TeKET: a Tree-Based Unsupervised Keyphrase Extraction Technique

Automatic keyphrase extraction techniques aim to extract quality keyphrases for higher level summarization of a document. Majority of the existing techniques are mainly domain-specific, which require application domain knowledge and employ higher order statistical methods, and computationally expensive and require large train data, which is rare for many applications. Overcoming these issues, this paper proposes a new unsupervised keyphrase extraction technique. The proposed unsupervised keyphrase extraction technique, named TeKET or Tree-based Keyphrase Extraction Technique, is a domain-independent technique that employs limited statistical knowledge and requires no train data. This technique also introduces a new variant of a binary tree, called KeyPhrase Extraction (KePhEx) tree, to extract final keyphrases from candidate keyphrases. In addition, a measure, called Cohesiveness Index or CI, is derived which denotes a given node’s degree of cohesiveness with respect to the root. The CI is used in flexibly extracting final keyphrases from the KePhEx tree and is co-utilized in the ranking process. The effectiveness of the proposed technique and its domain and language independence are experimentally evaluated using available benchmark corpora, namely SemEval-2010 (a scientific articles dataset), Theses100 (a thesis dataset), and a German Research Article dataset, respectively. The acquired results are compared with other relevant unsupervised techniques belonging to both statistical and graph-based techniques. The obtained results demonstrate the improved performance of the proposed technique over other compared techniques in terms of precision, recall, and F1 scores

The inherent tensions within sustainable supply chains: a case study from Bangladesh

The complexities surrounding the supply chain logistics for perishable commodities within Bangladesh are extensive. Poor infrastructure, fragmented transportation and corruption compound the operational complexities within this emerging market. This case study analyses many of the day-to-day operational challenges and tensions inherent within Micro-Small-Medium Enterprises (MSMEs) forming the backbone of the Bangladesh socio-economic structure. The drive for transition toward greater levels of sustainability and corporate responsibility is problematic, affecting many levels within an extended and fragmented supply chain. The selected case study highlights the “lived in” geographical, environmental, economic and cultural factors that impact the ability of emerging market enterprises to remain profitable within emergency scenarios whilst transitioning toward a more sustainable model. This study, whilst detailing many of the tensions and critical issues facing MSMEs, highlights the benefits of direct Government intervention, criticality of a leaner and more efficient supply chain and reassessment of financial incentives to drive the transition to a more efficient and sustainable economy

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided

Automated briquetting machine

Recycling is a word commonly heard at present especially now that the earth is running low in natural resources. Wood is one of these natural resources that we use and need every day: it serves as our shelter and is used in construction, a basic raw material for fuel, and sometimes, it is even used for cooking. Wood has abundant functions in our daily living and that is the reason why the group’s interest in finding solutions for word recycling grew. The group’s thesis focuses in a new way of recycling wood, it aims to combine engineering solutions and the idea of recycling to fabricate a machine that can process wood waste or sawdust and turn them into wood briquettes. Wood briquettes are compacted wood materials that can be used as an alternative fuel source to replace firewood and charcoal. Fourkings Lumber and Construction Supply Incorporated gained an interest in sponsoring the group’s thesis of making an 2Automated Briquetting Machine3. The said machine would process 40 kg of sawdust per hour and turned these wastes into wood briquettes. The machine has a built-in-hopper, attached to a screw conveyor that regulates the feed of materials into the compression chamber. It uses hydraulic cylinders to compress the wood waste through a heated die to stabilize the hardness of the wood briquettes. The machine is installed with a programmable logic controller, working with pressure switches and proximity sensors, to automate the manufacturing process of the machine. With the automation of the machine, production only requires a single operator. Manual processes only include the feeding of raw materials and the collection of end products. By fabricating the 2Automated Briquetting Machine3, the sponsor not only finds a new solution for these space-consuming waste, but it would also provide them with a new business opportunity for the company