Search

To develop a machine-based algorithm from clinical and demographic data, physical activity and glucose variability to predict hyperglycaemic and hypoglycaemic excursions in patients with type 2 diabetes on multiple glucose lowering therapies who fast during Ramadan.Thirteen patients (10 males and three females) with type 2 diabetes on 3 or more anti-diabetic medications were studied with a Fitbit-2 pedometer device and Freestyle Libre (Abbott Diagnostics) 2 weeks before and 2 weeks during Ramadan. Several machine learning techniques were trained to predict blood glucose levels in a regression framework utilising physical activity and contemporaneous blood glucose levels, comparing Ramadan to non-Ramadan days.The median age of participants was 51 years (IQR 49-52); median BMI was 33.2 kg/mXGBoost, a machine learning AI algorithm achieves high predictive performance for normal and hyperglycaemic excursions, but has limited predictive value for hypoglycaemia in patients on multiple therapies who fast during Ramadan.

Background: There are currently no treatment algorithms specifically for early peri-prosthetic joint infection (PJI) after hemiarthroplasty for hip fracture. Commonly, debridement, antibiotics, and implant retention (DAIR) is attempted as first-line management, despite lack of evidence supporting this strategy in this patient group. The purpose of this study was to evaluate outcomes of DAIR for early PJI after hemiarthroplasty for hip fracture in our unit. Methods: The departmental database from December 2008 to January 2019 was searched to identify all patients in our unit who were treated for early PJI after hemiarthroplasty for hip fracture. Data for included patients were collected from electronic healthcare records and analyzed. Primary outcome measure was treatment success, defined as patient survival to discharge, with eradication of infection and implant retention. Results: Twenty-six patients were identified and included in the study. Mean age was 84.7 years. All except one patient were American Society of Anesthesiologists (ASA) class 3 or 4. All patients were McPherson host grade B or C. Twenty-three of 26 patients underwent DAIR and three of 26 proceeded directly to excision arthroplasty. Debridement, antibiotics, and implant retention was successful in three of 23 patients (13%) after a single procedure, with success in two additional patients after a second procedure, giving overall success rate of five of 23 patients (22%). Conclusions: Debridement, antibiotics, and implant retention has a high failure rate in treating early PJI after hemiarthroplasty for hip fracture. These patients are generally elderly and frail with multiple host and wound compromising factors. Debridement, antibiotics, and implant retention should not be recommended as first-line management for the majority of these patients, for whom getting it right the first time is of vital importance to avoid consequences associated with failed surgical procedures. Further multicenter studies that also explore alternate treatment strategies are required to devise an algorithm specifically for hip fracture patients, to aid decisions on treatment and improve outcomes.

The use of constrained Total Hip Replacements (THR) is controversial due to lack of definite indications and potentially high failure rates because of mechanical loosening or component failure. A review was performed to assess a departmental use of a single constrained acetabular component over a ten years period.Patient demographics, operative indications, complications and patient follow-up were recorded. Post-operative Oxford Hip Scores (OHS) were obtained via a combination of New Zealand Joint Registry interrogation and telephonic questioning. Cup version and inclination angles were obtained from standardised anteroposterior radiographs using established techniques.Forty-four constrained components (in 39 patients) were implanted between 2005 and 2014. The mean age was 78 years with mean ASA 2.7 and mean follow-up 37.2 months (range 13-116). The mean post-operative OHS was 36 (SD 9.25), and there were 4 failures (3 dislocations and 1 peri-prosthetic fracture). The 3 dislocations had either cup ante version (AV) or inclination angles (IA) outside the data set interquartile range (AV 13-24°, IA 40-50°). The cup inclination was significantly lower (p0.01) in patients with pain on sitting. At post-operative follow-up, 14/39 patients had died from unrelated causes, with only 1 patient surviving beyond 6 years.Constrained acetabular components offer a solution to hip instability in a difficult group of patients. This study has shown good medium-term outcomes of a single component type in a predominantly frail group of low demand patients. Despite constraint, correct cup placement (particularly inclination) remains important to prevent dislocation or poor reported outcome.

Approximately 350 million patients of hepatitis B and 170 million patients of Hepatitis C are present worldwide according to WHO. Many risk factors are involved in the transmission of theses deadly viral infections but blood transfusion in Beta thalassemic patients is working with two faces, one as remedy and the other is key risk factor in the spread of silent killers. Thalassemia patients registered in Combine Military Hospital (CMH) Rawalakot and Sheikh Khalifa Bin Zayed Al-Nahyan Hospital, Muzaffarabad Azad Jammu and Kashmir Pakistan were studied for the viral hepatitis B and C prevalence. A total of 303 (including 164 males and 139 females) individuals, aged between 1 and 12 years were studied. All the understudy participants were interviewed through questionnaire method. After taking written consent from each participant or guardian, 5 ml of blood was collected from each participant and brought to the working laboratory for HBV and HCV screening through ICT kit method. All ICT positive samples were further confirmed through ELISA. Individuals 25(8.2%) were found positive for both hepatitis B surface Antigen (HBsAg) and Anti hepatitis C antibody (Anti-HCV antibody) after initial screening with no coinfection of both diseases. Out of 25 total infected individuals, 05(1.6%) were found HBsAg positive and 20(6.6%) were found anti-HCV positive. All the ICT positive individuals were further confirmed by quantitative Enzyme Linked Immunosorbent Assay (ELISA) and 23(7.6%) individuals were confirmed for both hepatitis B and C including 05(1.6%) HBsAg positive as well as 18(5.9%) anti-HCV antibody positive individuals. We can conclude that 8.2% prevalence of hepatitis B and C among thalassemic patients is an alarming health concern which directly indicates to pay attention for ensuring 100% safe blood transfusion.

Neck of femur fractures are the most prevalent type of injury in elderly trauma patients. Both intra and extra capsular type of fractures are equally distributed in the given population. Traditionally, Extra capsular fractures are fixed with Dynamic Hip screw or Intra medullary nailing based on the type of fracture. NICE (National Institute of Clinical Excellence) recommends fixing 31-A1 and 31-A2 fractures with DHS (Dynamic Hip Screw) whereas AO recommends fixing 31-A1 with DHS and 31-A2.1 subtype with DHS and 31-A2.2 and 31-A2.3with IMN (Intra medullary nail). In regional trauma centre 178 patients, 125 females and 53 males with extra capsular neck of femur fractures fixed were selected in a retrospective study. The data was spanning over a period of 1 year. Fractures were classified as per AO classification by two registrars. The implant selection was analysed in terms of the short term out come to find out the cost effectiveness of one over the other. The quality of reduction was assessed as per standard criteria and consideration of lateral femoral wall thickness was taken into account to assess the stability of fracture. The study found more risk of peri prosthetic fractures associated with Intra medullary nailing as compared to Dynamic Hip screw and more risk of Varus collapse was found to be associated with DHS as compared to IM Nail. Moreover, despite of Nail being costly as compared to DHS, the study did not reveal its superiority in terms of inpatient hospital stay. In appropriately selected patient DHS provides results in terms of hospital stay, revision rate and wound complications comparable to IM Nail in the short term justifying its use in the above-mentioned fracture patterns as per the standard National Institute of clinical Excellence guidelines.

Despite growing concerns over pathological internet usage, studies based on validated psychometric instruments are still lacking in Pakistan. This study aimed to examine the psychometric properties of the Internet Addiction Test (IAT) in a sample of Pakistani students. A total of 522 students of medicine and dentistry completed the questionnaire, which consisted of four sections: (a) demographics, (b) number of hours spent on the Internet per day, (c) English version of the IAT, and (d) the Defense Style Questionnaire-40. Maximum likelihood analysis and principal axis factoring were used to validate the factor structure of the IAT. Convergent and criterion validity were assessed by correlating IAT scores with number of hours spent online and defense styles. Exploratory and confirmatory factor analysis reflected the goodness of fit of a unidimensional structure of the IAT, with a high alpha coefficient. The IAT had good face and convergent validity and no floor and ceiling effects, and was judged easy to read by participants.

Road accidents cannot be stopped despite providing the best possible roads and intersections, however there are ways to reduce the impact of road accidents on road-users and the vehicles plying on the road.The incidence of accidental deaths has shown an increasing trend during the period 2005 - 2015 with an increase of 54.3 in the year 2015 as compared to 2005 increase in the rate of accidental deaths during the same period was 25.5.A total of 4,00,517 accidental deaths were reported in the country during 2015 (5,535 more than such deaths reported in 2014) showing an increase of 1.4 as compared to 2014. However, the average rate of Accidental Deaths has remained same 32.6 in 2014 and 2015.In the stretches we studied the road accidents are increasing rapidly .We studied accidental records of various police-stations ,identified the black-spots of accidents and then analyzed the geometric features of those spots whose observation is given in this paper .The identification of such points provides us ease to work on some section of road which is most prone to accidents .We analyzed the geometric deficiencies and they recommended ways to reduce their affects. The findings indicated that large radii right-turn curves were more dangerous than left curves, in particular, during lane changing maneuvers. However sharper curves are more dangerous in both left and right curves. Moreover, motorway carriageways with no or limited shoulders have the highest CR when compared to other carriageway width. Proper traffic guidance and control system to guide road users ensuring safe movement of vehicles has been recommended and some of the facilities such as pedestrian crossings and median openings, acceleration and deceleration lanes were re-designed in order to improve the safety of the road and minimize the accidents.

P300 is a positive event-related potential used by P300-brain computer interfaces (BCIs) as a means of communication with external devices. One of the main requirements of any P300-based BCI is accuracy and time efficiency for P300 extraction and detection. Among many attempted techniques, independent component analysis (ICA) is currently the most popular P300 extraction technique. However, since ICA extracts multiple independent components (ICs), its use requires careful selection of ICs containing P300 responses, which limits the number of channels available for computational efficiency. Here, we propose a novel procedure for P300 extraction and detection using constrained independent component analysis (cICA) through which we can directly extract only P300-relevant ICs. We tested our procedure on two standard datasets collected from healthy and disabled subjects. We tested our procedure on these datasets and compared their respective performances with a conventional ICA-based procedure. Our results demonstrate that the cICA-based method was more reliable and less computationally expensive, and was able to achieve 97 and 91.6% accuracy in P300 detection from healthy and disabled subjects, respectively. In recognizing target characters and images, our approach achieved 95 and 90.25% success in healthy and disabled individuals, whereas use of ICA only achieved 83 and 72.25%, respectively. In terms of information transfer rate, our results indicate that the ICA-based procedure optimally performs with a limited number of channels (typically three), but with a higher number of available channels (>3), its performance deteriorates and the cICA-based one performs better.

Soldering and erosion are two of the biggest serious problems faced in the die-casting industries. Cermet coatings utilized by high-velocity oxy-fuel (HVOF) spray technology have been developed in an attempt to overcome these problems. MoB-based cermet feedstock powders (MoB/NiCr and MoB/CoCr) were deposited on SKD61 (AISI H-13) substrates used as a preferred die (mold) material. Microstructural and mechanical properties of the coatings have been characterized by scanning electron microscopy, x-ray diffraction, Romulus bond strength test, and Vickers microhardness test. The durability of these coatings on cylindrical specimens against soldering also has been investigated by immersing in molten aluminum alloy (ADC-12) for 25 h at 670 °C and subsequently, compared with that of NiCr and CoMoCr coatings. Both types of MoB-based cermet coatings have shown high soldering resistance as negligible intermetallic formation occurred during the immersion test. This result is attributed to the existence of multiple inert borides in the coatings. The coatings also showed excellent mechanical properties. MoB/NiCr, in particular, showed higher bond strength, hardness, and wear resistance than MoB/CoCr. This suggests that MoB/NiCr will show higher durability than MoB/CoCr, NiCr, and CoMoCr during high pressure die-casting of aluminum alloys.

We propose a method for increasing word recognition accuracies by correcting the output of a handwriting recognition system. We treat the handwriting recognizer as a black box, such that there is no access to its internals. This enables us to keep our algorithm general and independent of any particular system. We use a novel method for correcting the output based on a ''phrase-based'' system in contrast to traditional source-channel models. We report the accuracies of two in-house handwritten word recognizers before and after the correction. We achieve highly encouraging results for a large synthetically generated dataset. We also report results for a commercially available OCR on real data.

Soldering and erosion of casting moulds and their components are one of the most prominent and persistent problems occurring in the die-casting industry. In this study, MoB- and Co-based cermets and alloy, respectively, were deposited on bond coated hard substrates (SKD61) using kinetic spray coating technique. Coating of hard powder materials on hard substrate using kinetic spray technique has also been considered a difficult phenomenon. Microstructural and mechanical characterization of the coatings was carried out by SEM, XRD, micro-hardness, bond strength and wear resistance tests. The results showed that Co-based alloy displayed the highest deposition efficiency along with microstructural properties. However, MoB/NiCr demonstrated the highest mechanical properties. The coatings were tested for soldering resistance by immersion of coated SKD61 cylinders in molten aluminium alloy (ADC-12) bath. MoB/NiCr displayed superior soldering resistance among the coatings. Among the candidate coatings, MoB/NiCr is considered as the one with the highest durability in a die-casting operation.

Despite several decades of research in document analysis, recognition of unconstrained handwritten documents is still considered a challenging task. Previous research in this area has shown that word recognizers perform adequately on constrained handwritten documents which typically use a restricted vocabulary (lexicon). But in the case of unconstrained handwritten documents, state-of-the-art word recognition accuracy is still below the acceptable limits. The objective of this research is to improve word recognition accuracy on unconstrained handwritten documents by applying a post-processing or OCR correction technique to the word recognition output. In this paper, we present two different methods for this purpose. First, we describe a lexicon reduction-based method by topic categorization of handwritten documents which is used to generate smaller topic-specific lexicons for improving the recognition accuracy. Second, we describe a method which uses topic-specific language models and a maximum-entropy based topic categorization model to refine the recognition output. We present the relative merits of each of these methods and report results on the publicly available IAM database.

Securing biometrics databases from being compromised is an important research challenge that must be overcome in order to support widespread use of biometrics based authentication. In this paper we present a novel method for securing fingerprints by hashing the fingerprint minutia and performing matching in the hash space. Our approach uses a family of symmetric hash functions and does not depend on the location of the (usually unstable) singular points (core and delta) as is the case with other methods described in the literature. It also does not assume a pre-alignment between the test and the stored fingerprint templates. We argue that these assumptions, which are often made, are unrealistic given that fingerprints are very often only partially captured by the commercially available sensors. The Equal Error Rate (EER) achieved by our system is 3%. We also present the performance analysis of a hybrid system that has an EER of 1.96% which reflects almost no drop in performance when compared to straight matching with no security enhancements. The hybrid system involves matching using our secure algorithm but the final scoring reverts to that used by a straight matching system.

Introduction: Despite growing concerns over pathological use of the Internet, studies based on validated psychometric instruments are still lacking in Pakistan. The present study aimed to examine the psychometric properties of Young’s Internet Addiction Test (IAT) in a sample of the Pakistani population. We examined the validity, internal consistency, readability and floor and ceiling effects of IAT scores.Methods: This cross-sectional study was conducted at CMH Lahore Medical College and Institute of Dentistry, Lahore, Pakistan from 1 March 2015 to 30 May 2015. A total of 522 medical and dental students completed the questionnaire, which consisted of three sections: (a) demographics and percentage grades in annual examinations, (b) a categorical question to record the estimated number of hours spent on the Internet per day, and (c) the English version of the IAT. All data were analyzed in SPSS v. 20. Principal axis factor analysis was used to validate the factor structure of the IAT in our study sample. An alpha coefficient > .7 was sought in the reliability analysis. Histograms and the values of skewness and kurtosis were analyzed for floor and ceiling effects. In addition, readability of the IAT was assessed as the Flesch Reading Ease score and Flesch-Kincaid Grade level function. Results: A total of 522 medical and dental students participated in the survey. Most respondents were female medical students enrolled in preclinical years of their degree program. Median age (min-max) of the respondents was 20 years (17-25 years). A single-factor model for IAT score explained 33.71% of the variance, with a high alpha coefficient of .893. In addition, the IAT had good face and convergent validity and no floor and ceiling effects, and was judged easy to read by participants. Conclusion: The English version of the IAT showed good psychometric properties in a sample of Pakistani university students. A single-factor model for assessing internet addiction showed good reliability and was found suitable with our study sample.

ObjectiveThe ability to predict patient readmission risk is extremely valuable for hospitals, especially under the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services which went into effect starting October 1, 2012. There is a plethora of work in the literature that deals with developing readmission risk prediction models, but most of them do not have sufficient prediction accuracy to be deployed in a clinical setting, partly because different hospitals may have different characteristics in their patient populations. Methods and materialsWe propose a generic framework for institution-specific readmission risk prediction, which takes patient data from a single institution and produces a statistical risk prediction model optimized for that particular institution and, optionally, for a specific condition. This provides great flexibility in model building, and is also able to provide institution-specific insights in its readmitted patient population. We have experimented with classification methods such as support vector machines, and prognosis methods such as the Cox regression. We compared our methods with industry-standard methods such as the LACE model, and showed the proposed framework is not only more flexible but also more effective. ResultsWe applied our framework to patient data from three hospitals, and obtained some initial results for heart failure (HF), acute myocardial infarction (AMI), pneumonia (PN) patients as well as patients with all conditions. On Hospital 2, the LACE model yielded AUC 0.57, 0.56, 0.53 and 0.55 for AMI, HF, PN and All Cause readmission prediction, respectively, while the proposed model yielded 0.66, 0.65, 0.63, 0.74 for the corresponding conditions, all significantly better than the LACE counterpart. The proposed models that leverage all features at discharge time is more accurate than the models that only leverage features at admission time (0.66 vs. 0.61 for AMI, 0.65 vs. 0.61 for HF, 0.63 vs. 0.56 for PN, 0.74 vs. 0.60 for All Cause). Furthermore, the proposed admission-time models already outperform the performance of LACE, which is a discharge-time model (0.61 vs. 0.57 for AMI, 0.61 vs. 0.56 for HF, 0.56 vs. 0.53 for PN, 0.60 vs. 0.55 for All Cause). Similar conclusions can be drawn from other hospitals as well. The same performance comparison also holds for precision and recall at top-decile predictions. Most of the performance improvements are statistically significant. ConclusionsThe institution-specific readmission risk prediction framework is more flexible and more effective than the one-size-fit-all models like the LACE, sometimes twice and three-time more effective. The admission-time models are able to give early warning signs compared to the discharge-time models, and may be able to help hospital staff intervene early while the patient is still in the hospital.

The translation of non-invasive Brain-Computer Interfaces (BCI) from the only possible communication pathway for paralyzed patients into more widespread applications is limited by: the lack of effective, user-friendly and robust paradigms, low information transfer rates (ITR), and lack of suitable electroencephalography (EEG) recording platforms. This study addresses the last point by exploring the extent to which the recently introduced EarEEG technology, which provides an unobtrusive, discreet and user-friendly way of recording EEG, can be used in BCI applications. This was approached by comparing conventional on-scalp EEG and EarEEG recordings in a well-established visual P300 BCI setup. The two recording methods were compared qualitatively by comparing Event-Related Potential (ERP) waveforms, and quantitatively in terms of P300 signal-to-noise ratios (SNR) and ITRs of the BCI paradigm. The study showed similar ERP waveforms and on par P300 SNRs from both on-scalp and ear electrodes, and only a 6.5% decrease in single channel ITR for ear electrodes compared to on-scalp electrodes. This demonstrates that the EarEEG platform is a feasible technology for P300-based BCIs.

With a growing focus on renewable energy, interest in design of wind turbines has also been expanding. In today’s market, the horizontal axis (windmill) turbine is the most common type in use; but, vertical axis (Darrieus) turbines have certain advantages. Darrieus turbines, which are liftdriven, have a higher power potential than the horizontal, or drag-driven turbines. The main flaw with their design is their inability to self-start. Darrieus turbines require an external energy source to bring the device to a minimum rotational speed. This paper presents design, construction and testing of a vertical axias (Darrieus) wind turbine with 3 blades, starting solely from the low energy of the wind. A separate drag device (Savonius type turbine) on the top of an existing Darrieus turbine was mounted to make the turbine self start at low wind speed. The cut-in speed of the turbine was 3 m/s, cut-off speed was 20 m/s and power obtained was 50 Watts at wind velocity of 6 m/s. The testing with primiarly available permanent magnetic generator sponsored by industry resulted in 35 Watts at 9m/s. doi: 10.5829/idosi.ijee.2015.06.01.02

This workshop report is an overview of the Predictive Models in Personalized Medicine workshop held on Dec. 11, 2010 at 2010 Neural Information Processing Systems (NIPS) Conference in Whistler, Canada. The workshop included 3 keynote talks and 6 oral and 5 poster presentations on peer reviewed submissions. The workshop also featured a panel discussion on the growing trends of the field. The workshop was very well received and attended. Based on the response and multiple requests received, it is planned to organize the workshop again at NIPS 2011.

The ability to predict patient readmission risk is extremely valuable for hospitals, especially under the Hospital Readmission Reduction Program (HRRP) of the Center for Medicare and Medicaid Services (CMS) which went into effect starting October 1, 2012. There is a plethora of work in the literature that deals with developing readmission risk prediction models, but most of them do not have sufficient prediction accuracy to be deployed in a clinical setting, partly because different hospitals may have different characteristics in their patient populations. In this work we experimented with a generic framework for institution-specific readmission risk prediction, which takes patient data from a single institution and produces a statistical risk prediction model optimized for that particular institution and optionally condition specific. This provides great flexibility in model building, and is also able to provide institution-specific insights in its readmitted patient population. We showcase some initial results at three institutions for Heart Failure (HF), Acute Myocardial Infarction (AMI) and Pneumonia (PN) patients. The developed models yield better prediction accuracy than the ones present in the literature.

Feature extraction is one of the most important modules for Facial Expression Recognition (FER) systems, which deals with getting the distinguishable features each expression and quantizing it as a discrete symbol. In this paper, we have proposed the novel robust feature extraction technique for the FER systems called Stepwise Linear Discriminant Analysis (SWLDA). This technique focuses on the selection of localized features from the facial expression images and discriminate their classes on the basis of regression values i.e. partial F-test. The proposed technique is then compared with conventional techniques such as LDA in combination with ICA. The results shows that SWLDA better than conventional techniques in terms of robustness in suitable feature selection and classification.

Minutiae based fingerprint matching algorithms are wildly used in fingerprint identification and verification applications. However, they may suffer from spurious matches because they do not use the rich local image information. In this paper, we extend minutiae based methods to incorporate such local image information. Our method uses local mutual information, a proven similarity measure in various applications, to improve the matching rate. The overall minutiae distribution pattern between two fingerprints is represented by the initial minutiae matching result, while the mutual information measures the similarity between neighborhoods of matched minutiae, thus enhancing the final matching decision. FVC2002 DB1 and DB3 databases are used to test the proposed approach. Experimental result shows the improvement when combining minutiae matching scores with mutual information scores.

Despite several decades of research in document analysis, recognition of unconstrained handwritten documents is still considered a challenging task. Previous research in this area has shown that word recognizers produce reasonably clean output when used with a restricted lexicon. But in absence of such a restricted lexicon, the output of an unconstrained handwritten word recognizer is noisy. The objective of this research is to process noisy recognizer output and eliminate spurious recognition choices using a topic based language model. We construct a topic based language model for every document using a training data which is manually categorized. A topic categorization sub-system based on Maximum Entropy model is also trained which is used to generate the topic distribution of a test document. A given test word image is processed by the recognizer and its word recognition likelihood is refined by incorporating topic distribution of the document and topic based language model probability. The proposed method is evaluated on a publicly available IAM dataset and experimental results show significant improvement in the word recognition accuracy from 32% to 40% over a test set consisting of 4033 word images extracted from 70 handwritten document images.

The security, lack of anonymity and revocability of the biometric template are critical issues that need to be addressed in order to vindicate the viability of biometric based authentication systems. Several methods have been proposed to address these problems. However, most of these methods offer lower accuracies than the base system where the template is insecure. This is because in most systems the gain in the security is achieved as a result of loss in non-redundant information. In this paper, we describe tradeoff between accuracy of an anonymous system and the security of the biometrics system. As a case study we start with a highly secure representation of a fingerprint. Then we describe several methods and show experimental results proving that every time we add more information to the secure representation, the accuracy increases, however valuable information is revealed to an adversary. We propose, via a k-trial attack model, how the trade-off can be complemented by another security policy such as an account lockout after a given number of attempts.

Biometric identification has numerous advantages over conventional ID and password systems; however, the lack of anonymity and revocability of biometric templates is of concern. Several methods have been proposed to address these problems. Many of the approaches require a precise registration before matching in the anonymous domain. We introduce binary string representations of fingerprints that obviates the need for registration and can be directly matched. We describe several techniques for creating anonymous and revocable representations using these binary string representations. The match performance of these representations is evaluated using a large database of fingerprint images. We prove that given an anonymous representation, it is computationally infeasible to invert it to the original fingerprint, thereby preserving privacy. To the best of our knowledge, this is the first linear, anonymous and revocable fingerprint representation that is implicitly registered.

In this paper we present a system for classification of machine printed and handwritten text in mixed documents. The classification is performed at the word level. We propose a feature extraction algorithm for each word image based on Gabor filters followed by classification using an expectation maximization (EM) based probabilistic neural network that reduces overfitting of training data. An overall precision of 94.62% was obtained for the Arabic script using the modified neural network. The accuracies obtained using a simple backpropagation neural network and an SVM were 83.33% and 90.26% respectively

An intrusion is defined as any set of actions that compromise the integrity, confidentiality or availability of a resource. Intrusion detection is an important task for information infrastructure security. One major challenge in intrusion detection is that we have to identify the camouflaged intrusions from a huge amount of normal communication activities. Data mining is to identify valid, novel, potentially useful, and ultimately understandable patterns in massive data. It is demanding to apply data mining techniques to detect various intrusions. In the last several years, some exciting and important advances have been made in intrusion detection using data mining techniques. Research results have been published and some prototype systems have been established. Inspired by the huge demands from applications, the interactions and collaborations between the communities of security and data mining have been boosted substantially. This seminar will present an interdisciplinary survey of data mining techniques for intrusion detection so that the researchers from computer security and data mining communities can share the experiences and learn from each other. Some data mining based intrusion detection systems will also be reviewed briefly. Moreover, research challenges and problems will be discussed so that future collaborations may be stimulated. For data mining/database researchers and practitioners, the seminar will provide background knowledge and opportunities for applying data mining techniques to intrusion detection and computer security. For computer security researchers and practitioners, it provides knowledge on how data mining can benefit and enhance computer security. We will try to understand and appreciate the following technical issues.

In order to improve the readability and the automatic recognition of handwritten document images, preprocessing steps are imperative. These steps in addition to conventional steps of noise removal and filtering include text normalization such as baseline correction, slant normalization and skew correction. These steps make the feature extraction process more reliable and effective. Recently Arabic handwriting recognition has received some attention from the research community. Due to the unique nature of the script, the conventional methods do not prove to be effective. In our work, we describe an orientation independent technique for baseline detection of Arabic words. In addition to that we describe, in the rest of the paper, our techniques for slant normalization, slope correction, line and word separation in handwritten Arabic documents. We show how the baseline can be exploited for slope and skew correction before proceeding with the steps of line and word separation.

The possibility that a biometric database is compromised is one of the main concerns in implementing biometric identification systems. The compromise of a biometric renders it permanently useless. In this paper we present a method of hashing fingerprint minutia information and performing fingerprint identification in a new space. Only hashed data is transmitted and stored in the server database, and it is not possible to restore fingerprint minutia locations using hashed data. We also present a performance analysis of the proposed algorithm.

We present methodologies for three important tasks that will eventually enable digital access of multilingual Indian document images. First, we describe several document image analysis techniques necessary to prepare Devanagari document images for OCR. The second task is OCR for machine printed Devanagari words without the help of a lexicon. We describe the OCR methodology and show how it is being extended to other Indian languages. Finally, we describe a versatile platform that facilitates automatic segmentation of document images in multiple Indian languages and an interface to capture the ground truth corresponding to the text. We use transliterated English text and virtual keyboards in a range of Indian languages for this purpose. The multilingual data entry capabilities of the tool and its underlying UNICODE data representation within a structured XML document also allow users to annotate passages of text in one language in other languages using a markup scheme to switch between scripts. Text and annotations are rendered in the appropriate scripts as the text is being annotated, thus providing users prompt and natural feedback. The XML back-end allows meta-data to be recorded describing the annotated document.

To encourage the delivery of high quality care with an additional emphasis on the transparency of hospital results and reporting, the Centers for Medicare & Medicaid Services (CMS) and the Joint Commission require each hospital to submit certain inpatient core measures every quarter. In this research, we developed a system that can achieve this automatically with high accuracy for any arbitrary clinical question, in a manner that is easily user configurable/teachable.