Detection of Iris Presentation Attacks Using Feature Fusion of Thepade's Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features.

Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade's sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade's SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade's SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human-computer interaction and security in the cyber-physical space by improving person validation

Formulation and Evaluation of Gastro Retentive Mucoadhesive Sustained Release Pellets of Acyclovir

Acyclovir is an antiviral drug, belonging to the deoxyguanosine family, widely prescribed for the treatment of herpes simplex viral infections, as well as in the treatment of herpes zoster (shingles). Oral bioavailability of acyclovir is very low (10–20%) owing to its first pass metabolism with elimination half-life (t1/2) of 2-3 h. It has absorption window in upper gastrointestinal tract. Due to its rapid elimination from site of absorption and short biological half life, sustained release formulation system for acyclovir is advantageous. In this study, gastro retentive muco-adhesive SR pellets of acyclovir was prepared using HPMC K 100M as matrix former and Sodium CMC as mucoadhesive polymer by extrusion spheronization technique. Acyclovir pellets prepared with higher concentration of HPMC (batch G) showed in vitro drug release for 12 h with sufficient mucoadhesion strength and ex vivo resident time. Release kinetic studies indicated that drug release data had best fit to Higuchi’s model. In-vivo studies in rat model proved that relative bioavailability of acyclovir SR pellets get increased by 1.98 fold as compared plain drug suspension. The optimized formulation batch G was found to be stable during six months accelerated stability period

Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia.

The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique

Isometric push/pull strength of agricultural workers of Central India

Many manually operated farm tools and equipment require exertion of push/pull force in horizontal plane. However, very few data are available on push/pull strength of agricultural workers of India.  A study was therefore, carried out to collect these data on male as well as female agricultural workers in the state of Madhya Pradesh, India.  A strength measurement set-up developed at CIAE, Bhopal was used for the purpose.  The data were collected on 1701 agricultural workers as subjects from 20 selected districts representing various agro-climatic zones of Madhya Pradesh out of which 944 were male and 757 were female.  The mean age, stature and mass of the male subjects were (29.8±9.5) years, (1649±59) mm and (51.2±6.4) kg whereas for female subjects the values were (33.7±8.2) years, (1519±54) mm and (45.0±7.3) kg, respectively.  The isometric push/pull strength of male subjects was higher than those of female subjects. The mean values for isometric push and pull strength in standing posture with both hands (in horizontal plane) were (242.4±56.4) N and (231.0±42.5) N, respectively for male workers and (175.5±33.9) N and (159.4±42.9) N, respectively for female workers.  The mass of the subjects indicated a positive correlation with isometric push/pull strength.  The 5th percentile push and pull strength values were  149.7 N and 161.2 N for male workers and 119.7 N and 88.8 N for female workers.  These values can be used to set a limit in the design of manually operated farm tools and equipment as well as for manual materials handling activities involving push/pull forces depending on the frequency of movement.  Considering the ergonomical requirement of 30% of the 5th percentile strength for frequent exertions, the design limits of push and pull strengths for male workers will be 45 N and 48 N and for female it will be 36 N and 27 N.  For the occasional exertions, the limit of push and pull strength is 60% of the strength which will be 90 N to 96 N for male and 72 N to 54 N for female workers.Keywords: push/pull strength, agricultural workers, central India, agricultural machinery Citation: Agrawal K. N., P. S. Tiwari, L. P. Gite, and V. Bhushanababu.  Isometric push/pull strength of agricultural workers of Central India.  Agric Eng Int: CIGR Journal, 2010, 12(1): 115-124. &nbsp

Aerobic Capacity of Indian Farm Women Using Sub-maximal Exercise Technique on Tread Mill

For sustained physical activities, the aerobic capacity, i.e., maximal oxygen consumption (VO2max) of a worker sets the limit for their maximum performance. Therefore to know the aerobic capacity of farm women, a study was carried out at NRCWA Bhopal Sub-centre, CIAE, Bhopal on fifteen farm women workers (nine in 25 to 35 year and six in 36 to 45 year age group) using sub maximal exercise (workload) technique on a computerized tread mill. The stature of subjects lied between the values of 5th to 95th percentile of Madhya Pradesh farm women. The mean body weights of these workers of 25 to 35 year and 36 to 45 year age groups were 49.8 + 9.3 kg and 46.0 + 7.1 kg, respectively. Corresponding mean VO2max of farm women were 33.5 + 4.86 ml kg-1 min-1 and 32.65 + 5.77 ml kg-1 min-1.  At mean aerobic capacity of farm women for the age of 25 to 45 year of  33.18 ml kg-1 min-1, the heart rate levels of 120 beats per min or work pulse of 40 beats per min may be considered as optimal criteria, for the quick appraisal of the state of activity that may be continued for longer period with proper rest pauses A linear relationship between heart rate and oxygen consumption rate was also observed and regression equations have been suggested for estimating the oxygen consumption rate of farm women from their measured heart rate data for agricultural activities in the field

Review of Recommendation on Location Based Services

In every Era “Location” is a strong component of “Mobility” Location based services (LBS) are services offered using mobile phone by taking mobile’s geographical location. The proposed system is providing location based services and offers with respect to user interest . Vendors are allowed to post and edit an advertisement for users. The system contains various modules such as advertising , Social program, Tourist place, Parking place, Emergency calls etc. The system uses apriori algorithm for mining frequent ratings from user. This information is used to provide popularity of location. It also provides user’s feedback, ranking based suggestion in secured manner. The purpose of this system is to notify the user based on their preferences and their interest in the particular area and notify them using android application. This will lead to lower advertising costs and expenditures also save the time of user for finding the located area of ads with help of GPS

Behavior prediction of traffic actors for intelligent vehicle using artificial intelligence techniques: A review

Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence (AI) techniques. Accurate behavior prediction of surrounding traffic actors is essential for the safe and secure navigation of the intelligent vehicle. Minor misbehavior of these vehicles on the busy roads may lead to an accident. Due to this, there is a need for vehicle behavior research work in today's era. This research article reviews traffic actors' behavior prediction techniques for intelligent vehicles to perceive, infer, and anticipate other vehicles' intentions and future actions. It identifies the key strategies and methods for AI, emerging trends, datasets, and ongoing research issues in these fields. As per the authors' knowledge, this is the first systematic literature review dedicated to the vehicle behavior study examining existing academic literature published by peer review venues between 2011 and 2021. A systematic review was undertaken to examine these papers, and five primary research questions have been addressed. The findings show that using sophisticated input representation that includes traffic rules and road geometry, artificial intelligence-based solutions applied to behavior prediction of traffic actors for intelligent vehicles have shown promising success, particularly in complex driving scenarios. Finally, the paper summarizes the most widely used approaches in behavior prediction of traffic actors for intelligent vehicles, which the authors believe serves as a foundation for future research in behavior prediction of surrounding traffic actors for secure and accurate intelligent vehicle navigation

Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform with Machine Learning Classifiers and Ensembles

Iris biometric identification allows for contactless authentication, which helps to avoid the transmission of diseases like COVID-19. Biometric systems become unstable and hazardous due to spoofing attacks involving contact lenses, replayed video, cadaver iris, synthetic Iris, and printed iris. This work demonstrates the iris presentation attacks detection (Iris-PAD) approach that uses fragmental coefficients of transform iris images as features obtained using Discrete Cosine Transform (DCT), Haar Transform, and hybrid Transform. In experimental validations of the proposed method, three main types of feature creation are investigated. The extracted features are utilized for training seven different machine learning classifiers alias Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), and decision tree(J48) with ensembles of SVM+RF+NB, SVM+RF+RT, and RF+SVM+MLP (multi-layer perceptron) for proposed iris liveness detection. The proposed iris liveness detection variants are evaluated using various statistical measures: accuracy, Attack Presentation Classification Error Rate (APCER), Normal Presentation Classification Error Rate (NPCER), Average Classification Error Rate (ACER). Six standard datasets are used in the investigations. Total nine iris spoofing attacks are getting identified in the proposed method. Among all investigated variations of proposed iris-PAD methods, the 4 ×4 of fragmental coefficients of a Hybrid transformed iris image with RF algorithm have shown superior iris liveness detection with 99.95% accuracy. The proposed hybridization of transform for features extraction has demonstrated the ability to identify all nine types of iris spoofing attacks and proved it robust. The proposed method offers exceptional performances against the Synthetic iris spoofing images by using a random forest classifier. Machine learning has massive potential in a similar domain and could be explored further based on the research requirements