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Objective: To assess the rate of occurrence of sub-acute Stent Thrombosis (SAT) after primary PCI in the patients presented with the acute coronary syndrome. Study Design: Retrospective cross-sectional study. Place and Duration of Study: At Tertiary Cardiac Care Center of Lahore Pakistan, from Jul 2019 to Jun 2021. Methodology: The retrospective demographical and angiographic data of the admitted patients who underwent PCI for ACS,was gathered from the Hospital Registry of a tertiary cardiac care center of Lahore. The sampling technique was nonprobability consecutive sampling, and all the data was analyzed using SPSS 20.0 and continuous data was presented as Mean±SD and frequencies & percentages for categorical variables. Chi square test (CI=95%, α=5%) was used to find the association of SAT with predisposing factors. p-value < 0.05 was taken as statistically significant. Results: Out of (n=551) patient underwent PPCI, the stent thrombosis was observed to occur in 29(5.2 %), among which 4.9 % were subacute stent thrombosis. The predisposing factors of sub-acute stent thrombosis were Diabetes Mellitus, ACS, smaller vessel size, presence of bifurcation lesion, under sizing of the stent and edge dissection and were in significant association with SAT (p < 0.05). Conclusion: Our study reveals that subacute stent thrombosis is an event with a comparatively higher frequency in patients who go through primary PCI for acute coronary syndrome, demonstrating a frequency of 4.9% and a mortality rate of 74% within 30 days of the procedure.

Background: Percutaneous Coronary intervention (PCI) is a routinely performed procedure in coronary care units. The aim of our study was to investigate the frequency of major adverse cardiac events (MACEs) after percutaneous coronary angiography in acute ST-elevation myocardial infarction (STEMI) patients in our institution. The objective of the study is to determine the frequency of MACEs after Percutaneous Coronary Intervention (PCI) in patients with acute myocardial infarction.Patients & Methods: This prospective observational study was carried out at Punjab Institute of Cardiology, Lahore from May 2017 to June 2017. A total 35 patients with STEMI who underwent Primary PCI were enrolled by consecutive sampling technique. An inclusion criterion was chest pain of 30 minutes to 12 hours duration with ECG showing ≥ 0.1mm ST-elevation in at least two contiguous leads. An exclusion criterion was thrombolysis within last 24 hours, malignancy, stroke and Left Main or equivalent disease on coronary Angiogram. Patients were followed after 2 weeks till 1 month for any MACES. (Including re-admission, need for repeat revascularization, stent thrombosis, recurrent acute MI, angina, stroke, and mortality). Frequency and percentages were calculated for MACEs by using SPSS 23.0.Results: Out of 35 cases, 34 (97.1%) were male and 1 (2.9%) were female. Mean age was 47.11±10.59 years, 14 (40%) patients had hypertension, 10 (28.6%) were diabetics and 12(34.3%) were current smokers while 4 (11.4%) were ex-smokers, 10 (28.6%) had family history of CAD, and 3 (8.6%) had hyperlipidemia. Successful revascularization with TIMI-III flow was attained in 34(97.1%) cases. 33minutes was mean door to balloon time. At one month follow-up, out of 35 patients, angina was reported only in 1 (2.9%) patient. There was no readmission, repeat revascularization, stent thrombosis, myocardial infarction, stroke and death reported in study subjects.Conclusion: Successful revascularization by Primary PCI was associated with very few early MACEs. For the treatment of coronary artery disease, PCI is an effective option. It has a few early MACEs and uses less contrast and has fewer distal complications than conventional angioplasty and invasive procedures.

Background: Contrast induced nephropathy is a well-known entity but it is less known that whether cardiac disease is a risk factor for this complication. Contrast induced nephropathy (CIN) is related with increase mortality and morbidity and the chances of this complication rises in patients who have coronary artery disease (CAD). Objective: The objective of the study is to identify the frequency of contrast induced nephropathy in patients undergoing percutaneous coronary intervention who have normal baseline renal functions. Material and methods: This cross sectional study conducted at angiography department of Punjab Institute of Cardiology, Lahore over a period of 2 years from January 2015 to January 2017. Patients regardless of gender with age of 30 - 65 years, diabetic or non-diabetics were included. Patients with renal disease or serum creatinine > 2 mg/dl at presentation or any other co-morbid medical illness, LV ejection fraction 200ml, 612 (65.3%) patients. According to the age group following patients suffered from CIN: 30-40 years, 72 (8.6%) patients, 41-55 years, 288 (11.2 %) patients and 56-65 years 576 (28.5%) patients. Out of 2988 male patients, 324 (10.8 %) patients suffered from CIN (p=0.06). Similarly, out of 2412 females 612 (25.3 %) patients had CIN (p=0.05). In 936 patients suffering from CIN, 684(73.1%) patients were diabetic and 252 (26.9%) were non-diabetic. � Conclusion: There is a high frequency of contrast induced nephropathy in elderly and diabetic patients who undergo PCI even if they have normal preexisting renal functions, so this complication can be avoided by the minimum use of contrast during the procedure

Background: Pandemic caused by Corona virus infection has damaged the population throughout the universe. Cases of COVID-19 increased rapidly in whole world. COVID-19 has been associated with a number of cardiovascular co-morbidities including hypertension, ischemic heart disease, diabetes mellitus, dyslipidemia, atrial fibrillation and cardiac failure. Aims and objective: To determine the frequency of cardiovascular complications in patients with COVID-19. Material and methods: This observational cohort study was conducted in corona ward of DHQ Hospital, Gujrat from 1st January to 31st June 2021. Total 216 patients between 18-70 years of age, admitted who had COVID-19 confirmed by PCR after taking informed consent; patients already suffering from any cardiovascular, respiratory or other life threatening illness were excluded while hypertensive, diabetics and smoker were included in the study. Duration of admission was according to the severity of illness. Detailed history was taken from all patients followed by relevant examination. Investigations like ECG, echocardiographically and troponins were done to diagnose cardiovascular complications. All the patients were treated conservatively. Frequency of cardiovascular complications was noted. The patients who had clinical relief of symptoms, fever free, normal X-Ray chest and at least two consecutive negative PCR results for covid-19 were discharged. p value

Discovering structural, functional and evolutionary information in biological sequences have been considered as a core research area in Bioinformatics. Multiple Sequence Alignment (MSA) tries to align all sequences in a given query set to provide us ease in annotation of new sequences. Traditional methods to find the optimal alignment are computationally expensive in real time. This research presents an enhanced version of Bird Swarm Algorithm (BSA), based on bio inspired optimization. Enhanced Bird Swarm Align Algorithm (EBSAA) is proposed for multiple sequence alignment problem to determine the optimal alignment among different sequences. Twenty-one different datasets have been used in order to compare performance of EBSAA with Genetic Algorithm (GA) and Particle Swarm Align Algorithm (PSAA). The proposed technique results in better alignment as compared to GA and PSAA in most of the cases.

Background: Depression has a significant relationship with cardiovascular diseases. But evidence is scarce regarding its impact on adverse outcome in patients of non-obstructive coronary artery disease. Aim: To assess the association of depression with adverse outcome in patients with myocardial infarction with non-obstructive coronary arteries (MINOCA). Methods: This cohort study was conducted in the Department of Cardiology, Punjab Institute of Cardiology, Lahore for 12 months i.e. July 2017 to June 2018. 260 patients; 130 in exposed group and 130 in unexposed group fulfilling selection criteria were enrolled for the study with or without depression. Patients were then evaluated for adverse outcome. SPSS v. 25 was used to analyse the data. Results: The mean age of exposed group was 42.85±9.33 years and the mean age of unexposed group was 52.52± 8.18 years. There were 49(37.7%) males and 81(62.3%) females in exposed group with the male-to-female ratio was 1: 1.7, while in unexposed group there were 55(42.3%) males and 75(57.7%) females with the male-to-female ratio was 1: 1.4. ST-segment elevation was observed in 31(23.8%) exposed patients while in 27(20.8%) unexposed patients. In exposed group, cardiovascular events were noted in 44(33.8%) patients, while in 27(20.8%) patients in unexposed group (OR=1.362, 95% CI; 1.071, 1.731, P=0.018). In exposed group, cardiovascular related mortality were noted in 38(29.2%) patients, while in 17(13.1%) patients in unexposed group (OR=1.54, 95% CI; 1.22, 1.943, P = 0.001). In exposed group, all-cause mortality were noted in 19 (14.6%) patients, while in 9 (6.9%) patients in unexposed group (OR=1.418, 95% CI; 1.063, 1.892, p=0.045). Conclusions: Our study results showed that depression in patients having MINOCA is significantly related to adverse cardiovascular sequel and leads to more severe sequel. Keywords: Depression, adverse outcome, mortality, myocardial infarction, non-obstructive coronary arteries, MINOCA

Background: Pulmonary embolism is the lethal condition that is associated with higher rate of mortality in cardia patients. The diagnosis of the acute pulmonary embolism is frequently observed in patients presenting in emergency department or during hospitalization. Level of D-dimer may be assessed by blood test to help the physicians to diagnose the thrombosis. Literature showed variable evidence regarding predictive accuracy of D-dimer for detection of pulmonary embolism. So to get local data, we conducted this study. Aim: To determine the diagnostic accuracy of D-dimer assay for detection of pulmonary embolism in patients of acute myocardial infarction presenting in emergency department taking CTPA as gold standard Methods: Cross - sectional study conducted in Cardiology Department , Punjab Institute of Cardiology, Lahore for a period of six months from 1-9-2018 to 1-3-2019. One hundred patients, fulfilled the selection criteria were enrolled from emergency. Then blood sample was taken for evaluation of D-dimer level. Reports were checked and D-dimer level was noted. Pulmonary embolism was labeled as positive on D-dimer, if D-dimer level ≥500 and was labeled as negative if D-dimer level

Internet of things (IoT) and machine learning based systems incorporating smart wearable technology are rapidly evolving to monitor and manage healthcare and physical activities. This paper is focused on the proposition of a fog-centric wireless, real-time, smart wearable and IoT-based framework for ubiquitous health and fitness analysis in a smart gym environment. The proposed framework aims to aid in the health and fitness industry based on body vitals, body movement and health related data. The framework is expected to assist athletes, trainers and physicians with the interpretation of multiple physical signs and raise alerts in case of any health hazard. We proposed a method to collect and analyze exercise specific data which can be used to measure exercise intensity and its benefit to athlete’s health and serve as recommendation system for upcoming athletes. We determined the validity of the proposed framework by giving a six weeks workout plan with six days a week for workout activity targeting all muscles followed by one day for recovery. We recorded the electrocardiogram, heart rate, heart rate variability, breath rate, and determined athlete’s movement using a 3D-acceleration. The collected data in the research is used in two modules. A Health zone module implemented on body vitals data which categorizes athlete’s health state into various categories. Hzone module is responsible for health hazards identification and alarming. Outstandingly, the Hzone module is able to identify an athlete’s physical state with 97% accuracy. A gym activity recognition (GAR) module is implemented to recognize workout activity in real-time using body movements and body vitals data. The purpose of the GAR module is to collect and analyze exercise specific data. The GAR module achieved an accuracy of above 89% on athlete independent model based on muscle group.

Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, we discuss the segmentation and classification of the Magnetic resonance imaging (MRI) of Alzheimer’s disease, through the concept of transfer learning and customizing of the convolutional neural network (CNN) by specifically using images that are segmented by the Gray Matter (GM) of the brain. Instead of training and computing the proposed model accuracy from the start, we used a pre-trained deep learning model as our base model, and, after that, transfer learning was applied. The accuracy of the proposed model was tested over a different number of epochs, 10, 25, and 50. The overall accuracy of the proposed model was 97.84%.

Obesity is the fifth primary hazard for mortality in the world; hence different therapeutic targets are explored to overcome this problem. Endocannabinoid is identified as the emerging target for the treatment of obesity as Cannabinoid 1 (CB1) receptor over-activation resulted in abdominal obesity. Potent antagonists or inverse agonists for CB1 receptor are the new strategies to develop anti-obesity drugs. Here, ligand-based 3D-QSAR studies was performed on 100 analogues belonging to a class of 1,2,4-tirazole containing diarylpyrazolylcarboxamide as CB1 receptor antagonists. We developed three CoMFA models using different charge schemes, AM1BCC, Gasteiger-Huckle and MMFF. These models produced almost similar statistical results (q2cv = 0.725, 0.692, 0.719 and r2ncv = 0.929, 0.924, 0.928 for AM1BCC, Gasteiger-Huckle and MMFF, respectively). The said models were validated through 20 external test set compounds which resulted in significant r2pred values (r2pred = 0.747, 0.743 and 0.745 for AM1BCC, Gasteiger-Huckle and MMFF, respectively). Comparatively, AM1BCC model provided slightly better statistics among all three tested charges scheme models, hence AM1BCC model was further utilized to generate CoMSIA models considering different field combinations. The best selected CoMSIA model also produced substantial q2cv = 0.788, r2ncv = 0.916 and r2pred = 0.836 values. Furthermore, two new molecules were designed by modifying the same scaffolds on the basis contour map analysis. The activities of newly designed molecules were predicted through obtained CoMFA model ranked as better than their parent molecules. Moreover, these newly designed compounds were successfully docked on the complex crystal structure of CB1 receptor (PDB ID: 5XRA). The docked conformation of these newly designed inhibitor interacted with Ser173, His178, Lys192, Thr197 and Ser383 mainly by hydrophobic and pi-pi stacking interactions. The obtained results signify the potential of the developed model; suggesting that the models can be useful to test and design potent novel CB1 receptor antagonists or inverse agonists prior to the synthesis.

In this research we propose a framework that will strengthen the IoT devices security from dual perspectives; avoid devices to become attack target as well as a source of an attack. Unlike traditional devices, IoT devices are equipped with insufficient host-based defense system and a continuous internet connection. All time internet enabled devices with insufficient security allures the attackers to use such devices and carry out their attacks on rest of internet. When plethora of vulnerable devices become source of an attack, intensity of such attacks increases exponentially. Mirai was one of the first well-known attack that exploited large number of vulnerable IoT devices, that bring down a large part of Internet. To strengthen the IoT devices from dual security perspective, we propose a two step framework. Firstly, confine the communication boundary of IoT devices; IoT-Sphere. A sphere of IPs that are allowed to communicate with a device. Any communication that violates the sphere will be blocked at the gateway level. Secondly, only allowed communication will be evaluated for potential attacks and anomalies using advance detection engines. To show the effectiveness of our proposed framework, we perform couple of attacks on IoT devices; camera and google home and show the feasibility of IoT-Sphere.

Automatic tracking and quantification of exercises not only helps in motivating people but also contributes towards improving health conditions. Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. Excellent trackers are available for aerobic exercises but, in contrast, tracking free weight exercises is still performed manually. This study presents the details of our data acquisition effort using a single chest-mounted tri-axial accelerometer, followed by a novel method for the recognition of a wide range of gym-based free weight exercises. Exercises are recognized using LSTM neural networks and the reported results confirm the feasibility of the proposed approach. We train and test several LSTM-based gym exercise recognition models. More specifically, in one set of experiments, we experiment with separate models, one for each muscle group. In another experiment, we develop a universal model for all exercises. We believe that the promising results will potentially contribute to the vision of an automated system for comprehensive monitoring and analysis of gym-based exercises and create a new experience for exercising by freeing the exerciser from manual record-keeping.

Stroke is the second leading cause of death and disability worldwide, with ischemic stroke as the most common type. The preferred diagnostic procedure at the acute stage is the acquisition of multi-parametric magnetic resonance imaging (MRI). This type of imaging not only detects and locates the stroke lesion, but also provides the blood flow dynamics that helps clinicians in assessing the risks and benefits of reperfusion therapies. However, evaluating the outcome of these risky therapies beforehand is a complicated task due to the variability of lesion location, size, shape, and cerebral hemodynamics involved. Though the fully automated model for predicting treatment outcomes using multi-parametric imaging would be highly valuable in clinical settings, MRI datasets acquired at the acute stage are mostly scarce and suffer high class imbalance. In this paper, parallel multi-parametric feature embedded siamese network (PMFE-SN) is proposed that can learn with few samples and can handle skewness in multi-parametric MRI data. Moreover, five suitable evaluation metrics that are insensitive to imbalance are defined for this problem. The results show that PMFE-SN not only outperforms other state-of-the-art techniques in all these metrics but also can predict the class with a small number of samples, as well as the class with high number of samples. An accuracy of 0.67 on leave one cross out testing has been achieved with only two samples (minority class) for training and accuracy of 0.61 with the highest number of samples (majority class). In comparison, state-of-the-art using hand crafted features has 0 accuracy for minority class and 0.33 accuracy for majority class.

Dynamic route planning is a classical problem with interesting applications in optimizing car navigation, fleet management, urban evacuation planning, unmanned ground and airborne vehicle movement, and maritime route planning. While numerous traditional and heuristic-based algorithms have been proposed to address deficiencies in dynamic route planning, ongoing efforts are increasingly focusing on deriving scalable solutions from nature motivated schemes. The present work inspired by the behavior and prey hunting skills of sea predator, the hammerhead shark, seeks to devise a new dynamic route optimization algorithm. The proposed hammerhead shark optimization algorithm (HOA) finds destination in an unknown solution space by applying the natural route optimization behavior inherent in the hammerhead shark. Once the destination location is identified, HOA attempts to determine the path starting from source employing targeted movement towards the destination. The proposed algorithm can deal with static as well as dynamic environments. During validation, the proposed algorithm is performance tested against two state of the art algorithms A* and ant colony optimization (ACO). Attributes including the iteration count, path length (cells) and time complexity are compared. The results show that proposed algorithm outperforms A* in terms of latency, and ACO in terms of path optimality. The highly satisfactory performance of HOA leads us to recommend it as a suitable candidate for further adoption in optimal, scalable and dynamic path calculation concerns.

As the source code of various IoT botnet families including Mirai has been made publicly available, the adversaries are drastically introducing new variants of these IoT Botnet families. However, there is a lack of generic mechanism for the detection of these emerging variants. As a consequence, it is infeasible for security solution providers to effectively identify new variants of IoT botnets. In this paper, we have done static code analysis of 17 IoT botnet variants of family Mirai and Qbot in order to dig out the attacker's perspective, generic behavior, employed technologies and implemented techniques. With the help of this analysis, we have identified generic behavioral patterns of IoT botnets and have developed generic signatures for the identification of IoT botnets. These signatures includes identification on the basis of CPU architectures, Bot control commands, Bot scanning commands, obfuscation methods, botnet specific exploits and attacks. A comparative analysis of analyzed IoT-Botnet families has been presented. For the evaluation of identified signatures, we first tested them on unknown Mirai and Qbot variants and gained a detection rate of 100% for both the variants. Secondly, we tested those signatures on other IoT-Botnet families: IRC-Bot, Perl ShellBot, Trick-Bot and gained a detection rate of 98%, 96.79% and 98.2% respectively. Further, we have presented open research challenges in the field of IoT-Botnet detection. This research will enhance IoT botnets understanding and pave the way for generic detection and prevention methods of IoT botnets.

Starch/chitosan modified polyurethanes (PUs) were synthesized by step growth polymerization reaction between −NCO terminated prepolymer and chain extenders (1,4-Butanediol/starch/chitosan). Isophorone diisocyanate (IPDI) was reacted with hydroxyl-terminated polybutadiene (HTPB) to synthesize prepolymer and was further reacted with different moles ratio of starch/chitosan to produced five samples of polyurethane (PU). These samples were characterized by Fourier transformed infrared (FTIR) and Proton nuclear magnetic resonance (1H NMR) spectroscopy. The surface characterizations of PUs were done by scanning electron microscope (SEM). Thermogravimetric analysis showed that the thermal stability of PUs was higher when the mixture of both natural materials was used at equal amounts. It is concluded that combination of both starch and chitosan are efficient for the synthesis of PUs.

Feature subset selection is a process to choose a set of relevant features from a high dimensionality dataset to improve the performance of classifiers. The meaningful words extracted from data forms a set of features for sentiment analysis. Many evolutionary algorithms, like the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have been applied to feature subset selection problem and computational performance can still be improved. This research presents a solution to feature subset selection problem for classification of sentiments using ensemble-based classifiers. It consists of a hybrid technique of minimum redundancy and maximum relevance (mRMR) and Forest Optimization Algorithm (FOA)-based feature selection. Ensemble-based classification is implemented to optimize the results of individual classifiers. The Forest Optimization Algorithm as a feature selection technique has been applied to various classification datasets from the UCI machine learning repository. The classifiers used for ensemble methods for UCI repository datasets are the k-Nearest Neighbor (k-NN) and Na&iuml, ve Bayes (NB). For the classification of sentiments, 15&ndash, 20% improvement has been recorded. The dataset used for classification of sentiments is Blitzer&rsquo, s dataset consisting of reviews of electronic products. The results are further improved by ensemble of k-NN, NB, and Support Vector Machine (SVM) with an accuracy of 95% for the classification of sentiment tasks.

A number of different feature selection and classification techniques have been proposed in literature including parameter-free and parameter-based algorithms. The former are quick but may result in local maxima while the latter use dataset-specific parameter-tuning for higher accuracy. However, higher accuracy may not necessarily mean higher reliability of the model. Thus, generalized optimization is still a challenge open for further research. This paper presents a warzone inspired &ldquo, infiltration tactics&rdquo, based optimization algorithm (ITO)&mdash, not to be confused with the ITO algorithm based on the It&otilde, Process in the field of Stochastic calculus. The proposed ITO algorithm combines parameter-free and parameter-based classifiers to produce a high-accuracy-high-reliability (HAHR) binary classifier. The algorithm produces results in two phases: (i) Lightweight Infantry Group (LIG) converges quickly to find non-local maxima and produces comparable results (i.e., 70 to 88% accuracy) (ii) Followup Team (FT) uses advanced tuning to enhance the baseline performance (i.e., 75 to 99%). Every soldier of the ITO army is a base model with its own independently chosen Subset selection method, pre-processing, and validation methods and classifier. The successful soldiers are combined through heterogeneous ensembles for optimal results. The proposed approach addresses a data scarcity problem, is flexible to the choice of heterogeneous base classifiers, and is able to produce HAHR models comparable to the established MAQC-II results.

Publically available relational data without security protection may cause data protection issues. Watermarking facilitates solution for remote sharing of relational database by ensuring data integrity and security. In this research, a reversible watermarking for numerical relational database by using evolutionary technique has been proposed that ensure the integrity of underlying data and robustness of watermark. Moreover, mRMR based feature subset selection technique has been used to select attributes for implementation of watermark instead of watermarking whole database. Binary Bat algorithm has been used as constraints optimization technique for watermark creation. Experimental results have shown the effectiveness of the proposed technique against data tempering attacks. In case of alteration attacks, almost 70% data has been recovered, 50% in deletion attacks and 100% data is retrieved after insertion attacks. The watermarking based on evolutionary technique (WET) i.e., mRMR based Binary Bat Algorithm ensures the data accuracy and it is resilient against malicious attacks.

Urdu language uses cursive script which results in connected characters constituting ligatures. For identifying characters within ligatures of different scales (font sizes), Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) Network are used. Both network models are trained on formerly extracted ligature thickness graphs, from which models extract Meta features. These thickness graphs provide consistent information across different font sizes. LSTM and CNN are also trained on raw images to compare performance on both forms of inputs. For this research, two corpora, i.e. Urdu Printed Text Images (UPTI) and Centre for Language Engineering (CLE) Text Images are used. Overall performance of networks ranges between 90% and 99.8%. Average accuracy on Meta features is 98.08% while using raw images, 97.07% average accuracy is achieved.

Sentiment classification of social media has recently become popular among scientists due to the emergence of product reviews, blogs and social networking sites. A large number of reviews are difficult to evaluate personally. Moreover due to variable nature of reviews it becomes difficult, to compile overall result of reviews, to know which product is better than other. Researchers have already implemented machine learning techniques to analyze sentiment present in the given document. But execution time for these techniques increases due to the increase in feature set of data. Also irrelevant features participate in determining the sentiment of the given document, thereby varying the accuracy of the algorithm. In order to get much better classification, we propose a Biogeography based optimization algorithm to select optimal features set from given data. Then by using Naive Bayes and Support Vector Machine techniques, we perform sentiment classification of product reviews. The proposed technique can be applied to other classification problems where feature set is large.

Platelet-derived growth factor (PDGF) is one of the various growth factors, which involves in regulation of cell growth and division. In this work, 3D-quantitative structure–activity relationship studies of 75 quinazolines derivative as PDGFR’s inhibitor were performed. Based on the cognate ligand (PBD code: 3MJG 2.3 A), numerous alignment methods were used to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) models. Docked pose of the most active compound followed by database alignment led to derived best CoMFA model (q 2 = 0.531, r ncv 2 = 0.913). With the same alignment, a statistically reliable CoMSIA model with all the five fields was also derived (q 2 = 0.525, r ncv 2 = 0.889). A test set was used to validate both the models, which gave satisfactory predictive (r pred 2 ) values of 0.77 and 0.79, respectively. Contour maps of CoMFA and CoMSIA revealed the effect of important structural features on biological activity within the binding pocket and explained its interactions with ligand.

In this paper, we describe the formatting guidelines for IJCA Journal Submission. This paper presents a system for communication and control by disabled people based on automatic recognition of phonemes. This system allows users to navigate around an alphabet board by making phonemic utterances, thus enabling the user to spell out messages. Phoneme recognition provides an alternative to speech recognition technologies for people who have lost the ability to speak but remain capable of producing simple repeatable utterances. Voice Controlled Cellular Communication (V3C) aims at developing a voice-controlled tool for operating computer targeting physically handicapped and blind users having difficulties using a standard keyboard and mouse. It presents an interface that allows a user to activate any web page element through visual enumeration (Indexing) by an appropriate command. General Terms Computational Linguistics, Speech Recognition, Machine Learning et. al.

This research presents an optimization technique for route planning using simulated ant agents for dynamic online route planning and optimization of the route. It addresses the issues involved during route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated ant agent system (SAAS) is proposed using modified ant colony optimization algorithm for dealing with online route planning. It is compared with evolutionary technique on randomly generated environments, obstacle ratio, grid sizes, and complex environments. The evolutionary technique performs well in simple and less cluttered environments while its performance degrades with large and complex environments. The SAAS generates and optimizes routes in complex and large environments with constraints. The traditional route optimization techniques focus on good solutions only and do not exploit the solution space completely. The SAAS is shown to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints and its efficiency has been tested in a mine field simulation with different environment configurations and is capable of tracking the moving goal and performs equally well as compared to moving target search algorithm.

research presents a collaborative evolutionary planning framework for large scale grid exploration and planning problems. It caters for both dynamic and unknown environments using evolutionary techniques. In addition, we integrate the exploration and planning process in a unified framework using multi agent system. As a proof of success, we have developed extensive simulation with realistic obstacles and target. Our algorithm addresses the issues involved during such exploration and post exploration route planning. It acts as a controller and navigator for multiple agents and demonstrates the applicability for two different domains, Field Exploration and Route Planning. The EPF uses an optimized search algorithm for exploration phase and genetic algorithm for optimization of route in dynamic environments. The EPF can be used in different exploration and route planning problems but this paper focuses on obstacle detection and avoidance for its implementation.

This research presents an optimization technique for route planning using simulated ant agents for dynamic online route planning and optimization of the route. It addresses the issues involved during route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated ant agent system (SAAS) is proposed using modified ant colony optimization algorithm for dealing with online route planning. It is compared with evolutionary technique on randomly generated environments, obstacle ratio, grid sizes, and complex environments. The SAAS generates and optimizes routes in complex and large environments with constraints. The SAAS is shown to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints and its efficiency has been tested in a mine field simulation.

Structure-based 3D-QSAR study was performed on a class of 5-benzylidene-2-phenylthiazolinones non-redox type 5-LOX inhibitors. In this study, binding pocket of 5-Lipoxygenase (pdb id 3o8y) was identified by manual docking using 15-LOX (pdb id 2p0m) as a reference structure. Additionally, most of the binding site residues were found conserved in both structures. These non-redox inhibitors were then docked into the binding site of 5-LOX. To generate reliable CoMFA and CoMSIA models, atom fit data base alignment method using docked conformation of the most active compound was employed. The q(2)cv and r(2)ncv values for CoMFA model were found to be 0.549 and 0.702, respectively. The q(2)cv and r(2)ncv values for the selected CoMSIA model comprised four descriptors steric, electrostatic, hydrophobic and hydrogen bond donor fields were found to be 0.535 and 0.951, respectively. Obtained results showed that our generated model was statistically reliable. Furthermore, an external test set validates the reliability of the predicted model by calculating r(2)pred i.e.0.787 and 0.571 for CoMFA and CoMSIA model, respectively. 3D contour maps generated from CoMFA and CoMSIA models were utilized to determine the key structural features of ligands responsible for biological activities. The applied protocol will be helpful to design more potent and selective inhibitors of 5-LOX.

Most of the data mining projects generate information (summarized in the form of graphs and charts) for business executives and decision makers; however it leaves to the choice of decision makers either to use it or disregard it. The manual use of the extracted knowledge limits the effectiveness of data mining technology considerably. This chapter proposes an architecture, in which data mining module is utilized to provide continuous supply of knowledge to a rule based expert system. Proposed approach solves the knowledge acquisition problem of rule based systems and also enhances effective utilization of data mining techniques (i.e. by supplying extracted knowledge to rule based system for automated use). The chapter describes the details of a data mining driven rule based expert system applied in medical billing domain. Main modules of the system along with the final analysis of performance of the system have also been presented.

This paper proposes an automated sign language to speech interpreter that begins by capturing the 3D video stream through Kinect and the joints of interest in the human skeleton are then worked upon. The proposed system deals with the problems faced by mute people in conveying their message through Pakistani sign language. This research makes use of the 3D trajectory algorithm for processing the normalized data. Performed gestures are classified using the robust learning technique of ensemble. Once recognized, the gestures are translated to speech. This system has been tested on several signs taken from PSL, demonstrating the real time practicality of using ASLSI.

Most of the research in the area of performance evaluation of rule-based expert systems (RBESs) is focused on verification and validation issues. Many researchers discuss usability, usefulness, portability, and response time for the evolution of RBES. The final goal of all such studies is to construct a system with optimal, accurate knowledge base. Arguably, a system with best knowledge base is actually worthless if it is never utilized in the real world. We have proposed “benefit” as a measure of evaluation and suggested some guidelines for performance evaluation of RBESs. The proposed measure has been demonstrated for performance evaluation of an RBES applied in the medical billing domain. Results showed that the system has saved hundreds of working hours during the evaluation period of 3 months. Moreover, other associated measures have also been considered. Associated measures in the medical billing domain are “claim rejection rate”—reduced by 54%—and “claim aging,” which has decreased from 34 to 28 days due to the RBES. Guidelines proposed by this research can be applied for the evaluation of expert systems implemented in other application domains, including in the first place business decision support systems.

Software testing is one of the essential parts of the software development life cycle. In software industry, the testing cost can be approximately 50% of the total cost of a software project so efficient ways of testing software are crucially important in reducing costs, time and effort. There are two major methods of software testing; black-box testing (focuses only what the software can do) and white-box testing (tests the internal structure of the software under consideration thoroughly and the ultimate goal is to write test cases that force the program coverage.) For program coverage, identification of suitable paths is one of the major software testing problems. These test paths are known as test sequences. Generation of automated and effective test sequences is also a very difficult task in software testing process. In the proposed work, the problem “Test Sequence Generation” is considered as a multi-objective optimization problem by having two objectives to be optimized simultaneously, Oracle Cost, and Path Priority. In real time environment, there are many constraints which have to be fulfilled when dealing with an effective testing. So, such test sequences that meet multiple objectives simultaneously are generated in order to reduce the testing efforts. To solve this problem a recently developed algorithm “Multi-Objective Firefly Algorithm (MOFA)” is used. The problem “Test Sequence Generation” is first implemented by Firefly Algorithm and later by using MOFA-considering the problem as Multi-Objective Optimization Problem. The proposed technique implementing test sequences with multiple (two) objectives and its results are presented.

Requirement Engineering (RE) is considered as an important part in Software Development Life Cycle. It is a traditional Software Engineering (SE) process. The goal of RE is to Identify, Analyze, Document and Validate requirements. Requirement Prioritization is a crucial step towards making good decisions about product plan but it is often neglected. It is observed that in many cases the product is considered as a failure without proper prioritization because it fails to meet its core objectives. When a project has tight schedule, restricted resources, and customer expectations are high then it is necessary to deploy the most critical and important features as early as possible. For this purpose requirements are prioritized. Several requirement prioritization techniques have been presented by various researchers over the past years in the domain of SE as well as Computational Intelligence. A new technique is presented in this paper which is a hybrid of both domains named as FuzzyHCV. FuzzyHCV is a hybrid of Hierarchical Cumulative Voting (HCV) and Fuzzy Expert System. Comparative analysis is performed between new technique and an existing HCV technique. Result shows that proposed technique has proved to be more reliable and accurate.

This research presents an optimization technique for multiple routes generation using simulated niche based particle swarm optimization for dynamic online route planning, optimization of the routes and proved to be an effective technique. It effectively deals with route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated niche based particle swarm optimization (SN-PSO) is proposed using modified particle swarm optimization algorithm for dealing with online route planning and is tested for randomly generated environments, obstacle ratio, grid sizes, and complex environments. The conventional techniques perform well in simple and less cluttered environments while their performance degrades with large and complex environments. The SN-PSO generates and optimizes multiple routes in complex and large environments with constraints. The traditional route optimization techniques focus on good solutions only and do not exploit the solution space completely. The SN-PSO is proved to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints. The efficiency of the SN-PSO is tested in a mine field simulation with different environment configurations and successfully generates multiple feasible routes.

This paper presents a novel digital watermarking algorithm using Contourlet and Discrete Cosine transforms to perceptually shape the watermark by taking care of two conflicting parameters imperceptibility and robustness. Multi Objective Genetic Algorithm is used to decide the optimal watermarking strength (a). Contourlet transform is used to get better quality by taking care of Human Visual System and better robustness under various kinds of linear and non-linear filtering attacks. Discrete Cosine Transform with Zigzag scanning is used to detect the watermark blindly. The watermark embedding process is applied over the sub-band coefficients that lie on the edges and around the edges where distortions are less noticeable. The proposed algorithm provides an excellent tradeoff between robustness and imperceptibility and it is image adaptive. The experimental results show that the proposed algorithm improved the resistance to attacks especially JPEG compression with 5% quality, Median filter with a mask of 6 × 6, resizing up to 20% and various non-linear filtering attacks. Motivation of presented work is to adapt a watermark sequence to those frequency components of an image which provide a transparent and robust watermark.

One of the most promising uses for multi agent systems is the searching for items or resources in unknown environments. The use of multi agent systems to locate unexploded ordinance proves to be an excellent example of one such application. This research explores the possibility of a hybrid architecture that implements mine detection, obstacle avoidance and route planning with a group of autonomous agents with coordination capabilities. Groups of inter cooperating multi agents working towards a common goal have the potential to perform a task faster and with an increased level of efficiency then the same number of agents acting in an independent manner. This coordination framework will address the issues involved during such unknown exploration

In this study we present a sub-swarm based particle swarm optimization algorithm for niching (NSPSO). The NSPSO algorithm is capable of locating and maintaining a sufficient number of niches throughout the execution of the algorithm. The niches which are identified are then exploited by using a sub-swarm strategy which tries to refine the niche and converge to an optimum solution. NSPSO is capable of locating multiple solutions and is well suited for multimodal optimization problems. From the experimentation results, we have observed that NSPSO is quite efficient in locating both global and local optima. We present a comparison of the performance of NSPSO with NichePSO and SPSO.

One of the most promising uses for multi agent systems is the searching for items or resources in unknown environments. The use of multi agent systems to locate unexploded ordinance proves to be an excellent example of one such application. This research explores the possibility of a hybrid architecture that implements mine detection, obstacle avoidance and route planning with a group of autonomous agents with coordination capabilities. Groups of inter cooperating multi agents working towards a common goal have the potential to perform a task faster and with an increased level of efficiency then the same number of agents acting in an independent manner. This coordination framework will address the issues involved during such unknown exploration