Data mining using L-fuzzy concept analysis.

Association rules in data mining are implications between attributes of objects that hold in all instances of the given data. These rules are very useful to determine the properties of the data such as essential features of products that determine the purchase decisions of customers. Normally the data is given as binary (or crisp) tables relating objects with their attributes by yes-no entries. We propose a relational theory for generating attribute implications from many-valued contexts, i.e, where the relationship between objects and attributes is given by a range of degrees from no to yes. This degree is usually taken from a suitable lattice where the smallest element corresponds to the classical no and the greatest element corresponds to the classical yes. Previous related work handled many-valued contexts by transforming the context by scaling or by choosing a minimal degree of membership to a crisp (yes-no) context. Then the standard methods of formal concept analysis were applied to this crisp context. In our proposal, we will handle a many-valued context as is, i.e., without transforming it into a crisp one. The advantage of this approach is that we work with the original data without performing a transformation step which modifies the data in advance

Behavioural shift of estuarine mudcrab as biomarker of arsenic exposure in Sundarbans estuary of West Bengal

Mudcrab Scylla serrata (Crustacea: Decapoda) in an ecologically and economically important species of Sundarbans Biosphere Reserve was studied for its behaviour under the exposure of toxic arsenic - a common xenobiotic of this area. The behavioural profile of aquatic animals exposed to diverse toxicants are considered as an index to estimate the degree and nature of stress experienced by the animals both in nature and in experimental conditions. Present investigation involved study of selected behavioural shift of S. serrata under the sublethalconcentrations of 1, 2 and 3 ppm of sodium arsenite for 1, 2, 3 and 4 days in controlled laboratory condition. Exposure to arsenic resulted an appearance of selected abnormal behavioural manifestation including tendency of avoidance, hypersecretion of mucoid element and release of excess excretory products. Toxin induced alteration of studied behaviour is indicative to possible shift in the overall physiological functions and biological activities of this important species in its natural habitat. Chronic exposure to 3 ppm of sodium arsenite for 30 days may lead to decline this economically important species in Sundarbans Biosphere Reserve

Pregnancy outcome in anti-thyroid peroxidase antibody negative subclinical hypothyroid women with and without treatment

Background: There is insufficient evidence in the literature whether pregnancy with anti-thyroid peroxidase antibody negative subclinical hypothyroidism is benefited with treatment.Methods: The 100 uncomplicated primigravida women before 16 weeks of gestation who were anti thyroid peroxidase antibody negative and diagnosed as subclinical hypothyroid based on serum thyroid stimulating hormone (TSH), Free T4 (FT4) and anti-thyroid peroxidase antibody (anti TPO Ab) were enrolled in this study. They were divided into case and control group having 50 patients in each arm. Case group were treated with levothyroxine therapy as per the recommended dose. Maternal and perinatal outcome were compared between the two groups.Results: In our study we had found increased percentage of cases of antepartum hemorrhage (APH), pregnancy induced hypertension (PIH), pre-labour rupture of membrane (PROM), preterm delivery, meconium-stained liquor, intrauterine growth restriction (IUGR), low birth weight (LBW), APGAR score at 1 and 5 minutes, neonatal hyperbilirubinemia and NICU admission among women who were not treated but it was not statistically significant when compared with control group.Conclusions: When compared between the treated and non-treated group in anti TPO Ab negative subclinical hypothyroid patients, we didn’t find any significant difference in parameters studied by us. In view of inadequate literature, controversy exists whether to treat or not anti TPO negative subclinical hypothyroidism in pregnancy with levothyroxine

Toward Building an Intelligent and Secure Network: An Internet Traffic Forecasting Perspective

Internet traffic forecast is a crucial component for the proactive management of self-organizing networks (SON) to ensure better Quality of Service (QoS) and Quality of Experience (QoE). Given the volatile and random nature of traffic data, this forecasting influences strategic development and investment decisions in the Internet Service Provider (ISP) industry. Modern machine learning algorithms have shown potential in dealing with complex Internet traffic prediction tasks, yet challenges persist. This thesis systematically explores these issues over five empirical studies conducted in the past three years, focusing on four key research questions: How do outlier data samples impact prediction accuracy for both short-term and long-term forecasting? How can a denoising mechanism enhance prediction accuracy? How can robust machine learning models be built with limited data? How can out-of-distribution traffic data be used to improve the generalizability of prediction models? Based on extensive experiments, we propose a novel traffic forecast/prediction framework and associated models that integrate outlier management and noise reduction strategies, outperforming traditional machine learning models. Additionally, we suggest a transfer learning-based framework combined with a data augmentation technique to provide robust solutions with smaller datasets. Lastly, we propose a hybrid model with signal decomposition techniques to enhance model generalization for out-of-distribution data samples. We also brought the issue of cyber threats as part of our forecast research, acknowledging their substantial influence on traffic unpredictability and forecasting challenges. Our thesis presents a detailed exploration of cyber-attack detection, employing methods that have been validated using multiple benchmark datasets. Initially, we incorporated ensemble feature selection with ensemble classification to improve DDoS (Distributed Denial-of-Service) attack detection accuracy with minimal false alarms. Our research further introduces a stacking ensemble framework for classifying diverse forms of cyber-attacks. Proceeding further, we proposed a weighted voting mechanism for Android malware detection to secure Mobile Cyber-Physical Systems, which integrates the mobility of various smart devices to exchange information between physical and cyber systems. Lastly, we employed Generative Adversarial Networks for generating flow-based DDoS attacks in Internet of Things environments. By considering the impact of cyber-attacks on traffic volume and their challenges to traffic prediction, our research attempts to bridge the gap between traffic forecasting and cyber security, enhancing proactive management of networks and contributing to resilient and secure internet infrastructure

DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction

Internet traffic volume estimation has a significant impact on the business policies of the ISP (Internet Service Provider) industry and business successions. Forecasting the internet traffic demand helps to shed light on the future traffic trend, which is often helpful for ISPs decision-making in network planning activities and investments. Besides, the capability to understand future trend contributes to managing regular and long-term operations. This study aims to predict the network traffic volume demand using deep sequence methods that incorporate Empirical Mode Decomposition (EMD) based noise reduction, Empirical rule based outlier detection, and $K$-Nearest Neighbour (KNN) based outlier mitigation. In contrast to the former studies, the proposed model does not rely on a particular EMD decomposed component called Intrinsic Mode Function (IMF) for signal denoising. In our proposed traffic prediction model, we used an average of all IMFs components for signal denoising. Moreover, the abnormal data points are replaced by $K$ nearest data points average, and the value for $K$ has been optimized based on the KNN regressor prediction error measured in Root Mean Squared Error (RMSE). Finally, we selected the best time-lagged feature subset for our prediction model based on AutoRegressive Integrated Moving Average (ARIMA) and Akaike Information Criterion (AIC) value. Our experiments are conducted on real-world internet traffic datasets from industry, and the proposed method is compared with various traditional deep sequence baseline models. Our results show that the proposed EMD-KNN integrated prediction models outperform comparative models.Comment: 13 pages, 9 figure

A comprehensive in vitro biological investigation of metal complexes of tolfenamic acid

Objective: The inquisitive objective of the study was to observe the antimicrobial, cytotoxicity, and antioxidant activities of some newly synthesized metal complexes of tolfenamic acid.Methods: While antimicrobial activity was studied by disk diffusion method, cytotoxicity was studied by performing brine shrimp lethality bioassay. Moreover, DPPH radical scavenging potential was observed to determine the antioxidant property of the complexes.Results: From the disk diffusion antimicrobial screening of tolfenamic acid and its metal complexes, it was found out that considerable antimicrobial activity in terms of zone of inhibition against the tested organisms had been demonstrated by Cu and Zn complex of tolfenamic acid. In addition, the brine shrimp lethality bioassay corroborated that tolfenamic acid and Cu, Co, Zn complexes of the parent NSAID exhibited cytotoxicity with LC50 values 1.23 ± 0.91 lg/ml, 1.12 ± 0.12 lg/ml, 1.17 ± 0.56 lg/ml, 1.35 ± 0.24 lg/ ml respectively, compared to the vincristine sulfate had LC50 value of 0.82 ± 0.09 lg/ml. Furthermore, 1,1- diphenyl-2-picrylhydrazyl assay revealed that in comparison with standard BHT had IC50 of 11.84 ± 0.65, Cu and Co complex of tolfenamic acid exhibited significant antioxidant or radical-scavenging properties with IC50 values 13.61 ± 0.58 lg/ml and 15.38 ± 0.09 lg/ml, respectively.Conclusion: It can be postulated that metal complexes of tolfenamic acid have auspicious pharmacological effects: antimicrobial, cytotoxicity, and antioxidant potency. Hence, these complexes might have better therapeutic responses in future; notwithstanding, it needs further detailed analysis in other pharmacological perspectives.Keywords: Tolfenamic acid, Metal complex, Antimicrobial screening, Cytotoxicity, Antioxidant activit

Variable sampling interval run sum X‾ chart with estimated process parameters

The X‾ type control chart is often evaluated by assuming the process parameters are known. However, the exact values of process parameters are hardly known and thus Phase-I dataset is needed to estimate them. In this paper, the performance of the variable sampling interval run sum X‾ chart with estimated process parameters is evaluated by using the performance measure of the average of the average time to signal (AATS) and the optimal design of the proposed chart in minimizing the out-of-control AATS is developed. The performance measure of the standard deviation of the average time to signal (SDATS) is then used to identify the number of Phase-I samples (w) needed to have an in-control AATS performance close to its known process parameter case. Results show that large w is needed to minimize the performance gap between known and unknown process parameters cases of the VSI RS X‾ chart

Familial Aggregation of Vibrio cholerae-associated Infection in Matlab, Bangladesh

Vibrio cholerae is a major cause of diarrhoeal illness in endemic regions, such as Bangladesh. Understanding the factors that determine an individual's susceptibility to infection due to V. cholerae may lead to improved prevention and control strategies. Increasing evidence suggests that human genetic factors affect the severity of V. cholerae-associated infection. This study, therefore, sought to characterize the heritable component of susceptibility to infection due to V. cholerae using the Matlab Health and Demographic Surveillance System database of the International Centre for Diarrhoeal Disease Research, Bangladesh. In total, 144 pedigrees that included a cholera patient and 341 pedigrees without a cholera patient were evaluated during 1 January–31 December 1992. The odds of the sibling of a patient being admitted with cholera were 7.67 times the odds of the sibling of an unaffected individual being admitted with cholera [95% confidence interval (CI) 2.40–24.5, p<0.001], after adjustment for gender, age, socioeconomic status, and hygiene practices. Although exposure to environmental reservoirs is essential in the epidemiology of cholera, household-specific factors, such as familial relatedness to an index case, may also be important determinants of risk of cholera. Further analysis of human genetic factors that contribute to susceptibility to cholera may be productive

Glycosylation of Erythrocyte Spectrin and Its Modification in Visceral Leishmaniasis

Using a lectin, Achatinin-H, having preferential specificity for glycoproteins with terminal 9-O-acetyl sialic acid derivatives linked in α2-6 linkages to subterminal N-acetylgalactosamine, eight distinct disease-associated 9-O-acetylated sialoglycoproteins was purified from erythrocytes of visceral leishmaniaisis (VL) patients (RBCVL). Analyses of tryptic fragments by mass spectrometry led to the identification of two high-molecular weight 9-O-acetylated sialoglycoproteins as human erythrocytic α- and β-spectrin. Total spectrin purified from erythrocytes of VL patients (spectrinVL) was reactive with Achatinin-H. Interestingly, along with two high molecular weight bands corresponding to α- and β-spectrin another low molecular weight 60 kDa band was observed. Total spectrin was also purified from normal human erythrocytes (spectrinN) and insignificant binding with Achatinin-H was demonstrated. Additionally, this 60 kDa fragment was totally absent in spectrinN. Although the presence of both N- and O-glycosylations was found both in spectrinN and spectrinVL, enhanced sialylation was predominantly induced in spectrinVL. Sialic acids accounted for approximately 1.25 kDa mass of the 60 kDa polypeptide. The demonstration of a few identified sialylated tryptic fragments of α- and β-spectrinVL confirmed the presence of terminal sialic acids. Molecular modelling studies of spectrin suggest that a sugar moiety can fit into the potential glycosylation sites. Interestingly, highly sialylated spectrinVL showed decreased binding with spectrin-depleted inside-out membrane vesicles of normal erythrocytes compared to spectrinN suggesting functional abnormality. Taken together this is the first report of glycosylated eythrocytic spectrin in normal erythrocytes and its enhanced sialylation in RBCVL. The enhanced sialylation of this cytoskeleton protein is possibly related to the fragmentation of spectrinVL as evidenced by the presence of an additional 60 kDa fragment, absent in spectrinN which possibly affects the biology of RBCVL linked to both severe distortion of erythrocyte development and impairment of erythrocyte membrane integrity and may provide an explanation for their sensitivity to hemolysis and anemia in VL patients