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Apart from being relied upon for feeding the entire world, the agricultural sector is also responsible for a third of the global Gross-Domestic-Product (GDP). Additionally, a majority of developing nations depend on their agricultural produce as it provides employment opportunities for a significant fraction of the poor. This calls for methods to ensure the accurate and efficient diagnosis of plant disease, to minimize any adverse effects on the produce. This paper proposes the recognition and classification of maize plant leaf diseases by application of the Deep Forest algorithm. The Automated novel approach and accurate classification using the Deep Forest technique are a significant step-up from the existing manual classification and other techniques with less accuracy. The proposed approach has outperformed Deep Neural models and other traditional machine learning algorithms in terms of accuracy. It justifies its low dependency on extensive Hyper-parameter tuning and the size of the dataset as against other Deep Learning Models based on neural networks.

We present the design and implementation of DIO, a novel digital-physical construction toolkit to enable constructionist learning for children from age group 8-12 years. The toolkit comprises of dome-shaped (D) tangible modules with various attachments that allow suspension on the body of multiple children and/or in the environment to support a variety of sensing/input (I), actuation/output (O) functionalities. The modules are enabled for wireless communication and can be linked together using an Augmented Reality based programming interface running on a smartphone. The smartphone recognizes our hemispherical modules omnidirectionally through novel computer vision based 3D patterns; custom made to provide logical as well as semantic encoding. In this paper, we show how, owing to its unique form-factor, the toolkit enables multi-user constructions for the children and offers a shared learning experience. We further reflect on our learning from a one-year long iterative design process and contribute a social scaffolding based procedure to engage children with such constructionist toolkits effectively.

The quantitative and differential analysis of leukocytes present in human body provide conducive hematological information to physicians for diagnosis of various infections and ailments. This paper proposes an Optimized Binary Bat algorithm, an enhanced version of the original Binary Bat Algorithm, for classification of different types of leukocytes. It is used for the first time in this field of application to the best of our knowledge. A set of features are extracted from images of WBCs and then the optimized algorithm is used to obtain a subset of those features which are essential and more relevant from the high-dimensional dataset. Similar to the original BBA, the optimized BBA is an evolutionary algorithm inspired by the echolocation technique used by bats for locating a prey or an object. OBBA aims to reduce the dimensionality of the dataset by determining the features which are most discriminative. The proposed algorithm is implemented using four different classifiers, K-nearest neighbors (KNN), Logistic Regression, Random Forest and Decision Tree, and their performance is compared. The proposed OBBA can be used in classification of WBCs with an average accuracy of 97.3% and help in analysis of numerous hematological conditions. The optimized BBA is also compared with other nature inspired algorithms including Optimized Crow Search algorithm and Optimized Cuttlefish algorithm, and the results indicate that the suggested algorithm is sufficiently fast and accurate to be used in hematological analysis.

The Internet is a vast collection of documents that are constantly evolving. Most of the data on the Internet is available through structured or semi-structured documents. XML is one of the semi-structured data exchange format used over the web. Therefore, understanding and detecting the changes occurring in XML documents becomes crucial. Due to the tree-structured and self-determined nature of the XML document, many change detection algorithms have been proposed. Most of these algorithms are based on comparing the entire XML trees of two versions of XML documents for identifying and locating the changes in them. We proposed an algorithm in this paper that uses the multiple digest scheme that helps in reducing the search space of locating the changes in the XML document. The approach used in this paper does not create any extra computation overhead for the processing of XML document which is justified by the results obtained.

Introduction Child abuse and neglect (CAN) is a significant global problem with a serious impact on the victims throughout their lives. Dentists have the unique opportunity to address this problem. However, reporting such cases has become a sensitive issue due to the uncertainty of the diagnosis. The authors are testing the knowledge of the dentists toward CAN and also trying to question the efforts of the educational institutions to improve this knowledge for the better future of the younger generation. Materials and methods Questionnaire data were distributed to 1,106 members regarding their knowledge, professional responsibilities, and behavior concerning child abuse. Results There were 762 responses to the questionnaire, yielding a response rate of 68.9%. Although dentists consider themselves able to identify suspicious cases, only a small percentage of the participants correctly identified all signs of abuse and 76.8% knew the indicators of child abuse. Most of them were willing to get involved in detecting a case and about 90% believed that it is their ethical duty to report child abuse. Only 7.2% suspected an abuse case in the past. The numbers indicate a lack of awareness about CAN in these participants. No differences were observed between sexes, year of graduation, types of license, frequency at which children were treated, and formal training already received. Conclusion A large proportion of child physical abuse cases go undocumented and unreported. The data showed that not all dental care providers and students were prepared to fulfill their legal and professional responsibilities in these situations. Clinical significance There should be modifications in the dental school curriculum focusing on educational experiences regarding child abuse to strengthen their capability to care and protect children. How to cite this article Malpani S, Arora J, Diwaker G, Kaleka PK, Parey A, Bontala P. Child Abuse and Neglect: Do We know enough? A Cross-sectional Study of Knowledge, Attitude, and Behavior of Dentists regarding Child Abuse and Neglect in Pune, India. J Contemp Dent Pract 2017;18(2):162-169.

Through this article, we introduce the novel concept of place-based social networks, as a way to enable effective, personal story-sharing for communities. We present our explorations of utilizing Starbucks at an American University to establish such a network for the student community. We show how anonymity between the community members, combined with the shared, spatiotemporal grounding provided by the place (Starbucks,) leads to prosocial behavior among the students.

In this paper, an IoT-based smart home system is designed which consists of a number of subsystems required for a smart home. These subsystems are critical parameters monitoring with alert, home security systems, energy saving system, electrical appliance control and monitoring system. A detailed study has been done so as to find the appropriate hardware and software resources to fulfil the requirements of the smart home. Safety is one of the important concerns for IoT application which has been addressed using secure cloud platform which provides the authentication using login ID and password technique. A simple, efficient and robust architecture is proposed in the paper. The system is also implemented on the hardware set-up. The system is designed in such a way that consumes minimum power so simple sleep and wake up energy efficient algorithm is implemented along with ultra-low power MCU platform like MSP430.

Online cricket commentary has become very popular as the internet provides access to a large number of sports websites. A key challenge for them is to offer their readers an insightful and fast paced live commentary. In this paper, we propose a framework to automate cricket shot identification and commentary generation using sensor data as features for machine learning models.

In the present scenario, it is essential to have an automatic billing system for shopping malls, supermarket and other wholesale & retail stores. Numerous billing systems like barcode scanning mechanism-based systems or tag-based systems are available in the market. It is important to replace such existing system with better and robust systems so hereby we proposed “Smart Goods Billing Management and Payment System for Shopping Malls”. In this system, the basic fundamental is barcode scanning for products, but we replace the conventional barcode scanner for faster and better results. In our prototype, the android phone is being used as a barcode scanner for simple, better and portable barcode scanner. This scanner is connected wirelessly to MCU via Bluetooth module. MCU is also connected to PC/Laptop for creating the database of all customers, their products, and bills. This database also tracks the total sale and number of goods sold per day. In addition, RFID technology is implemented in this system for payment through card-based system. Simulation and hardware-based results are proposed in this paper.

Electrical measurement of quantities plays a crucial role in the real world. Measuring voltage is one of the main tasks for developing large number of applications. This paper is focused on design and development of digital voltmeter using different techniques. It has been shown how to develop a Digital Voltmeter (DVM) from simplest type to professional digital voltmeter. Different types of techniques are adopted for making a voltmeter and the result is compared for testing the accuracy. The simulation of all techniques is done on Proteus ISIS 7.10 and implemented on the hardware. The techniques used for developing the digital voltmeter is comparator based DVM, 8051 based DVM and PIC based DVM. The accuracy of DVMs is shown by simulation in Proteus ISIS and through hardware implementation.

This paper presents a prototype for the monitoring of Heartbeat rate. A Heart Beat (HB) sensor is being developed for acquainting the input signals using Light Dependent Resistance (LDR) and Light Emitting Diode (LED). It senses the heartbeat of a person and converts it in the form of electrical signals and pulses. The signals are amplified using a signal conditioning circuit and processed by a controller. The frequency of the signal depends on the heartbeat rate, this lays down the basic principle of the HB measuring system. The user needs to put his/her finger in the HB sensor for acquiring the input signals. Although number of methods has been proposed and implemented in this domain yet the proposed system in this text provides a simpler and robust method for measuring the heart rate. The proposed system is being implemented on the hardware and also simulated in Proteus ISIS 7.10 to prove its effectiveness. The proposed model is much more precise, straightforward and cheaper than other heartbeat rate measuring systems. This work has tried to make an easy and stout system for the monitoring of heart beat.