Intermediation Capabilities of Information and Communication Technologies (ICTs) in Ghana’s Agricultural Extension System

Information and communication technologies (ICTs), specifically those that are digital and interactive, present opportunities for enhanced intermediation between actors in Ghana’s agricultural extension system. To understand these opportunities, this study investigates the capabilities of ICTs in support of seven forms of intermediation in the context of agricultural extension: disseminating (information), retrieving (information), harvesting (information), matching (actors to services), networking (among actors), coordinating (actors), and co-creating (among actors). The study identifies the types of ICTs currently functioning in Ghana’s agricultural system, and applies a Delphi-inspired research design to determine the consensus and dissensus of researchers, scientists, and practitioners about the potential of these ICTs to support each of the seven intermediation capabilities. The findings reveal that experts reached consensus that interactive voice response (IVR) technologies currently have the highest potential to support disseminating, retrieving, harvesting, and matching. Meanwhile, social media messaging (SMM) technologies are currently seen as highly capable of supporting coordinating and, to a lesser extent, co-creating, but no consensus is reached on the potential of any of the technologies to support networking.

Modular hardware platform for the development of industrial Internet of Things (IoT) devices

The modular hardware platform architecture for the development of industrial IoT devices is presented as an example of information harvesting and its analysis. Variants of modules implementing typical functions of such devices are given: data acquisition from a distributed array of sensors; preprocessing, aggregation and data transmission; data mining; storage of primary data and analysis results.

ENHANCED APPROACH FOR SOIL CLASSIFICATION USING BOOSTED C5.0 DECISION TREE ALGORITHM

Data mining includes the utilization of erudite data analysis tools to discover previously unidentified, suitable patterns and relationships in enormous data sets. Data mining tools can incorporate statistical models, machine learning methods such as neural networks or decision trees, and mathematical algorithms. As a result data mining comprises of more process. This performs analysis and prediction than collecting and managing data. The main objective of data mining is to identify valid, potentially useful, novel and understandable correlations and patterns in existing data. Finding and analyzing useful patterns in data is known by different names (e.g., knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing). The term data mining is basically utilized by statisticians, database researchers, and the business communities.

Web Harvesting for Data Retrieval on Scientific Journal Sites

Publishing scientific articles online in journals is a must for researchers or academics. In choosing the journal of purpose, the researcher must look at important information on the journal's web, such as indexing, scope, fee, quarter and other information. This information is generally not collected in one page, but spread over several pages in a web journal. This will be complicated when researchers have to look at information in several journals, moreover, the information in these journals may change at any time. In this research, web harvesting design is conducted to retrieve information on web journals. With web harvesting, information that is spread across several pages can be collected into one, and researchers do not need to worry if the information has changed, because the information collected is the last or updated information. Harvesting technique is done by taking the page URL of the page, starting the source code from where the information is retrieved and end source code until the information stops being retrieved. Harvesting technique was successfully developed based on the web bootstrap framework. The test data is taken from several scientific journal webs. The information collected includes name, description, accreditation, indexing, scope, publication rate, publication charge, template and quarter. Based on tests carried out using black box testing, it is known that all the features made are as expected.

Optional Stopping in a Heteroscedastic World

When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or “heteroscedasticity”). We asked humans to perform a categorisation task in which discrete, continuously-valued samples (oriented gratings) arrived in series until the observer made a choice. Human behaviour was best described by a model that adaptively weighted sensory signals by their inverse prediction error, and integrated the resulting quantities to a collapsing decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in hetereoscedastic natural environments.

Sensors for crop productivity, yield maps

For gathering the final results of a season-round crop work, the georeferenced yield is a key piece of information. Harvesting equipment can be equipped with sensors to gather such information. Systems based on different technologies (impact, volume, optics, density, gravity…) will be explained for recording the yield flow inside the machinery, during the harvesting. Adaptations of yield sensors depending on the commodity, along with new sensing systems will be discussed. Sensor for quality quantification will also be explained, as they are important for certain crops. Basic procedures for the calibration of the sensing system and the proper registration of yield data to generate a successful yield map are presented.

AN EMPIRICAL ANALYSIS ON DATA MINING SYSTEM AND ITS TECHNIQUES .

Data mining is an extraction of knowledge discovery from huge amount of data which is previously unknown and potentially useful for analytical processing and decision making. The other acronyms of data mining are such as Data archeology, Data dredging, Information harvesting and Business Intelligence. The various data mining techniques are used to find the hidden interestingness or new patter to store the data. These techniques and approaches of data mining can efficiently build the new environment for analyzing and predictions. This paper highlights data mining process and its various techniques to find the interestingness. Finally, concluded with its limitations. The objective of the paper is opens new horizons for researchers of forthcoming generations.

Chiral information harvesting in helical poly(acetylene) derivatives using oligo(p-phenyleneethynylene)s as spacers

A chiral harvesting transmission mechanism is described in poly(acetylene)s bearing oligo(p-phenyleneethynylene)s (OPEs) used as rigid achiral spacers and derivatized with chiral pendant groups.

Rhetoric, Torture, and Surveillance Time

Relying on select US government Torture Memos, this article develops the term “surveillance time” to highlight the ways in which surveillance practices, in this case within the material confines of post-9/11 detention centers, come to threaten humans’ subjectivities through temporal disruption and manipulation. While surveillance has lately been understood in digital terms, such as in corporations’ data-mining practices and in technologies like facial-recognition software, we should not neglect its material, embodied dimensions. Surveillance time ultimately asks us to reconsider how monitoring and information-harvesting practices blur the boundaries between human bodies and data. Attention to the relationship between torture and surveillance also opens up new possibilities for understanding the now-ubiquitous monitoring strategies integrated into everyday life.

Evolutionary Multilabel Feature Selection Using Promising Feature Subset Generation

Recent progress in the development of sensor devices improves information harvesting and allows complex but intelligent applications based on learning hidden relations between collected sensor data and objectives. In this scenario, multilabel feature selection can play an important role in achieving better learning accuracy when constrained with limited resources. However, existing multilabel feature selection methods are search-ineffective because generated feature subsets frequently include unimportant features. In addition, only a few feature subsets compared to the search space are considered, yielding feature subsets with low multilabel learning accuracy. In this study, we propose an effective multilabel feature selection method based on a novel feature subset generation procedure. Experimental results demonstrate that the proposed method can identify better feature subsets than conventional methods.