Maximum Likelihood Algorithm Research Articles

Molecular Characterization and Phylogenetic Analysis of Spirometra Tapeworms from Snakes in Hunan Province.

Sparganosis is a neglected zoonotic parasitic disease that poses huge threats to humans worldwide. Snakes play an important role in sparganosis transmission because they are the most common second intermediate hosts for Spirometra parasites. However, the population genetics of Spirometra isolates from snakes is currently not well studied in China. The present study was performed to explore the molecular characteristics and phylogenetic analysis of Spirometra tapeworms from different species of snakes in Hunan Province. This study obtained 49 Spirometra isolates from 15 geographical areas in Hunan Province, Central China. Subsequently, the 18S and 28S ribosomal DNA (rDNA) fragments were amplified from the isolated parasites, and their sequences were analyzed to assess their genetic diversity. Phylogenetic analyses were performed using the maximum likelihood algorithm. The results showed that sequence variations among these isolates were 0–2.3% and 0–0.1% for 18S and 28S rDNA, respectively. The phylogenetic analysis showed that all Spirometra isolates from Hunan Province were clustered into the same branch with Spirometra erinaceieuropaei isolated from other areas (China, Vietnam, Australia). Moreover, the phylogenetic trees revealed that Spirometra is closely related to Adenocephalus, Pyramicocephalus, Ligula, Dibothriocephalus, Schistocephalus, and Diphyllobothrium. The Spirometra isolates of different hosts/regions in Hunan Province are not host segregated or geographically isolated, and support for the taxonomic status of Spirometra tapeworms in China has been added. These results provide reference values for future accurate identification and taxonomic status of Spirometra tapeworms in China.

A Proposed Methodology for Detecting the Urban Footprint in Egypt

The detection of informal built-up environments that sprawl on agricultural land is difficult to codify specially in particular with the increase in the population in the Arab Republic of Egypt. In 2017, a law was passed to remove building infractions in rural areas according to aerial photographs taken by the military and the building codes built after the 2019 Construction Violations Reconciliation Law (CVRL) were regulated, which mandates the demolition of any buildings built without a permit that are capped at 8.2 million units Since 2007. The use of remote sensing, which is a powerful technology that relies specifically on updated satellite images, is a key tool for detecting infringements built-up regions. The European Space Agency’s Sentinel Synthetic Aperture Radar (SAR) constellation is a key component of this research because of its advantages that allow for improved spatial resolution and atmospheric independence, and it takes day and night images, making it useful for a wide range of ground cover detection applications., a technological advantage that surpasses Optical sensors for the Sentinel 2 and Landsat missions that rely on clear weather conditions. The study aims to evaluate the use of radar satellites compared to optical satellites to detect urban built-up areas and encroachments on agricultural areas. The study was conducted on an area of 64 square kilometres, in Damietta City, East Delta, Egypt. Supervised classification was also used to derive the overall accuracy of radar satellite images and compare them with optical satellite images, using Error Matrix tables on Scup + QGIS programs. The results of the study resulted in reaching a high accuracy of land cover classification from the process of combining the Sentinel-1A,1B images in Maximum likelihood (ML) algorithms compared to Sentinel-2 images, reaching up to an overall accuracy (OA) of 80%, while the classification in Random Forest (RF) algorithms reached an overall accuracy (OA) of 87%. This paper recommends using the method of merging (SAR) Sentinel 1A,1B images and applying it, as well as conducting periodic monitoring of urban expansions using remote sensing techniques, to make sustainable decisions for the future of urban expansion master plans in the region and to quickly explore informal built-up encroachments.

Drone-Based Characterization of Seagrass Habitats in the Tropical Waters of Zanzibar

Unmanned automatic systems (UAS) are increasingly being applied as an alternative to more costly time-consuming traditional methods for mapping and monitoring marine shallow-water ecosystems. Here, we demonstrate the utility of combining aerial drones with in situ imagery to characterize the habitat conditions of nine shallow-water seagrass-dominated areas on Unguja Island, Zanzibar. We applied object-based image analysis and a maximum likelihood algorithm on the drone images to derive habitat cover maps and important seagrass habitat parameters: the habitat composition; the seagrass species; the horizontal- and depth-percent covers, and the seascape fragmentation. We mapped nine sites covering 724 ha, categorized into seagrasses (55%), bare sediment (31%), corals (9%), and macroalgae (5%). An average of six seagrass species were found, and 20% of the nine sites were categorized as “dense cover” (40–70%). We achieved high map accuracy for the habitat types (87%), seagrass (80%), and seagrass species (76%). In all nine sites, we observed clear decreases in the seagrass covers with depths ranging from 30% at 1–2 m, to 1.6% at a 4–5 m depth. The depth dependency varied significantly among the seagrass species. Areas associated with low seagrass cover also had a more fragmented distribution pattern, with scattered seagrass populations. The seagrass cover was correlated negatively (r2 = 0.9, p < 0.01) with sea urchins. A multivariate analysis of the similarity (ANOSIM) of the biotic features, derived from the drone and in situ data, suggested that the nine sites could be organized into three significantly different coastal habitat types. This study demonstrates the high robustness of drones for characterizing complex seagrass habitat conditions in tropical waters. We recommend adopting drones, combined with in situ photos, for establishing a suite of important data relevant for marine ecosystem monitoring in the Western Indian Ocean (WIO).

Land use and land cover (LULC) change analysis using TM, ETM+ and OLI Landsat images in district of Okara, Punjab, Pakistan

Land use and land cover (LULC) changes are one of the world-wide variations which have the most significant effects on the natural environment and ecosystem due to human activities. Pakistan is facing enormous changes in LULC patterns without any good arrangement from the last few years. The main objective of this study is to identify the LULC changes in district Okara inat 3 different years, (2000, 2010, and 2020) using various Landsat images and to detect the reasons for the variations. Supervised classification was used to identify the LULC changes over the district of Okara, as it explains the maximum likelihood algorithm using ERDAS imagine 15 software. The total population in district Okara was 2.2 Million in 1998 that increased by 3 Million in 2017. The vegetation area was 91.6% in 2000, which decreased by 89.3% in 2020 in the study area. The built-up area was increased 4.5% from 2000 to 2020 due to the increasing population in district Okara. Overall accuracy assessment was observed 90% for 2000, 87% for 2010, and 85% for 2020 in the study area. Our result showed that, land which was used for vegetation area is changed into the build-up area due to the increasing in infrastructural and commercial areas in district Okara. As a result, proper observation of LULC changes will help the related agencies, government and policymakers for developing the land management.

A comparison of confounder selection and adjustment methods for estimating causal effects using large healthcare databases.

PurposeConfounding adjustment is required to estimate the effect of an exposure on an outcome in observational studies. However, variable selection and unmeasured confounding are particularly challenging when analyzing large healthcare data. Machine learning methods may help address these challenges. The objective was to evaluate the capacity of such methods to select confounders and reduce unmeasured confounding bias.MethodsA simulation study with known true effects was conducted. Completely synthetic and partially synthetic data incorporating real large healthcare data were generated. We compared Bayesian adjustment for confounding (BAC), generalized Bayesian causal effect estimation (GBCEE), Group Lasso and Doubly robust estimation, high‐dimensional propensity score (hdPS), and scalable collaborative targeted maximum likelihood algorithms. For the hdPS, two adjustment approaches targeting the effect in the whole population were considered: Full matching and inverse probability weighting.ResultsIn scenarios without hidden confounders, most methods were essentially unbiased. The bias and variance of the hdPS varied considerably according to the number of variables selected by the algorithm. In scenarios with hidden confounders, substantial bias reduction was achieved by using machine‐learning methods to identify proxies as compared to adjusting only by observed confounders. hdPS and Group Lasso performed poorly in the partially synthetic simulation. BAC, GBCEE, and scalable collaborative‐targeted maximum likelihood algorithms performed particularly well.ConclusionsMachine learning can help to identify measured confounders in large healthcare databases. They can also capitalize on proxies of unmeasured confounders to substantially reduce residual confounding bias.

Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

One of the most important methods used to cope with multipath fading effects, which cause the symbol to be received incorrectly in wireless communication systems, is the use of multiple transceiver antenna structures. By combining the multi-input multi-output (MIMO) antenna structure with non-orthogonal multiple access (NOMA), which is a new multiplexing method, the fading effects of the channels are not only reduced but also high data rate transmission is ensured. However, when the maximum likelihood (ML) algorithm that has high performance on coherent detection, is used as a symbol detector in MIMO NOMA systems, the computational complexity of the system increases due to higher-order constellations and antenna sizes. As a result, the implementation of this algorithm will be impractical. In this study, the backtracking search algorithm (BSA) is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA systems. To emphasize the efficiency of the proposed algorithm, simulations have been made for the system with various antenna sizes. As can be seen from the obtained results, a considerable reduction in complexity has occurred using BSA compared to the ML algorithm, also the bit error performance of the system is increased compared to other algorithms.

BLAST PERFORMANCE IN MIMO WIRELESS COMMUNICATION SYSTEMS

Recently, a new detection algorithm for the Vertical Bell Laboratories Layered Space Time (V-BLAST) system, which we call Enhanced-V-BLAST, or simply E-V-BLAST, was proposed. V-BLAST has demonstrated very high spectral efficiency, there is a wide gap between the original V-BLAST algorithm and the Maximum Likelihood (ML) algorithm. Recently, some algorithms have been presented to make narrower this gap. In a new detection algorithm (Enhanced (E) V BLAST) for V-BLAST system was presented which showed significantly better performance and flexibility than the original V-BLAST detection. The E-V-BLAST algorithm gets closer to the optimum detection Maximum Likelihood (ML) algorithm by making decisions based on multiple symbols. The Enhanced (E)-V-BLAST has two parameters which can be adjusted to achieve a desired performance and complexity trade-off. In the present paper, we analyze the performance of EV-BLAST as a function of the two inherent adjustable parameters of E-V-BLAST. On the basis of this analysis, we obtain some useful characteristics of the E-V-BLAST algorithm which allow one to achieve the desired performance and complexity trade-off.

Forest Cover Change Detection using Geospatial Technologies in Chandaka National Park, Odisha, India

Forest plays a vital role in carbon sequestration and climate regulation. A crucial tool for managing forest, particularly in protected regions, is keeping track of how the land cover changes in natural places. Using geospatial approaches, such as remote sensing and geographic information system (GIS), the present study has revealed spatio-temporal changes in land use categories and forest cover in the Chandaka National Park of Odisha, India, throughout the period of 1980-2020. The Landsat, LISS III and Sentinel satellite images of the year 1980, 2000 and 2020 were utilized respectively to map five land use land cover categories i.e. deciduous broadleaf forest, crop land, mixed forest, scrub land and water bodies in this preserved area. The satellite images were classified using a Supervised Classification method using Maximum Likelihood algorithm and ground control points (GCPs) were used for the spatial statistical analyses. The overall accuracies of the classification method in land cover categories in year 1980, 2000 and 2020 were 90.45%, 92.76% and 94.68%, respectively. Elsewhere, in order to study land use land cover (LULC) change and loss in forest of the Chandaka National Park, LULC classification, per-pixel scales post classification and self-knowledge on the LULC change process were used. The LULC change detection results showed that deciduous broadleaf forest decreased from 179.01 sq. km (76.66%) in 1980 to 132.75 sq. km (56.85%) in 2020, while mixed forest cover increased by 8.17 sq. km (3.50%) in 1980 to 38.84 sq. km (16.63%) in 2020. Crop land, Scrub land and Water bodies were also increased by 3.34%, 3.27% and 0.07%, respectively. Two significant change processes in the area are the logging activities in several places for timber and the conversion of natural forests with plantation. Agriculture expansion in the forest’s periphery is linked to the dramatic decline in forest cover change. The decline in forest cover is also a result of the production of charcoal and lumber exploitation. Overall, our findings indicated that more public awareness and participatory forest management are necessary to preserve Chandaka National Park. This study highlights the use of geospatial technologies in understanding the changes in LULC in the Chandaka National Park.

Continuous Estimation of Human Knee Joint Angles by Fusing Kinematic and Myoelectric Signals.

Exoskeleton robot is an essential tool in active rehabilitation training for patients with lower limb motor dysfunctions. Accurate and real-time recognition in human motion intention is a great challenge in exoskeleton robot, which can be implemented by continues estimation of human joint angles. In this study, we innovatively proposed a novel feature-based convolutional neural network-bi-directional long-short term memory network (CNN-BiLSTM) model to predict the knee joint angles more accurately and in real time. We validated our method on a public dataset, including surface electromyography(sEMG) and inertial measurement unit (IMU) data of 10 healthy subjects during normal walking. Initially, features extraction from each modal data achieved feature-level fusion. Then the importance of each sEMG and IMU signal feature for knee joint angle prediction was quantified by ensemble feature scorer (EFS) and the number of features required for prediction while ensuring accuracy was simplified by profile likelihood maximization (PLM) algorithm. Finally, the CNN-BiLSTM model was created by using the determined simplest features to further fuse the spatio-temporal correlation of signals. The results indicated that the EFS and PLM algorithm could remove the feature redundancy perfectly and estimation performance would become better when bi-modal gait data were fused. For the estimation performance, the average root mean square error (RMSE), adjusted [Formula: see text] and pearson correlation coefficient (CC) of our algorithm were 4.07, 0.95, and 0.98, respectively, which was better than CNN, BiLSTM and other three traditional machine learning methods. In addition, the model test time was 62.47 ± 0.29 ms, which was less than the predicted horizon of 100 ms. The real-time performance and accuracy are satisfactory. Compared with previous works, our method has great advantages in feature selection and model design, which further improves the prediction accuracy. These promising results demonstrate that the proposed method has considerable potential to be applied to exoskeleton robot control.

РЕГУЛЯРИЗУЮЩИЕ АЛГОРИТМЫ ДЕТЕКТИРОВАНИЯ СИГНАЛА M QAM В СИСТЕМЕ С MIMO, ОСНОВАННЫЕ НА ФИЛЬТРАЦИИ КАЛМАНА

The aim of the article is to increase the noise immunity of signal reception with a satisfactory computational complexity of signal processing algorithms. The article discusses two regularizing recurrent algorithms for detecting quadrature amplitude modulation (M-QAM) signal in a multiple-input and multiple-output (MIMO) method and a direct transform receiver based on the Kalman filter. Kalman filtering using for soft decision detection operates at a fixed point in time and estimates symbols in iterations. The proposed detection algorithms contain a regularizing parameter. For one algorithm, the regularization parameter is selected empirically, for the other, it is found by a closed expression, which includes estimations of symbols obtained at the last step of the iterative algorithm. Hard decisions are determined by the criterion of the minimum distance between the received soft decisions and the possible values of symbols for each transmitting antenna separately. The proposed detectors are compared in terms of noise immunity (in a system without coding) with the Zero Forsing method and an algorithm that works according to the root-mean-square error (RMS) criterion. The channel is supposed to be known or it is estimated by the least squares (LS) method using first-order polynomial approximation. The article claims that regularizing detection algorithms make it possible to increase the noise immunity of signal reception relative to the Zero Forsing algorithms and RMS. In addition, this article contains an analysis of the computational complexity of the proposed recurrent algorithms. The use of the regularization parameter makes it possible to reduce the number of iterations in the detection algorithm needed to obtain the required error probability per symbol. This can reduce the number of arithmetic operations, resulting in a reduction in the computational complexity of signal processing algorithms. The proposed detection algorithms are more complicated than the Zero Forsing and RMS, but it is much simpler than the detector synthesized according to the maximum likelihood (ML) algorithm.

Design and Testbed Implementation of Blind Parameter Estimated OFDM Receiver

Designing an intelligent or adaptive transceiver system is becoming a promising technology for upcoming generations of wireless communication systems due to its adaptivity, spectrum efficiency, and low-latency characteristics. However, there is no work available until now that characterizes and demonstrates complete adaptation in the physical layer for orthogonal frequency-division multiplexing (OFDM) systems. In this article, we propose and implement sequential blind parameter estimation methods for OFDM signals using radio frequency (RF) testbed setup in a realistic scenario. The estimations include the number of subcarriers, symbol duration, cyclic prefix, oversampling factor, symbol timing offset (STO), and carrier frequency offset (CFO). The proposed algorithms also include blind modulation classification for linearly modulated signals over a frequency-selective fading channel. The parameter estimation has been carried out through a cyclic cumulant process. The modulation formats are classified by using normalized fourth-order cumulant in the frequency domain. The STO and CFO are estimated by a proposed modified maximum likelihood algorithm. The performances of parameter estimations, modulation classification, and synchronization are measured through analytical, simulation, and measurement studies. The overall performance of the OFDM system is provided in terms of the received constellation diagram and bit error rate (BER) over an indoor propagation environment.

Dr. Eiichi Tanaka's Achievement in the Research of Image Reconstruction

In memoriam of Dr. Eiichi Tanaka who passed away on August 21, 2021, I review his achievement in the research of tomographic image reconstruction. Tomographic image reconstruction is an important research area which has wide applications including X-ray CT, nuclear medicine imaging such as PET and SPECT, and electron microscopy. Since 1970's, Dr. Tanaka has worked on numerous important topics in tomographic reconstruction fields aiming at using them in image reconstruction for PET and SPECT. Among them, in this paper, I will introduce his research on Filtered BackProjection (FBP) method, analytical attenuation correction in SPECT, image reconstruction in Time-of-Flight PET, image reconstruction for 3-D PET imaging, and iterative image reconstruction method called Dynamic Row-Action Maximum Likelihood Algorithm (DRAMA).

Three-dimensional cooperative localization via space-air-ground integrated networks

The space-air-ground integrated network (SAGIN) combines the superiority of the satellite, aerial, and ground communications, which is envisioned to provide high-precision positioning ability as well as seamless connectivity in the 5G and Beyond 5G (B5G) systems. In this paper, we propose a three-dimensional SAGIN localization scheme for ground agents utilizing multi-source information from satellites, base stations and unmanned aerial vehicles (UAVs). Based on the designed scheme, we derive the positioning performance bound and establish a distributed maximum likelihood algorithm to jointly estimate the positions and clock offsets of ground agents. Simulation results demonstrate the validity of the SAGIN localization scheme and reveal the effects of the number of satellites, the number of base stations, the number of UAVs and clock noise on positioning performance.

Automated Pain Assessment in Children Using Electrodermal Activity and Video Data Fusion via Machine Learning.

Pain assessment in children continues to challenge clinicians and researchers, as subjective experiences of pain require inference through observable behaviors, both involuntary and deliberate. The presented approach supplements the subjective self-report-based method by fusing electrodermal activity (EDA) recordings with video facial expressions to develop an objective pain assessment metric. Such an approach is specifically important for assessing pain in children who are not capable of providing accurate self-pain reports, requiring nonverbal pain assessment. We demonstrate the performance of our approach using data recorded from children in post-operative recovery following laparoscopic appendectomy. We examined separately and combined the usefulness of EDA and video facial expression data as predictors of children's self-reports of pain following surgery through recovery. Findings indicate that EDA and facial expression data independently provide above chance sensitivities and specificities, but their fusion for classifying clinically significant pain vs. clinically nonsignificant pain achieved substantial improvement, yielding 90.91% accuracy, with 100% sensitivity and 81.82% specificity. The multimodal measures capitalize upon different features of the complex pain response. Thus, this paper presents both evidence for the utility of a weighted maximum likelihood algorithm as a novel feature selection method for EDA and video facial expression data and an accurate and objective automated classification algorithm capable ofdiscriminating clinically significant pain from clinically nonsignificant pain in children.

Maximum Likelihood Algorithm for Time-Delay Based Multistatic Target Localization

In this letter we address the problem of multistatic target localization using time-delay measurements corrupted by noise with unknown non-uniform variances. More concretely, we consider the problem of joint maximum likelihood (ML) estimation of the target position and the noise variances, for which we propose a majorization-minimization based algorithm. The proposed approach is compared with state-of-the-art algorithms, and the simulation results show the excellent accuracy of our algorithm.

Identification of mercury‐resistant Streptomyces isolated from Cyperus rotundus L. rhizosphere and molecular cloning of mercury (II) reductase gene

Streptomyces is one of mercury‐resistant bacteria which can convert Hg2+ into nontoxic Hg0 . This study aimed to identify mercury‐resistant Streptomyces present in the Cyperus rotundus rhizosphere from artisanal small‐scale gold mining (ASGM) area and clone merA gene to the cloning and expression vectors. Molecular identification was conducted using 16s rRNA gene with the maximum likelihood algorithms. Results revealed that the AS1 and AS2 strains were a group of Streptomyces ardesiacus and the BR28 strain was closed to Brevibacillus agri. The AS2 merA gene was cloned to pMD20 cloning vectors, pGEX‐5x‐1 and pET‐28c expression vectors. The transformation was successfully performed in BL21 and DH5α competent cells. The full length of the merA gene was confirmed to be 1,425 bp. This study is the first research on identifying mercury‐resistant Streptomyces and cloning the full‐length merA gene in Indonesia.

Implementing the meta-analytic approach for the evaluation of surrogate endpoints in SAS and R: a word of caution

ABSTRACT The meta-analytic approach has become the gold-standard methodology for the evaluation of surrogate endpoints and several implementations are currently available in SAS and R. The methodology is based on hierarchical models that are numerically demanding and, when the amount of data is limited, maximum likelihood algorithms may not converge or may converge to an ill-conditioned maximum such as a boundary solution. This may produce misleading conclusions and have negative implications for the evaluation of new drugs. In the present work, we explore the use of two distinct functions in R (lme and lmer) and the MIXED procedure in SAS to assess the validity of putative surrogate endpoints in the meta-analytic framework, via simulations and the analysis of a real case study. We describe some problems found with the lmer function in R that led to a poorer performance as compared with the lme function and MIXED procedure.

Pemetaan Distribusi Mangrove Menggunakan Citra Sentinel-2A: Studi Kasus Kota Langsa

Mangroves are one of the most productive ecosystems for human life, marine ecosystems, and coastal areas. Mangrove distribution is a distribution based on specific geographical or administrative boundaries. Kota Langsa is one of the areas that has a good representation of the distribution of mangroves. Therefore, researchers studied the Kota Langsa area because Kota Langsa is one of the areas with the largest and most diverse mangrove ecosystem in Aceh Province. This study examines the mapping of mangrove distribution using Sentinel-2A multispectral imagery with composite images of Red, Green, and Blue. This research uses SNAP software. The research stages consist of radiometric correction, atmospheric correction, and multispectral image classification. The method used in image classification is the maximum likelihood algorithm. The use of the maximum likelihood algorithm is because the maximum likelihood algorithm gives the best results among other algorithms. The development of the research is the distribution of mangroves in Langsa City, covering an area of 4727.35 ha, which is divided into three sub-districts and eleven gampong (kelurahan). The sub-districts that have mangrove distribution are East Langsa District covering an area of 3240.25 Ha (68.55%), Langsa Barat District covering an area of 1486.47 Ha (31.45%), and Langsa Lama District covering an area of 0.63 Ha (0.013).

Genetic and non-genetic effects associated with ewe productivity in Harnali sheep.

The objective of the current study was to estimate the genetic parameters for ewe productivity traits of Harnali sheep by examining non-genetic effects. The data records of 440 animals born to 85 sires and 259 dams were collected with respect to various traits such as litter size at birth (LSB), litter weight at birth (LWB), litter size at weaning (LSW), litter weight at weaning (LWW) and age at first lambing (AFL) for the period of 2001 to 2020. Genetic parameters were estimated by fitting a series of animal models using an average information restricted maximum likelihood (REML) algorithm in WOMBAT software. Least-squares analysis revealed significant (P < 0.05) influences of period of lambing, age and weight of ewe at lambing on the studied traits. These results indicated that heavier ewes had significantly higher (P < 0.05) values of litter weight traits than their counterparts. On the basis of likelihood ratio test, the estimates of direct heritability under best model for AFL, LSB, LWB, LSW and LWW were 0.06, 0.18, 0.09, 0.07 and 0.16, respectively. Maternal permanent environment effect made a significant contribution to the LSB trait (0.20). The genetic correlation between litter size and LWW was negative, while the remaining correlations were positive. The present results suggest that selection based on ewe productivity traits will result in low genetic progress and therefore the management role is more important for better gains.

Spatio-temporal assessment on land use and land cover (LULC) and forest fragmentation in shifting agroecosystem landscape in Ukhrul district of Manipur, Northeast India.

The information on land use and land cover (LULC) plays a critical role in understanding the interactions between human activities and the natural environment. The changes in LULC have a significant impact on the ecological integrity of forests, biodiversity, and natural resources, which in turn trigger global environmental change. Forest fragmentation is an important conservation challenge that includes interdependent forest loss components and spatial shift patterns. Over the years, Northeast India has experienced major changes in LULC and forest fragmentation. There are limited information and data regarding the change in LULC patterns and causes of forest fragmentation. The present study was carried out with an attempt to analyze the change in LULC and forest fragmentation using satellite data of three different time series: 1991, 2005, and 2020 for Ukhrul district, Manipur, Northeast India. Different LULC classes were classified using the supervised method, viz., maximum likelihood algorithm in ERDAS Imagine 2014 and generated thematic maps in ArcGIS 10.4 software. Considering the classified forest class, fragmentation in the forest area was grouped into different categories of fragmentation using the Landscape Fragmentation Tool (LFT v 2.0). The distribution of the perforated category has tremendously increased in 2020 from 1991. The outcome of the present study will help to understand the inherent forest vulnerability and to adopt sustainable management strategies for forest and agriculture ecosystems in the hill landscape.