Manhattan Distance Measure Research Articles

Semantic differences and psychological behavior in multi-criteria group decision-making: Do they need consideration?

In Multi-Criteria Group Decision-Making (MCGDM), linguistic evaluation is widely used due to its flexibility. In particular, Personalized Individual Semantics (PIS), namely decision-makers hold heterogeneous understandings of the same word, has been extensively studied to elicit numeric meaning to the linguistic scale. However, few studies consider the need to tailor linguistics to the different indicators used, as well as the semantic difference in the same level linguistic that experts use when situations change. The traditional PIS models overlook the psychological aspect of the decision makers. To solve these problems, this paper proposes a method that considers semantic differences arising from the decision-makers and the indicators and incorporates the experts' psychological behavior. We apply this method to assess the alternatives for a smart refrigerator product service system. The results suggest that a large semantic difference, when evaluating the linguistic indicators, can influence the final outcome. We validate the flexibility and the effectiveness of the method by comparing against the base case of no semantic difference, using the Euclidean and Manhattan distance measures.

A unique approach for protein secondary structure comparison under TOPS representation

To unravel the intricate connection between protein function and protein structure, it is imperative to comprehensively evaluate protein secondary structure similarity from various perspectives. While numerous techniques have been suggested for comparing protein secondary structure elements (SSE), there continues to be a substantial need for finding alternative ways of comparing the same. In this paper, Topology of Protein Structure (TOPS) representations of protein secondary structures are considered to offer a new alignment-free method for evaluating similarities/dissimilarities of protein secondary structures. Initially, a two-dimensional numerical representation of the SSE is created, associating each point with a mass reflecting its frequency of occurrence. Then the means of coordinate values are determined by averaging weighted sums, and these mean values are subsequently used to calculate moments-of-inertia. Next, a four-component descriptor is generated out of the eigenvalues of the matrix and the mean values of the represented coordinates. Thereafter, Manhattan distance measure is used to obtain the distance matrix. This is finally applied to obtain the phylogenetic trees under the use of NJ method. SSE considered in the proposed method comprises 36-elements from the Chew-Kedem database giving five different taxa: globin, alpha-beta, tim-barrel, beta, and alpha. Phylogenetic trees were created for these SSE through the application of various methods: Clustal-Omega, LZ-Complexity, SED, TOPS + and TOC, to facilitate comparative analysis. Phylogenetic tree of the proposed method outperformed results of the previous methods when applied to the same SSE. Therefore, the method effectively constructs phylogenetic tree for analyzing protein secondary structure comparison. Communicated by Ramaswamy H. Sarma

Multi-dimensional multi-round minimum cost consensus models with iterative mechanisms involving reward and punishment measures

In order to address the issues of unfairness in consensus and subjectivity bias in data, while further expanding the traditional minimum cost consensus model (MCCM) in terms of opinion dimension and consensus round, we propose the multi-dimensional MCCM (MD-MCCM) and multi-round MCCM (MR-MCCM). Subsequently, we introduce the concepts and definitions pertaining to these two variants of consensus and formulate their respective consensus model frameworks. To benefit from the advantages of both, we integrate these two types of consensus models, thereby introducing the concept of multi-dimensional multi-round minimum cost consensus. To address the issues of unfairness and subjectivity in the consensus-reaching process, we propose two specific reward and punishment measures and devise the multi-dimensional multi-round consensus iterative mechanisms. These mechanisms facilitate an iterative update of decision-makers’ opinions, weights, and unit costs within the feedback process. Based on this foundation, we construct the multi-dimensional multi-round MCCM (MDMR-MCCM) involving Euclidean and Manhattan distance measures, and their corresponding algorithm and consensus reaching process are presented. Finally, a group decision problem concerning site selection validates the rationality and effectiveness of our models. Moreover, a comparative analysis of results obtained using different models is provided.

Developing Viola Jones' algorithm for detecting and tracking a human face in video file

<p>Face detecting and tracking in video clips is very important in many areas of<br />daily life. All institutions, public departments, streets, and large stores use<br />cameras from a security point of view, and detecting and tracking human<br />faces is necessary for indexing and preserving information concerning the<br />visual media. This paper presents a novel method for hybridizing the<br />Viola_Jones face detection algorithm to track and identify a human face in<br />video sequences. The method represents a combination of Viola Jones'<br />algorithm with a measured normalized cross-correlation (NCC) algorithm<br />with a template matching method using the Manhattan distance measure<br />equation in the video between successive sequences After that, the fuzzy<br />logic method is added in comparing the image of the face to be detected with<br />the images of templates taken in the proposed algorithm, which increased the<br />accuracy of the results, which reached 99.3%.</p>

Detection of DoS Attacks in Smart City Networks With Feature Distance Maps: A Statistical Approach

Smart cities are highly digitalized cities, with a high volume of data stored digitally, as well as a large number of physical objects and Internet of Things (IoT) devices. It is no surprise that security concerns increase as connected IoT devices become more prevalent. There has long been a perception that Denial of Service (DoS) attacks give rise to a notable menace to the security of smart city networks. A statistical method based on feature distance maps is suggested in this work for the identification of DoS attacks in smart city networks. This study aims to build a DoS attack detection model that has a low computational complexity and a low rate of false positives. The performance of the model is arbitrated using smart city network traffic dataset. In order to make processing easier, the features are normalized using Min-Max normalization. A subset of features suitable for attack detection can be evaluated with Pearson Correlation Coefficient. A feature distance map method is proposed for extracting correlations between features between records. Manhattan distance measures are used to generate the feature distance maps. The feature distance maps enhance the statistical analysis process. Hellinger distance measure is used to generate the normal traffic profile by calculating the degree of similarity between the feature distance map of each record and the normal traffic profile mean. On the basis of the proposed threshold, the Hellinger distance is used to evaluate whether an unknown traffic data belongs to normal or attack. Compared with Mahalanobis distance-based DoS detection, the Hellinger distance-based attack detection takes less time to execute. Based on the theoretical and experimental results, the proposed DoS attack detection system has a low computational complexity and a low false positive rate. Furthermore, the proposed approach outperforms the existing state-of-the-art feature ranking based DoS attack detection systems.

An approach to linguistic q-rung orthopair fuzzy multi-attribute decision making with LINMAP based on Manhattan distance measure

In order to estimate the deficiency of a city in its ability to prevent and control risks, as well as to evaluate the corresponding measures, this paper focuses on multi-attribute decision making based on LINMAP method and Manhattan distance at linguistic q-rung orthopair fuzzy. Manhattan distance is a new product that combines clustering distance with linguistic q-rung orthopair fuzzy to be able to use the data more effectively for measurement. LINMAP method is a decision making method based on ideal points, which can solve the weights as well as provide ideal solutions by linear programming model. The combination of the two can create a new decision-making method, which can effectively evaluate the decision scheme of social public facilities according to the actual needs of decision-makers. The new method has the following advantages: (1) the conditions of linguistic fuzzy numbers can be applied more comprehensively, making the decision more realistic and effective; (2) the Manhattan distance is more in line with the human way of thinking and closer to life; (3) after comparative study, the results produced by this method have certain reliability.

The Promise of MOOCs Revisited? Demographics of Learners Preparing for University

This paper leverages cluster analysis to provide insight into how traditionally underrepresented learners engage with entry-level massive open online courses (MOOCs) intended to lower the barrier to university enrolment, produced by a major research university in the United States. From an initial sample of 260,239 learners, we cluster analyze a subset of data from 29,083 participants who submitted an assignment in one of nine entry-level MOOC courses. Manhattan distance and Gower distance measures are computed based on engagement, achievement, and demographic data. To our knowledge, this marks one of the first such uses of Gower distance to cluster mixed-variable data to explore fairness and equity in the MOOC literature. The clusters are derived from CLARA and PAM algorithms, enriched by demographic data, with a particular focus on education level, as well as approximated socioeconomic status (SES) for a smaller subset of learners. Results indicate that learners without a college degree are more likely to be high-performing compared to college-educated learners. Learners from lower SES backgrounds are just as likely to be successful as learners from middle and higher SES backgrounds. While MOOCs have struggled to improve access to learning, more fair and equitable outcomes for traditionally underrepresented learners are possible.

Research on the success of unsupervised learning algorithms in indoor location prediction

With location-based smart applications, the flow of life can be facilitated and support can be provided in case of security and emergency situations. Indoor location detection provides various conveniences in complex structures such as hospitals, schools, shopping centers, etc. Indoor location detection studies are carried out by using data related to location and signal and machine learning methods. Machine learning has become frequently used as a solution method in this field, as in many other fields. When the studies in the literature are examined, it is seen that the studies are mainly focused on producing solutions with supervised machine learning algorithms. Unsupervised algorithms are frequently used to determine the labels of data groups that do not have labels. In this direction, it can be seen as the first step in labeling the data collected in indoor positioning studies and then using it for training predictive models to be developed with supervised learning methods. For this reason, the results to be obtained regarding the success and usefulness of cluster analysis will constitute an important basis for further studies. In this study, it is aimed to examine the success of unsupervised learning, in other words, clustering algorithms. The Wireless Indoor Localization Data Set and well-known k-Means and Fuzzy c-Means algorithms have been used with different distance measure. The obtained methods performances have been evaluated with internal and external indices. The results show that the clustering algorithms can cluster correctly data points in the range of 93-95% according to the accuracy and F measure value. Although performances indicators are very close to each other according to the internal indexes, it can be stated that the model obtained using the Manhattan distance measure and the k-Means algorithm has higher performance in terms of clustering success.

3-Tuple Linguistic Distance-Based Model for a New Product go/no-go Evaluation

There is a need for a probabilistic linguistic term set model for go/no-go product screening problem for new product development to meet a firm’s expectation. This paper develops a novel 3-tuple linguistic distance-based model to evaluate whether an overall respondents perception meets a firm’s expectation (“go”) for new product development. The respondent’s perception is collected by a Kansei-based survey as an interval-linguistic term. Then, an expected distance between the firm’s expectation and the respondent’s perception is computed by a target-based Manhattan distance measure. The expected distance is compared with a threshold to shows that what product attribute meets the firm’s expectation based on customers’ perceptions. A real case study of Thai-tea soy milk packaging design is provided. The proposed model is compared to the existing model to show its effectiveness and applicability. Experimental results show that the proposed model can effectively point out the inferior product attributes, which leads to redesign the product until all product concepts meet the target attributes before launching the product to the market. Thus, it can significantly reduce the risk of failure of the product in a real market. This paper has significant contributions in that it allows respondents to provide their opinions with uncertainty by providing an interval linguistic assessment, handles a bias of the heterogeneity of respondents by determining the weight of respondents, and overcomes limitations of existing models by applying target-oriented linguistic terms.

The Fingerprint-Like Pattern of Nocturnal Brain Activity Demonstrated in Young Individuals is Also Present in Senior Adulthood.

PurposeThe quantitative sleep EEG has been considered as electroencephalographic “fingerprint”, ie, it is stable within but differs between individuals. So far, however, almost all studies addressing this aspect have been conducted in young men. It was therefore of interest to know whether the sleep EEG fingerprint concept holds true in older samples of both sexes.Patients and MethodsData from three different subsamples of 30 healthy individuals each were reused for the present secondary analysis (young men (YM) = 25.6 ± 2.4 years, elderly men (EM) = 69.1 ± 5.5 years, elderly women (EW) = 67.8 ± 5.7 years). Individuals slept ten times in the sleep laboratory, resulting in a total of 900 study nights. However, to avoid misinterpretation due to intervention-related changes in sleep EEG power spectra, only the 3 sham nights without any intervention were included, reducing the datasets to 270. To determine stability of NREM sleep EEG power spectra between sham night pairs, within- and between-subject Manhattan distance measures were computed separately by sample.ResultsRegardless of subsample and sham night pair, lowest distance measures, ie, largest similarity, were observed for within-subject power spectra comparisons (range of mean distance measures for EW from 3.82 to 4.06, for EM from 3.55 to 3.63, and for YM from 3.04 to 3.62). Moreover, intraindividual similarity did not differ substantially between samples. Between-subject power spectra distance measures were considerably larger (range of mean distance measures for EW from 12.95 to 13.15, for EM from 12.21 to 12.57, and for YM from 10.33 to 10.78) and varied significantly between young and elderly individuals.ConclusionThe present results support the view that the sleep EEG power spectrum is an individual trait-like characteristic that remains unique up until old age. This finding may help to increase the sensitivity in measuring intervention effects.

Score-based likelihood ratios for linguistic text evidence with a bag-of-words model

The likelihood ratio paradigm for quantifying the strength of evidence has been researched in many fields of forensic science. Within this paradigm, score-based approaches for estimating likelihood ratios are becoming more prevalent in the forensic science literature. In this study, a score-based approach for estimating likelihood ratios is implemented for linguistic text evidence. Text data are represented via a bag-of-words model with the Z-score normalised relative frequencies of selected most-frequent words (the number of the most-frequent words = N), and the Euclidean, Manhattan and Cosine distance measures are trialled as the score-generating functions for comparing paired text samples. The score-to-likelihood-ratio conversion model was built using a common source method, and the best fitting model was selected from the parametric models of the Normal, Log-normal, Gamma and Weibull distributions. With the Amazon Product Data Authorship Verification Corpus, two groups of documents (each group including documents of approximately 700, 1400 and 2100 words) were synthesised for each author, allowing 720 same-author comparisons and 517,680 different-author comparisons to test the validity of the system. A series of experiments was conducted using combinations of the following conditions: the three score functions, the different values of N for the feature vector and the different document lengths. The validity of the system was assessed using the log-likelihood-ratio cost (Cllr), and the strength of the derived likelihood ratios was charted in the form of Tippett plots. It was demonstrated that 1) the Cosine measure consistently outperforms the other measures—the best performance is achieved with N = 260, regardless of the document length (e.g., Cllr values of 0.70640, 0.45314 and 0.30692, respectively, for 700, 1400 and 2100 words)—and 2) the derived likelihood ratios are very well calibrated irrespective of the distance measures and document lengths. A follow-up experiment showed that the described score-based approach is relatively robust and stable for a limited quantity of background data. The derived likelihood ratios that were estimated separately to the three distance measures were logistic regression fused; and the fusion achieved a further improvement in performance—for example, a Cllr of 0.23494 for 2100 words. This study demonstrates the possibility of designing likelihood ratio–based systems that discriminate between same-author and different-author documents.

Dual Phase CBIR Model using Hybrid Feature Extraction and Manhattan Distance Measure

Dual Phase CBIR Model using Hybrid Feature Extraction and Manhattan Distance Measure

Password policy characteristics and keystroke biometric authentication

Behavioural biometrics have the potential to provide an additional or alternative authentication mechanism to those involving a shared secret (i.e. a password). Keystroke timings are the focus of this study, where key press and release timings are acquired whilst monitoring a user typing a known phrase. Many studies exist in keystroke biometrics, but there is an absence of literature aiming to understand the relationship between characteristics of password policies and the potential of keystroke biometrics. Furthermore, benchmark datasets used in keystroke biometric research do not enable useful insights into the relationship between their capability and password policy. Herein, substitutions of uppercase, numeric, special characters, and their combination of passwords derived from English words are considered. Timings for 42 participants for the same 40 passwords are acquired. A matching system using the Manhattan distance measure with seven different feature sets is implemented, culminating in an Equal Error Rate of between 6% and 11% and accuracy values between 89% and 94%, demonstrating comparable accuracy to other threshold-based systems. Further analysis suggests that the best feature sets are those containing all timings and trigraph press to press. Evidence also suggests that phrases containing fewer characters have greater accuracy, except for those with special character substitutions.

Optimized multimodal biometric system based fusion technique for human identification

This paper presents three novelty aspects in developing biometric system-based face recognition software for human identification applications. First, the computations cost is greatly reduced by eliminating the feature extraction phase and considering only the detected face features from the phase congruency. Secondly, a motivation towards applying a new technique, named mean-based training (MBT) is applied urgently to overcome the matching delay caused by the long feature vector. The last novelty aspect is utilizing the one-to-one mapping relationship for fusing the edge-to-angle unimodal classification results into a multimodal system using the logical-OR rule. Despite some dataset difficulties like Unconstrained Facial Images(UFI) which includes varying illuminations, expressions, occlusions, and poses, the multimodal system has highly improved the accuracy rate and achieved a promising recognition result, where the decision fusion is classified correctly (84, 92, and 72%) with only one training vector per MBT in contrast to (80, 62, and 68%) with five training vectors for Normal matching. These results are measured by Eucledian, Manhattan, and Cosine distance measure respectively.

Probabilistic Unsupervised Machine Learning Approach for a Similar Image Recommender System for E-Commerce

The recommender system is the most profound research area for e-commerce product recommendations. Currently, many e-commerce platforms use a text-based product search, which has limitations to fetch the most similar products. An image-based similarity search for recommendations had considerable gains in popularity for many areas, especially for the e-commerce platforms giving a better visual search experience by the users. In our research work, we proposed a machine-learning-based approach for a similar image-based recommender system. We applied a dimensionality reduction technique using Principal Component Analysis (PCA) through Singular Value Decomposition (SVD) for transforming the extracted features into lower-dimensional space. Further, we applied the K-Means++ clustering approach for the possible cluster identification for a similar group of products. Later, we computed the Manhattan distance measure for the input image to the target clusters set for fetching the top-N similar products with low distance measure. We compared our approach with five different unsupervised clustering algorithms, namely Minibatch, K-Mediod, Agglomerative, Brich, and the Gaussian Mixture Model (GMM), and used the 40,000 fashion product image dataset from the Kaggle web platform for the product recommendation process. We computed various cluster performance metrics on K-means++ and achieved a Silhouette Coefficient (SC) of 0.1414, a Calinski-Harabasz (CH) index score of 669.4, and a Davies–Bouldin (DB) index score of 1.8538. Finally, our proposed PCA-SVD transformed K-mean++ approach showed superior performance compared to the other five clustering approaches for similar image product recommendations.

Approximate relations between Manhattan and Euclidean distance regarding Latin hypercube experimental design

Among several existing distance measures, perhaps Euclidean distance is the most used tool to calculate distances and then Manhattan distance measure, though which distance measure is suitable depends on the problems. In the field of Design of Experiments (DoEs) especially in simulation domain Latin Hypercube design (LHD) is a well-known approach to find out design points for experiments/simulations. Generally, randomly generated LHDs show poor space-filling property. But space-filling is one of the most required impotent properties for DoE. So researchers search optimal LHD in the sense of space-filling. Some authors considered Euclidean distance measure on the other hand some other authors considered Manhattan distance measure to find out optimal LHD regarding space-filling. It is obvious that both optimal LHDs are not identical. So it is a crucial asked - which measure provided better space-filling? The main problem is that there is no any relation among the distance measures especially Euclidean distance measure and Manhattan distance measure. In this article we have established some relations and bounds between Euclidean distance measures and Manhattan distance measures in perspective of LHD. Finally some experimental results are compared namely maximin LHDs measured by both Euclidean distance measure and Manhattan distance measures by using some proposed transformation techniques.

Optimal Classification of Lung Cancer Related Genes using Enhanced reliefF Algorithm and Multiclass Support Vector Machine

Currently, the automatic lung cancer classification remains a challenging issue for the researchers, due to noisy gene expression data, high dimensional data, and the small sample size. To address these problems, an enhanced gene selection algorithm and multiclass classifier are developed. In this research, the lung cancer-related genes (GEO IDs: GSE10245, GSE19804, GSE7670, GSE10072, and GSE6044) were collected from Gene Expression Omnibus (GEO) dataset. After acquiring the lung cancer-related genes, gene selection was carried out by using enhanced reliefF algorithm for selecting the optimal genes. In enhanced reliefF gene selection algorithm, earthmover distance measure and firefly optimizer were used instead of Manhattan distance measure for identifying the nearest miss and nearest hit instances, which significantly lessens the “curse of dimensionality” issue. These optimal genes were given as the input for Multiclass Support Vector Machine (MSVM) classifier for classifying the sub-classes of lung cancer. The experimental section showed that the proposed system improved the classification accuracy up to 3-10% related to the existing systems in light of accuracy, False Positive Rate (FPR), error rate, and True Positive Rate (TPR).

Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering.

Background and objectives: Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre-clinical phase of drug development pipeline as well as in toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity and their associated biomarker genes. However, these methods consume more time for computation of the model parameters using the EM (expectation-maximization) based iterative approaches. To overcome this problem, in this paper, an attempt is made to propose an alternative approach based on hierarchical clustering (HC) for the same purpose. Methods and materials: There are several types of HC approaches whose performance depends on different similarity/distance measures. Therefore, we explored suitable combinations of distance measures and HC methods based on Japanese Toxicogenomics Project (TGP) datasets for better clustering/co-clustering between DDs and genes as well as to detect toxic DDs and their associated biomarker genes. Results: We observed that Word’s HC method with each of Euclidean, Manhattan, and Minkowski distance measures produces better clustering/co-clustering results. For an example, in the case of the glutathione metabolism pathway (GMP) dataset LOC100359539/Rrm2, Gpx6, RGD1562107, Gstm4, Gstm3, G6pd, Gsta5, Gclc, Mgst2, Gsr, Gpx2, Gclm, Gstp1, LOC100912604/Srm, Gstm4, Odc1, Gsr, Gss are the biomarker genes and Acetaminophen_Middle, Acetaminophen_High, Methapyrilene_High, Nitrofurazone_High, Nitrofurazone_Middle, Isoniazid_Middle, Isoniazid_High are their regulatory (associated) DDs explored by our proposed co-clustering algorithm based on the distance and HC method combination Euclidean: Word. Similarly, for the peroxisome proliferator-activated receptor signaling pathway (PPAR-SP) dataset Cpt1a, Cyp8b1, Cyp4a3, Ehhadh, Plin5, Plin2, Fabp3, Me1, Fabp5, LOC100910385, Cpt2, Acaa1a, Cyp4a1, LOC100365047, Cpt1a, LOC100365047, Angptl4, Aqp7, Cpt1c, Cpt1b, Me1 are the biomarker genes and Aspirin_Low, Aspirin_Middle, Aspirin_High, Benzbromarone_Middle, Benzbromarone_High, Clofibrate_Middle, Clofibrate_High, WY14643_Low, WY14643_High, WY14643_Middle, Gemfibrozil_Middle, Gemfibrozil_High are their regulatory DDs. Conclusions: Overall, the methods proposed in this article, co-cluster the genes and DDs as well as detect biomarker genes and their regulatory DDs simultaneously consuming less time compared to other mentioned methods. The results produced by the proposed methods have been validated by the available literature and functional annotation.

Enhanced Manhattan-based Clustering using Fuzzy C-Means Algorithm for High Dimensional Datasets

The problem of mining a high dimensional data includes a high computational cost, a high dimensional dataset composed of thousands of attribute and or instances. The efficiency of an algorithm, specifically, its speed is oftentimes sacrificed when this kind of dataset is supplied to the algorithm. Fuzzy C-Means algorithm is one which suffers from this problem. This clustering algorithm requires high computational resources as it processes whether low or high dimensional data. Netflix data rating, small round blue cell tumors (SRBCTs) and Colon Cancer (52,308, and 2,000 of attributes and 1500, 83 and 62 of instances respectively) dataset were identified as a high dimensional dataset. As such, the Manhattan distance measure employing the trigonometric function was used to enhance the fuzzy c-means algorithm. Results show an increase on the efficiency of processing large amount of data using the Netflix ,Colon cancer and SRCBT an (39,296, 38,952 and 85,774 milliseconds to complete the different clusters, respectively) average of 54,674 milliseconds while Manhattan distance measure took an average of (36,858, 36,501 and 82,86 milliseconds, respectively) 52,703 milliseconds for the entire dataset to cluster. On the other hand, the enhanced Manhattan distance measure took (33,216, 32,368 and 81,125 milliseconds, respectively) 48,903 seconds on clustering the datasets. Given the said result, the enhanced Manhattan distance measure is 11% more efficient compared to Euclidean distance measure and 7% more efficient than the Manhattan distance measure respectively.

Cuckoo Search Algorithm Based Feature Selection in Image Retrieval System

Efficiency an d retrieval time are very important issues in any content-based image retrieval system. In this study, an efficient image retrieval system was introduced depending on several features extracted from the database images, namely color moment (Mean, Standard Deviation), GLCM, and DWT( only LL-sub band). To increase the retrieval speed, Cuckoo search algorithm was used to select the important positions that contain full power features from the (LL-sub band). On using the Cuckoo search algorithm, only (50) important positions were chosen out of the total (24576) positions within (LL- sub band). These positions were stored for later use when entering a query image. Thus, the time taken to retrieve images was greatly reduced and this process also increased the efficiency of the system due to the fact that the selected positions gave the lowest distance measures between the query images and the similar images when evaluated using Manhattan distance measure. Two effectual performance measures (precision & recall) were used to calculate the accuracy of the system. The findings proved the system efficiency when compared to other previous works. Keywords: CBIR, Color Moment, GLCM, DWT, Cuckoo Search algorithm, Manhattan measure DOI : 10.7176/JEP/10-15-08 Publication date :May 31 st 2019