Abstract : Several new smartphones are released every year. Many people upgrade to new phones, and their old phones are not put to any further use. In this paper, we explore the feasibility of using such retired smartphones and their on-board sensors to build a home security system. We observe that door-related events such as opening and closing have unique vibration signatures when compared to many types of environmental vibrational noise. These events can be captured by the accelerometer of a smartphone when the phone is mounted on a wall near a door. The rotation of a door can also be captured by the magnetometer of a smartphone when the phone is mounted on a door. We design machine learning and threshold-based methods to detect door opening events based on accelerometer and magnetometer data and build a prototype home security system that can detect door openings and notify the homeowner via email, SMS and phone calls upon break-in detection. To further augment our security system, we explore using the Smartphone’s built-in microphone to detect door and window openings across multiple doors and windows simultaneously. Experiments in a residential home show that the accelerometer- based detection can detect door open events with accuracy higher than 98%, and magnetometer-based detection has 100% accuracy. By using the magnetometer method to automate the training phase of a neural network, we find that sound-based detection of door openings has an accuracy of 90% across multiple doors. Keywords : Smartphone, IOT, sensors, MEMS
Abstract : The In the last decade, the benefit of on-line payment has opened several new opportunities for e-commerce, lowering the geographical boundaries for retail. While e-commerce continues to be gaining quality, it's additionally the playground of fraudsters UN agency try to misuse the transparency of on-line purchases and also the transfer of master-card records. . We introduce GOTCHA!, a new approach on how to define and extract features from a time-weighted network, and how to exploit and integrate network-based and intrinsic features in fraud detection. The combination of all features (i.e., intrinsic and network features) is fed to the machine learning algorithms. This is the Gotcha! Model. As the creation of network features drastically increases the number of features to learn from, ensemble methods like Random Forest are used to train the models.
Authors - Mangesh. M. Vedpathak, Balbhim, L. Chavan
Abstract : Based on primary data as yield obtained from present research work and with the help of questionnaire through personal interview from farmers, cost benefit analysis on field experiment for Chilli crop was carried out. The present research aimed to analyze the cost and return of Chilli vegetable using organic and chemical fertilizer treatments. Experimental study area is one hector with five treatments where T5 was kept as control and four others treatment were T1-Vermicompost @ 3.5 t/ha, T2-NADEP compost @ 6.25 t/ha, T3-pit compost @ 6.25 t/ha, and T4-chemical fertilizer (300:150:150- N: P2O5: K2O kg/ha). Yield of Chilli was harvested after 90th day. The per hectare cost of cultivation of Chilli crop was Rs 2,25,138 which gives gross reruns of Rs 2,81,350 with the application of vermicompost treatment (T1). The per hectare cost of cultivation of Chilli crop was Rs 1,74,928 which gives gross reruns of Rs 2,11,840 with the application of NADEP compost treatment (T2). The per hectare cost of cultivation of Chilli crop was Rs 1,70,239 which gives gross reruns of Rs 2,54,870 with the application of pit compost treatment (T3). The per hectare cost of cultivation of Chilli crop was Rs 1,50,536 which gives gross reruns of Rs 2,99,721 with the application of chemical fertilizer treatment (T4). The per hectare cost of cultivation of Chilli crop was Rs 1,37,501 which gives gross reruns of Rs 1,83,705 in control treatment (T5). It was found that Chilli crop found more profitable with application of chemical fertilizer treatment (with yield - 9055 kg/ha) followed by pit compost (with yield - 7700 kg/ha), control (with yield - 5550 kg/ha), vermicompost treatment (with yield - 8,500 kg/ha) and NADEP compost treatment (with yield - 6400 kg/ha) respectively. The input output ratios were about 1.24, 1.21, 1.49, 1.99 and 1.33 in the treatment T1, T2, T3, T4 and T5 respectively. The higher B: C ratios (1.99) clearly indicated that cultivation of Chilli crop with chemical fertilizer treatment was found to be profitable as compared to remaining fertilizer treatment. The analysis of the data revealed that, the B/C ratio was more (1:1.99) with higher yield (9055 kg/ha) with application of chemical fertilizer treatment to the Chilli crop followed by pit compost treatment with B/C ratio 1:1.33. Lowest profit came out (Rs 36,912) in NADEP compost treatment with B/C ratio 1:21. Chilli crop was most profitable with application of chemical fertilizer treatment followed by pit compost treatment.
Keywords : Chemical Fertilizer, Chilli crop, NADEP