Hand geometry recognition: an approach for closed and separated fingers

Hand geometry has been a biometric trait that has attracted attention from several researchers. This stems from the fact that it is less intrusive and could be captured without contact with the acquisition device. Its application ranges from forensic examination to basic authentication use. However, restrictions in hand placement have proven to be one of its challenges. Users are either instructed to keep their fingers separate or closed during capture. Hence, this paper presents an approach to hand geometry using finger measurements that considers both closed and separate fingers. The system starts by cropping out the finger section of the hand and then resizing the cropped fingers. 20 distances were extracted from each finger in both separate and closed finger images. A comparison was made between Manhattan distance and Euclidean distance for features extraction. The support vector machine (SVM) was used for classification. The result showed a better result for Euclidean distance with a false acceptance ratio (FAR) of 0.6 and a false rejection ratio (FRR) of 1.2

Prediction of Students’ Performance with Artificial Neural network Using Demographic Traits

Many researchers have studied student academic performance in supervised and unsupervised learning using numerous data mining techniques. Neural networks often need a greater collection of observations to achieve enough predictive ability. Due to the increase in the rate of poor graduates, it is necessary to design a system that helps to reduce this menace as well as reduce the incidence of students having to repeat due to poor performance or having to drop out of school altogether in the middle of the pursuit of their career. It is therefore necessary to study each one as well as their advantages and disadvantages, so as to determine which is more efficient in and in what case one should be preferred over the other. The study aims to develop a system to predict student performance with Artificial Neutral Network using the student demographic traits so as to assist the university in selecting candidates (students) with a high prediction of success for admission using previous academic records of students granted admissions which will eventually lead to quality graduates of the institution. The model was developed based on certain selected variables as the input. It achieved an accuracy of over 92.3 percent, showing Artificial Neural Network potential effectiveness as a predictive tool and a selection criterion for candidates seeking admission to a university

A Mobile Palmprint Authentication System Using a Modified MNT Algorithm, Circular Local Binary Pattern, and CNN (mobileNet)

A few approaches have been proposed for hand segmentation in palmprint recognition. Skin-color information does not process sufficient information for discrimination in complex backgrounds and variable illumination. The use of guides has also been proposed, which restricts hand placement during capturing. Contour tracing algorithms have also been proposed in the literature. This worked in an even background scenario with no objects or patterns around the hand. In the case of uneven background with objects present, the traditional contour tracing algorithm cannot accurately segment the hand from the background. Hence, this paper proposes a modified Moore Neighbor Tracing (MNT) algorithm for hand detection and key-point extraction in complex backgrounds. The hand image is converted to grey, and the edges in the hand image are detected. The modified algorithm then transverses selected edges and returns the peak and valleys of each finger. This is then used to crop the palm. The modified algorithm improves the accuracy of hand detection in complex backgrounds with an F-Score of 0.8657. A mobile palmprint biometric system was also presented using Circular Local Binary Pattern (CLBP) and Convolutional Neural Network (CNN). The system showed an accuracy of 98.3% for hands captured with the mobile device and the CASIA online database. An accuracy of 99.0% was also recorded for GPDS and PolyU online databases

Effect of Piper guineensees on physicochemical and organoleptic properties of watermelon (Citrulus lanatus) juice stored in refrigerator and ambient

Extracted juice from watermelon containing 0.01gPiper guineensesstored in refrigerator (6±2 oC) and on the shelf (28±1oC) usingpolyethylene bottles was evaluated for physicochemical and organoleptic changes. pH, total soluble sugars, titratable acidity and organolepticevaluation of the juice was carried out till deterioration sets in. Results showed that the sample stored in therefrigerator kept for 7 days while the sample on the shelf lasted for 3 days. pH value decreased from 5.40 to 4.80 and 5.70 for the samplestored in the refrigerator and on the shelf respectively while TSS increased from 0.064%Brix to 0.435% Brix and 0.578%Brix for sample stored in refrigerator and shelf respectively. Titratable acidity decreased from 2.90 % to 0.20% and 0.50% for samples for the juice stored in the refrigerator and on shelf respectively.All these changes were statistically significant (p<0.05).The sample stored on the shelf lost its organoleptic qualities on the third day with an average value less than 2 for taste, smell and colour. However, the juice stored in the fridge lost its organoleptic qualities at the 7th day with an average value of 3.0, 2.90 and 2.80 for taste, smell andcolour respectively.  From the results, juice extracted from water meloncannot be kept at ambient temperature beyond 3 days without proper refrigeration and an additive. This calls for alternative way of extending its shelf life in the absence of electricity supply using local spice like P. guineenses and to make it available during off season

Photic Stress and Rhythmic Physiological Processes: Roles of Selenium as a Chronobiotic

Physiological processes exhibit distinct rhythmic patterns influenced by external cues. External cues such as photic signal play an important role in the synchronization of physiological rhythms. However, excess of or indiscriminate exposure to photic signals exerts profound effects on physiological processes, disrupting normal hormonal secretory rhythms, altering sleep/wakefulness cycle, and impairing reproductive function. Alteration in sleep/wakefulness cycle, impairment in reproductive cycle, and disruption of normal hormonal secretory rhythms characterize risk groups for photic stress such as night workers, trans-meridian travelers, and night-active people. Evidence from primary studies is increasing on the tendency of selenium to reset internal biorhythms by targeting circadian proteins and melatonin. The review highlights the chronobiological roles of selenium

Assessment of Machine Learning Classifiers for Heart Diseases Discovery

Heart disease (HD) is one of the utmost serious illnesses that afflict humanity. The ability to anticipate cardiac illness permits physicians to deliver better knowledgeable choices about their patient’s wellbeing. Utilizing machine learning (ML) to minimize and realize the symptoms of cardiac illness is a worthwhile decision. Therefore, this study aims to analyze the effectiveness of some supervised ML procedures for detecting heart disease in respect to their accuracy, precision, f1-score, sensitivity, specificity, and false-positive rate (FPR). The outcomes, which were obtained using python programming language were compared. The data employed in this investigation came from an open database of the National Health Service (NHS) heart disease which originated in 2013. Through the machine learning (ML) technique, a dimensionality reduction technique and five classifiers were employed and a performance evaluation between the three classifiers- principal component analysis (PCA), decision tree (DT), random forest (RF), and support vector machine (SVM). The NHS database contains 299 observations. The system was evaluated using confusion matrix measures like accuracy, precision, f1-score, sensitivity (TPR), specificity, and FPR. It is concluded that ML techniques reinforce the true positive rate (TPR) of traditional regression approaches with a TPR of 98.71% and f-measure value of 68.12%. The true positives rate which is the same as the sensitivity was used to evaluate the accuracy of the classifiers and it was deduced that the PCA + DT outperformed that of the other two with a sensitivity of 98.71% and since the value is on the high side, this implies that the classifier will be able to accurately detect a patient with HD in his or her body

Comparative Analysis of Machine Learning Techniques for the Prediction of Employee Performance

Human Resources’ purpose is to assign the best people to the right job at the right time, train and qualify them, and provide evaluation methods to track their performance and safeguard employees’ perspective skills. These data are crucial for decision-makers, but collecting the best and most useful information from such large amounts of data is tough. Human Resource employees no longer need to manually handle vast amounts of data with the advent of data mining. Data mining’s primary goal is to uncover information hidden in data patterns and trends in order to produce results that are close to ideal. This study aims at comparing the performance of three techniques in the prediction of performance. The dataset undergoes preprocessing steps that include data cleaning, and data compression using Principal Component Analysis. After preprocessing, training and classification were done using Artificial Neural Network, Random Forest, and Decision tree algorithm. The result showed that Artificial Neural networks performed the best in the prediction of employee performance

An assessment of the levels of phthalate esters and metals in the Muledane open dump, Thohoyandou, Limpopo Province, South Africa

<p>Abstract</p> <p>Background</p> <p>This work reports the determination of the levels of phthalate esters (dimethyl phthalate (DMP), diethyl phthalate (DEP), dibutyl phthalate (DBP), diethyl hexyl phthalate (DEHP)) and metals (lead, cadmium, manganese, zinc, iron, calcium) in composite soil samples. The soil samples were collected randomly within the Muledane open dump, Thohoyandou, Limpopo province, South Africa. Control samples were collected about 200 m away from the open dump. The phthalate esters were separated and determined by capillary gas chromatography with a flame ionization detector, whilst the metals were determined by atomic absorption spectrophotometry.</p> <p>Results</p> <p>Open dump values for the phthalate esters and metals to be generally higher in comparison to control samples for DMP, DEP, DBP and DEHP – the mean values calculated were 0.31 ± 0.12, 0.21 ± 0.05, 0.30 ± 0.07, and 0.03 ± 0.01 mg/kg, respectively, for the open dump soil samples. Nonetheless, the mean open dump values for lead, cadmium, manganese, zinc, iron and calcium were 0.07 ± 0.04, 0.003 ± 0.001, 5.02 ± 1.92, 0.31 ± 0.02, 11.62 ± 9.48 and 0.12 ± 0.13 mg/kg, respectively. The results were compared statistically.</p> <p>Conclusion</p> <p>Our results revealed that the discarding of wastes into the open dump is a potential source of soil contamination in the immediate vicinity and beyond, <it>via </it>dispersal. Increased levels of phthalate esters and metals in the soil pose a risk to public health, plants and animals. Sustained monitoring of these contaminants is recommended, in addition to upgrading the facility to a landfill.</p

Predicting swimming performance using state anxiety

Competitive state anxiety is a common response to stressful competitive sports situations that could affect athletic performance. The effects of state anxiety on swimming performance need further inquiry. The aim of the study was to determine the component of state anxiety that best predicts swimming performance. A quantitative, cross-sectional study design that made use of the Competitive State Anxiety Inventory-2 to measure precompetitive state anxiety was used. A total of 61 male high school swimmers whose age ranged between 14 and 19 years (M = 16.16, standard deviation = 1.66 years) completed the Competitive State Anxiety Inventory-2 1 hr before competing in a 50-m individual swimming event. Performance was evaluated using finishing position. Due to the relatively short duration of the 50-m event, the available literature would suggest that Somatic Anxiety would have a greater effect on Performance - there is not enough time to allow cognitive anxiety to have a detrimental impact on performance. Thus, it was hypothesized that somatic rather than cognitive anxiety will best predict swimming performance. It emerged that both cognitive (b =.787; p <.001) and somatic anxieties (b =.840; p <.001) can independently predict swimming performance. However, when both cognitive and somatic anxieties were regressed onto swimming performance, somatic anxiety partially dominated cognitive anxiety (b =.626; p <.001) and became the significant predictor of swimming performance. It is recommended that swimmers and swimming coaches make use of specific intervention strategies that eradicate the detrimental effects of somatic anxiety immediately before competition.IS

The malleable brain: plasticity of neural circuits and behavior: A review from students to students

One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation (LTP) and long-term depression (LTD) respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by LTP and LTD, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity.Fil: Schaefer, Natascha. University of Wuerzburg; AlemaniaFil: Rotermund, Carola. University of Tuebingen; AlemaniaFil: Blumrich, Eva Maria. Universitat Bremen; AlemaniaFil: Lourenco, Mychael V.. Universidade Federal do Rio de Janeiro; BrasilFil: Joshi, Pooja. Robert Debre Hospital; FranciaFil: Hegemann, Regina U.. University of Otago; Nueva ZelandaFil: Jamwal, Sumit. ISF College of Pharmacy; IndiaFil: Ali, Nilufar. Augusta University; Estados UnidosFil: García Romero, Ezra Michelet. Universidad Veracruzana; MéxicoFil: Sharma, Sorabh. Birla Institute of Technology and Science; IndiaFil: Ghosh, Shampa. Indian Council of Medical Research; IndiaFil: Sinha, Jitendra K.. Indian Council of Medical Research; IndiaFil: Loke, Hannah. Hudson Institute of Medical Research; AustraliaFil: Jain, Vishal. Defence Institute of Physiology and Allied Sciences; IndiaFil: Lepeta, Katarzyna. Polish Academy of Sciences; ArgentinaFil: Salamian, Ahmad. Polish Academy of Sciences; ArgentinaFil: Sharma, Mahima. Polish Academy of Sciences; ArgentinaFil: Golpich, Mojtaba. University Kebangsaan Malaysia Medical Centre; MalasiaFil: Nawrotek, Katarzyna. University Of Lodz; ArgentinaFil: Paid, Ramesh K.. Indian Institute of Chemical Biology; IndiaFil: Shahidzadeh, Sheila M.. Syracuse University; Estados UnidosFil: Piermartiri, Tetsade. Universidade Federal de Santa Catarina; BrasilFil: Amini, Elham. University Kebangsaan Malaysia Medical Centre; MalasiaFil: Pastor, Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Wilson, Yvette. University of Melbourne; AustraliaFil: Adeniyi, Philip A.. Afe Babalola University; NigeriaFil: Datusalia, Ashok K.. National Brain Research Centre; IndiaFil: Vafadari, Benham. Polish Academy of Sciences; ArgentinaFil: Saini, Vedangana. University of Nebraska; Estados UnidosFil: Suárez Pozos, Edna. Instituto Politécnico Nacional; MéxicoFil: Kushwah, Neetu. Defence Institute of Physiology and Allied Sciences; IndiaFil: Fontanet, Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Turner, Anthony J.. University of Leeds; Reino Unid