3-Total Super Sum Cordial Labeling by Applying Operations on some Graphs

The sum cordial labeling is a variant of cordial labeling. In this paper, we investigate 3-Total Super Sum Cordial labeling. This labeling is discussed by applying union operation on some of the graphs. A vertex labeling is assigned as a whole number within the range. For each edge of the graph, assign the label, according to some definite rule, defined for the investigated labeling. Any graph which satisfies 3-Total Super Sum Cordial labeling is known as the 3-Total Super Sum Cordial graphs. Here, we prove that some of the graphs like the union of Cycle and Path graphs, the union of Cycle and Complete Bipartite graph and the union of Path and Complete Bipartite graph satisfy the investigated labeling and hence are called the 3-Total Super Sum Cordial graphs

Potential of Biogas Production from Food Waste in a Uniquely Designed Reactor under Lab Condition

An original digester design is adopted in building a lab scale 20 L biogas plant. The novelty is the digester has a recycling line apart from other inclusions (inlet, outlet, gasline etc.) including water seal. Initially cow dung (inoculum) was added. After initial gas production, cow dung in the digester was co digested with food waste. Main ingredients of the food waste included rice, vegetable peelings, cucumber, bitter gourd etc., This waste had slightly higher solids and volatile solids (9.3% and 94.9%) content compared to cow dung (8.5% and 93.1%). The biogas volume was determined by measuring the downward movement of the water line and calculating the volume of the water that was displaced by gas. The loading rate of the digester in terms of Total solids was 16.6 kg/(m3* day). The amount of biogas production starting from the 16th to the 23rd day was 6.7 L. This biogas did not burn. The failure to burn was probably due to low methane and a high carbon dioxide concentration in the biogas. After the 23rd day 650 mL biogas was ignited using a match stick. The total biogas yield recorded was 68.50 L for a period of 60 days. The lab temperature was between 30-340C. Maximum microbial counts of 6.21* 104 colonies/mL were observed during the peak of biogas production. The phosphate content was recorded 1.027 mg/100g (slurry content) on the 60th day. The volatile solids finally reduced to 81.66% of total solids. This volatile solids reduction/destruction/leads to conversion of biogas. The volume of biogas produced from the amount volatile solids destroyed calculated using the ideal gas law was 51Litres. In an Indian scenario, food waste

Potential of biogas production from food waste in a uniquely designed reactor under lab conditions

An original digester design is adopted in building a lab scale 20 L biogas plant. The novelty is the digester has a recycling line apart from other inclusions (inlet, outlet, gasline etc.) including water seal. Initially cow dung (inoculum) was added. After initial gas production, cow dung in the digester was co digested with food waste. Main ingredients of the food waste included rice, vegetable peelings, cucumber, bitter gourd etc., This waste had slightly higher solids and volatile solids (9.3% and 94.9%) content compared to cow dung (8.5% and 93.1%). The biogas volume was determined by measuring the downward movement of the water line and calculating the volume of the water that was displaced by gas. The loading rate of the digester in terms of Total solids was 16.6 kg/ (m3 *day). The amount of biogas production starting from the 16th to the 23rd day was 6.7L. This biogas did not burn. The failure to burn was probably due to low methane and a high carbon dioxide concentration in the biogas. After the 23rd day 650 mL biogas was ignited using a match stick. The total biogas yield recorded was 68.50L for a period of 60 days. The lab temperature was between 30- 340C. Maximum microbial counts of 6.21*104 colonies / mL were observed during the peak of biogas production. The phosphate content was recorded 1.027 mg/100g (slurry content) on the 60th day. The volatile solids finally reduced to 81.66% of total solids. This volatile solids reduction/destruction/ leads to conversion of biogas. The volume of biogas produced from the amount volatile solids destroyed calculated using the ideal gas law was 51Litres. In an Indian scenario, food waste can become a good feedstock for biogas production at Indian households instead of going to the dump yards or being burnt along with plastic/polythene cover. Of the different types of organic wastes available food waste holds highest potential of economic exploitation as it contains high amount of carbon in the volatile solids that can be converted into biogas. The widespread implementation of biogas digesters in urban areas would contribute to the solution of the problems of urban sanitation energy supply and mitigation of green house gases

Neural classification of mass abnormalities with different types of features in digital mammography

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BIRADSfeatures, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database

Neural networks for the classification of benign and malignant patterns in digital mammograms

This chapter presents neural network-based techniques for the classification of microcalcification patterns in digital mammograms. Artificial Neural Network (ANN) applications in digital mammography are mainly focused on feature extraction, feature selection and classification of microcalcification patterns into ‘benign’ and ‘malignant’. An extensive review of neural techniques in digital mammography is presented. Recent developments such as autoassociators and evolutionary neural networks for feature extraction and selection are presented. Experimental results using ANN techniques on a benchmark database are described and analyzed. Finally, a comparison of various neural network-based techniques is presented

Neural-association of microcalcification patterns for their reliable classification in digital mammography

Breast cancer continues to be the most common cause of cancer deaths in women. Early detection of breast cancer is significant for better prognosis. Digital Mammography currently offers the best control strategy for the early detection of breast cancer. The research work in this paper investigates the significance of neural-association of microcalcification patterns for their reliable classification in digital mammograms. The proposed technique explores the auto-associative abilities of a neural network approach to regenerate the composite of its learned patterns most consistent with the new information, thus the regenerated patterns can uniquely signify each input class and improve the overall classification. Two types of features: computer extracted (gray level based statistical) features and human extracted (radiologists' interpretation) features are used for the classification of calcification type of breast abnormalities. The proposed technique attained the highest 90.5% classification rate on the calcification testing dataset

Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network

Presence of mass in breast tissues is highly indicative of breast cancer. The research work investigates the significance of neural-association of mass type of breast abnormality patterns for benign and malignant class characterization using auto-associator neural network and original features. The characterized patterns are finally classified into benign and malignant classes using a classifier neural network. Grey-level based statistical features, BIRADS features, patient age feature and subtlety value feature have been used in proposed research work. The proposed research technique attained a 94% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database

Calculating growth rate of water hyacinth pollution wise (in relation to trophic state)

This study was carried out with respect to calculating growth rate of water hyacinth in relation to the trophic state of the water body. 50 water hyacinth plants about 10 from each lake were taken. The concentrations of phosphorous, nitrogen, potassium and calcium were determined. Biochemical oxygen demand and dissolved oxygen were also determined. Various growth aspects like total number of leaves were counted, root and petiole length were recorded, leaf area was plotted to obtain surface area. Growth Index was calculated based on fresh weight of the plant was chosen to study the growth of water hyacinth with respect to pollution levels of the 5 lakes. Growth Index was calculated using the equation GI = (A / M) ------- where M = Mean fresh weight of 530 water hyacinth plants collected from 5 lakes. A = Mean fresh weight of 10 water hyacinth plants collected from a particular lake in a particular month. A correlation of GI to pollution status of lakes was made and a correlation of lake water constituents with growth parameters of water hyacinth was done: The fresh mean weight of water hyacinth plants collected over 12 months period was distinctly higher for polluted Lakes when compared to less polluted lakes. Mean petiole length of plants collected from Yelahanka, Nagavara and Hebbal Lakes (polluted) were greater as compared to those collected from Jakkur and Doddabommsandra Lakes (less polluted). TSI based on TP was 88.28 for Nagavara lake which was Hypereutrophic and eutrophic for Jakkur lake where TP was 69.81. GI of water hyacinth plants showed a correlation coefficient of +0.62 to TP. This study concludes that higher the pollution level of the lake, higher would be the growth rate of water hyacinth. Polluted lakes had strong and sturdy petioles and more GI compared to less polluted lakes. TP versus GI, showed a good positive person's correlation coefficient. TP the limiting nutrient has significant impact on the pollution level

Characterization of breast abnormality patterns in digital mammograms using auto-associator neural network

Presence of mass in breast tissues is highly indicative of breast cancer. The research work investigates the significance of neural-association of mass type of breast abnormality patterns for benign and malignant class characterization using auto-associator neural network and original features. The characterized patterns are finally classified into benign and malignant classes using a classifier neural network. Grey-level based statistical features, BIRADS features, patient age feature and subtlety value feature have been used in proposed research work. The proposed research technique attained a 94% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database

Neural-association of microcalcification patterns for their reliable classification in digital mammography

Breast cancer continues to be the most common cause of cancer deaths in women. Early detection of breast cancer is significant for better prognosis. Digital Mammography currently offers the best control strategy for the early detection of breast cancer. The research work in this paper investigates the significance of neural-association of microcalcification patterns for their reliable classification in digital mammograms. The proposed technique explores the auto-associative abilities of a neural network approach to regenerate the composite of its learned patterns most consistent with the new information, thus the regenerated patterns can uniquely signify each input class and improve the overall classification. Two types of features: computer extracted (gray level based statistical) features and human extracted (radiologists' interpretation) features are used for the classification of calcification type of breast abnormalities. The proposed technique attained the highest 90.5% classification rate on the calcification testing dataset