in Pakistan, collecting environmental data (Soil moisture, Nitrogen, Phosphorus, Potassium, PH) related to the growth of farm crops over a weekly period. Using LoRaWAN smart farming system approximately 60% water, 40% Fertilizers, and 70% power consumption are easily saved and the most important gain in yield is 25%. A web-based tool is used to visualize collected data is also presented, to validate the LoRaWAN smart farming system.Item Detection of thermophilic proteins using deep learning(UMT Lahore, 2022) MUHAMMAD SHAHRAIZ DURRANBio informatics has made significant advancements in recent years, particularly in the study of thermophiles. Neural Networks may be able to fill the inherent gaps and limitations in machine learning techniques. In proteomics and genomics, deep learning (DL) and neural networks have gained appeal due to their capacity to unearth complex correlations that are concealed in vast amounts of biological data. This study began by examining state-of-the-art statistical techniques for finding thermophiles. According to studies, machine learning techniques and a range of feature extraction techniques are used in the majority of the work done to predict the thermophiles sites. There are various issues and restrictions with these machine learning-based forecasts that required to be resolved. A possible method to improve the reliability of thermophilic protein predictions is deep learning. Chou’s five-step rule is used in this paper to include a deep learning-based method for better prediction of thermophilic proteins. the deep learning methodology uses the gated recurrent unit, long short-term memory unit, and convolutional neural network. the results show a dataset improvement with the highest accuracy at 86% using the FCN modelItem Detection of cancerlectin proteins using deep learning(UMT Lahore, 2022) FATIMA KAMRANBio informatics has made significant advancements in recent years, particularly in the study of cancer lectins. Neural Networks may be able to fill the inherent gaps and limitations in machine learning techniques. In proteomics and genomics, deep learning (DL) and neural networks have gained appeal due to their capacity to unearth complex correlations that are concealed in vast amounts of biological data. This study began by examining state-of-the-art statistical techniques for finding cancer lectins. According to studies, machine learning techniques and a range of feature extraction techniques are used in the majority of the work done to predict the cancer lectin sites. There are various issues and restrictions with these machine learning-based forecasts that required to be resolved. A possible method to improve the reliability of cancer lectin predictions is deep learning. Chou’s five-step rule is used in this paper to include a deep learning-based method for better prediction of cancer lectins. the deep learning methodology uses the gated recurrent unit, long short-term memory unit, and convolutional neural network. the results show a dataset improvement with the highest accuracy at 86% using the CNN model.Item Micro bactarieal secretion of protein using deep learning(UMT Lahore, 2022) Micro bactarieal secretion of protein using deep learningBio informatics has made significant advancements in recent years, particularly in the study of protein secretion. Neural Networks may be able to fill the inherent gaps and limitations in machine learning techniques. In proteomics and genomics, deep learning (DL) and neural networks have gained appeal due to their capacity to unearth complex correlations that are concealed in vast amounts of biological data. This study began by examining state-of-the-art statistical techniques for finding protein secretion. According to studies, machine learning techniques and a range of feature extraction techniques are used in the majority of the work done to predict the protein secretion sites. There are various issues and restrictions with these machine learning-based forecasts that required to be resolved. A possible method to improve the reliability of Micro bactarieal secretion of protein predictions is deep learning. Chou’s five-step rule is used in this paper to include a deep learningbased method for better prediction of Micro bactarieal secretion of protein. the deep learning methodology uses the gated recurrent unit, long short-term memory unit, and convolutional neural network. the results show a dataset improvement with the highest accuracy at 86% using the FCN modelItem Automated psoriasis detection using deep learning(UMT Lahore, 2022) Nagina AminPsoriasis is a chronic, non contagious skin condition that cannot be cured but its early detection can help prevent serious life threatening complications. The high visual similarity between normal skin and psoriasis has made the detection of psoriasis a very complex task. Moreover, it can be confused with different skin abnormalities like eczema, tinea corporis, lichen planus, pityriasis, dandruff, seborrheic dermatitis. Image processing using deep learning has proven better than other approaches in this context because of its automatic feature extractions with intelligent decisions and less chances of distorted features. In this paper, automated detection of psoriasis using deep learning has been proposed. To obtain good results for a small dataset transfer learning mechanism is used in which pre- trained deep learning models are applied on a dataset to obtain the required results. Firstly, different transfer learning models are applied on our data to work on the best obtained accuracy. Among them, ResNeXt gave the best output for an appropriate accuracy to detect psoriasis from healthy skin as well as other skin diseases. Secondly, we have worked on facilitating the development of an automated system which classifies psoriasis, lichen planus, eczema, seborrheic dermatitis, pityriasis, normal skin and tinea corporis diseases by applying and improving the final layers of pre-trained model. We have obtained an accuracy of 94% on test images with 2 classifiers and an output to show if the input image is classified as psoriasis or not. Finally, we have also applied the classifier on 3 classes; normal skin, psoriasis and other skin diseases, and obtained good results.Item A systematic process to develop machine readable pakistan sign language corpus(UMT Lahore, 2022) FATIMA MUBARAKAA person's thoughts and emotions can be expressed in sign language (SL) in the most natural and intuitive way in the deaf community. PSL is a form of sign language which provides information through the use of hands and other body parts. It is estimated that there are ten million deaf people in Pakistan, but only 5% of them go to school. People who are hearing impaired lack a basic understanding of language and literacy due to the lack of educational resources in Pakistan. An automated system that can overcome this problem is needed to improve communication skills among the deaf community. A PSL dictionary creation system based on Hamnonsys is presented in this thesis. PSL dictionary contains the sign gloss of a given word and Hamnonsys. A virtual keyboard is used for entering Hamnonsys for given signs. A method is also being developed to add nonmanual components to the proposed system, for instance, there is a gaze, mouth gestures, facial expressions, and body posture to consider. When the user retrieves the sign and Hamnonsys from the dictionary, they can be converted to SiGML for input to a signed avatar that will animate the sign. It is possible to retrieve the sign and Hamnonsys from the dictionary. The proposed system could be used to animate signs and for creating Pakistan sign language dictionaries.Item Identify phosphothreonine sites inside proteins by using deep learning and pseudo amino acid compositions(UMT Lahore, 2022) GHULAM ZOHRAIn prokaryotes and eukaryotes, phosphorylation considers being more significant and frequently generated post-translation modification of a protein. It possesses an essential regulatory system that is essential for numerous pathogenic and biological activities, including brain function and the process of signalling. The phosphate element is linked to the threonine sites inside the amino acids during the threonine phosphorylation procedure. Phosphorylation is a type of post-translational modification that commonly happens at numerous amino acids. The specific feature of phosphorylation is that it changes the rate at which protein degradation occurs and affects the activation or deactivation of a protein target. In Addition to all these functions, phosphorylation can move from one subcellular segment to another and tie with other proteins. Discovery and identification of phosphorylation are essential and different procedures are performed based on Vivo, vitro and mass spectrometry. But these methods are costly and time taking. Therefore, effective and reliable computing approaches are in high demand to discover phosphorylation sites inside proteins quickly. This analysis develops a new predictor based on deep neural networks (DNN) and Pseudo Amino Acid Compositions (PseAAC) that can easily learn how to represent threonine phosphorylation sites effectively and efficiently. The suggested approach constructs stochastic moments using the data that is based on context. The relative stochastic moments are integrated to build the neural networks. Different architectures of DNN such as recurrent neural network, convolutional neural network and fully connected neural networks are utilized in this analysis to learn a representation of protein sequence and classification of sequence. Recurrent Neural Networks (RNNs) are trained with standard RNN units, Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) units. Among all other deep neural networks and reported literature, a Convolutional neural network generates outstanding results. The suggested model would augment in-vivo approaches by assisting investigators in determining threonine phosphorylation quickly and precisely, allowing them to know better the role of threonine phosphorylation in many biological functions.Item Cte-ml(UMT Lahore, 2022) Anam SanaTransposable Elements (TEs) are the repeated DNA sequences that are mostly found in eukaryotic genomes. TEs can change their location within the genome and produce multiple copies of themselves throughout the genome. These sequences can produce both positive and negative effects on organisms. Many diseases are produced by TEs translocation as it cause to increase the rate of mutation, insertions, and deletions in the genome and may cause manyn cancer-related diseases. Meanwhile, the productive outcome of TEs translocation is the genetic variability and progression of genomes. For understanding their roles in genomic evolution and stability, accurate classification of TEs is needed. TEs can be classified into orders, classes, subclasses and superfamilies. Many conventional bioinformatics tools has been used for the classification of TEs but no one has achieved reliable results. In our study, we present a method for the classification of transposable elements to their orders and superfamilies level. For this, we have collected and used benchmark dataset in this study. For feature vectors, we have calculated statistical moments along with position and composition relative features. Later on, we have trained four machine learning models to classify TEs. We have conducted nine experiments to classify TEs into a deeper level of orders and superfamilies by using validation techniques, i.e. self-consistency testing, independent set testing and cross-validation testing technique. These validation techniques are applied to models to check the effectiveness and to measure performance metrics, i.e. specificity, sensitivity, accuracy and Mathew’s correlation coefficient (MCC). For self-consistency testing, CTE-DT has achieve higher results for experiments 1, 2 and 3. For experiment 1, 99.53% Acc, 99.96% Sn, 99.53% Sp and 0.99 MCC is observed. For experiment 2, 100% Acc, 100% Sn, 100% Sp and 1.0 MCC is observed. For experiment 3, 100% Acc, 100% Sn, 99.99% Sp and 0.99 MCC is observed. For independent set testing, CTE-RF has achieved higher results for experiments 4, 5 and 6. For experiment 4, 92.74% Acc, 92.08% Sn, 93.39% Sp and 0.85 MCC is observed. For experiment 5, 95.27% Acc, 98.45% Sn, 99.49% Sp and 0.93 MCC is observed. For experiment 6, 96.09% Acc, 93.01% Sn, 89.86% Sp and 0.95 MCC is observed. For cross-validation testing, CTE-RF has achieved higher results for experiments 7, 8 and 9. For experiment 7, 90.68% Acc, 89.07% Sn, 92.28% Sp and 0.81 MCC is observed. For experiment 8, 95.08% Acc, 97.25% Sn, 99.49% Sp and 0.93 MCC is observed. For experiment 9, 95.08% Acc, 97.25% Sn, 99.49% Sp and 0.93 MCC is observed. Based on validation and comparison of models, the proposed model can help in the classification of transposable elements in an efficient and accurate way