scholarly journals Potential of deep learning in drought assessment by extracting information from hydrometeorological precursors

2021 ◽  
Author(s):  
Rajib Maity ◽  
Mohd Imran Khan ◽  
Subharthi Sarkar ◽  
Riya Dutta ◽  
Subhra Sekhar Maity ◽  
...  
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Climate Model ◽  
Meteorological Drought ◽  
Research Directions ◽  
Drought Assessment ◽  
Skill Scores ◽  
Basin Scale ◽  
New Research ◽  
Temperature Surface

Abstract This study explores the potential of deep learning (DL) approach to develop a model for basin-scale drought assessment using information from a set of primary hydrometeorological precursors, namely air temperature, surface pressure, wind speed, relative humidity, evaporation, soil moisture and geopotential height. The novelty of the study lies in extracting the information from the hydrometeorological precursors through the efficacy of DL algorithm, based on 1-dimensional convolutional neural network. Drought-prone regions, from where our study basins are selected, often suffer from the vagaries of rainfall that leads to drought-like situations. It is established that the proposed DL-based model is able to capture the underlying complex relationship between rainfall and the set of aforementioned hydrometeorological variables and subsequently, shows its promise for the basin-scale meteorological drought assessment as revealed through different performance metrics and skill scores. The accuracy of simulating the correct drought category, among the seven categories, is also high (>70%). Moreover, in general, the skill of any climate model is much higher for the primary meteorological variables as compared with other secondary or tertiary variables/phenomena, like droughts. Thus, the novelty of the proposed DL-based model also lies in the improved assessment of ensuing basin-scale meteorological droughts using the projected meteorological precursors and may lead to new research directions.

scholarly journals A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7543
Author(s):  
Bogdan Ilie Sighencea ◽  
Rareș Ion Stanciu ◽  
Cătălin Daniel Căleanu
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Evaluation Process ◽  
Surveillance Systems ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Trajectory Prediction ◽  
Processing Technologies ◽  
Practical Applications ◽  
New Research

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.

scholarly journals A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6116
Author(s):  
Muhammad Firoz Mridha ◽  
Md. Abdul Hamid ◽  
Muhammad Mostafa Monowar ◽  
Ashfia Jannat Keya ◽  
Abu Quwsar Ohi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Performance Metrics ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Imaging Modalities ◽  
Research Directions ◽  
Comprehensive Survey ◽  
Processing Techniques

Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.

INNOVATION CAPABILITY, ABSORPTIVE CAPACITY AND SMES PERFORMANCE IN PAKISTAN: THE MODERATING EFFECT OF BUSINESS STRATEGY

2018 ◽  
Vol 13 (Number 2) ◽  
pp. 1-11
Author(s):  
Muhammad Zulqarnain Arshad ◽  
Darwina Arshad
Keyword(s):  
Economic Growth ◽  
Absorptive Capacity ◽  
Business Strategy ◽  
Innovation Capability ◽  
Research Directions ◽  
Conceptual Paper ◽  
And Performance ◽  
New Research ◽  
The Relationship ◽  
Crucial Part

The small and medium-sized enterprises (SMEs) play a crucial part in county’s economic growth and a key contributor in country’s GDP. In Pakistan SMEs hold about 90 percent of the total businesses. The performance of SMEs depends upon many factors. The main aim for the research is to examine the relationship between Innovation Capability, Absorptive Capacity and Performance of SMEs in Pakistan. This conceptual paper also extends to the vague revelation on Business Strategy in which act as a moderator between Innovation Capability, Absorptive Capacity and SMEs Performance. Conclusively, this study proposes a new research directions and hypotheses development to examine the relationship among the variables in Pakistan’s SMEs context.

MATERIAL HANDLING MACHINES AND SYSTEMS – UMI-TWINN PROJECT CONTRIBUTION

2019 ◽  
Vol 12 (1) ◽  
pp. 7-20
Author(s):  
Péter Telek ◽  
Béla Illés ◽  
Christian Landschützer ◽  
Fabian Schenk ◽  
Flavien Massi
Keyword(s):  
Material Handling ◽  
Digital Data ◽  
Flow Process ◽  
Research Directions ◽  
Scientific Excellence ◽  
Research Activities ◽  
New Research ◽  
Eu Project ◽  
The University

Nowadays, the Industry 4.0 concept affects every area of the industrial, economic, social and personal sectors. The most significant changings are the automation and the digitalization. This is also true for the material handling processes, where the handling systems use more and more automated machines; planning, operation and optimization of different logistic processes are based on many digital data collected from the material flow process. However, new methods and devices require new solutions which define new research directions. In this paper we describe the state of the art of the material handling researches and draw the role of the UMi-TWINN partner institutes in these fields. As a result of this H2020 EU project, scientific excellence of the University of Miskolc can be increased and new research activities will be started.

scholarly journals Antimicrobial and Amyloidogenic Activity of Peptides Synthesized on the Basis of the Ribosomal S1 Protein from Thermus Thermophilus

2020 ◽  
Vol 21 (17) ◽  
pp. 6382 ◽  
Author(s):  
Stanislav Kurpe ◽  
Sergei Grishin ◽  
Alexey Surin ◽  
Olga Selivanova ◽  
Roman Fadeev ◽  
...  
Keyword(s):  
Antimicrobial Activity ◽  
Thermus Thermophilus ◽  
Antibacterial Properties ◽  
Cell Penetration ◽  
Research Directions ◽  
Bacterial Proteins ◽  
Hiv Transcription ◽  
Parent Protein ◽  
Amyloidogenic Peptides ◽  
New Research

Controlling the aggregation of vital bacterial proteins could be one of the new research directions and form the basis for the search and development of antibacterial drugs with targeted action. Such approach may be considered as an alternative one to antibiotics. Amyloidogenic regions can, like antibacterial peptides, interact with the “parent” protein, for example, ribosomal S1 protein (specific only for bacteria), and interfere with its functioning. The aim of the work was to search for peptides based on the ribosomal S1 protein from T. thermophilus, exhibiting both aggregation and antibacterial properties. The biological system of the response of Gram-negative bacteria T. thermophilus to the action of peptides was characterized. Among the seven studied peptides, designed based on the S1 protein sequence, the R23I (modified by the addition of HIV transcription factor fragment for bacterial cell penetration), R23T (modified), and V10I (unmodified) peptides have biological activity that inhibits the growth of T. thermophilus cells, that is, they have antimicrobial activity. But, only the R23I peptide had the most pronounced activity comparable with the commercial antibiotics. We have compared the proteome of peptide-treated and intact T. thermophilus cells. These important data indicate a decrease in the level of energy metabolism and anabolic processes, including the processes of biosynthesis of proteins and nucleic acids. Under the action of 20 and 50 μg/mL R23I, a decrease in the number of proteins in T. thermophilus cells was observed and S1 ribosomal protein was absent. The obtained results are important for understanding the mechanism of amyloidogenic peptides with antimicrobial activity and can be used to develop new and improved analogues.

scholarly journals A Study on the Performance of Unconstrained Very Low Resolution Face Recognition: Analyzing Current Trends and New Research Directions

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Luis S. Luevano ◽  
Leonardo Chang ◽  
Heydi Mendez-Vazquez ◽  
Yoanna Martinez-Diaz ◽  
Miguel Gonzalez-Mendoza
Keyword(s):  
Face Recognition ◽  
Low Resolution ◽  
Research Directions ◽  
Current Trends ◽  
New Research

The Role of Perceived Risk in Engaging Customer and Employees for Value Creation in Services

2021 ◽  
pp. 097226292110225
Author(s):  
Ritu Srivastava ◽  
Diptiman Banerji ◽  
Priyanka Nema ◽  
Shubham Choudhary
Keyword(s):  
Employee Engagement ◽  
Value Creation ◽  
Perceived Risk ◽  
Organizational Outcomes ◽  
Customer Engagement ◽  
Future Research ◽  
Second Phase ◽  
Research Directions ◽  
Marketing Effectiveness ◽  
New Research

Value creation, customer engagement and employee engagement have emerged as important organizational outcomes for continued success. At the turn of the new decade, it is imperative to identify new research directions for these outcomes to improve the marketing effectiveness of organizations while keeping people at the centre of this pursuit. The present study is propelled by this motivation. The study started with the exploration of the relationship of customer and employee engagement in value creation, while limiting the scope to services. The extant literature has not studied the three together. The second phase of the study dwelled on identifying common links among the three to develop a conceptual model that brought the concepts of customer engagement, employee engagement and value creation together. Perceived risk was identified as the underlying phenomenon that connected all three to be part of a social system. A conceptual framework has been proposed for connecting perceived risk to customer engagement and employee engagement that would create value in service organizations. The study identifies future research directions for theory building and practice.

Survey of Uses of Evolutionary Computation Algorithms and Swarm Intelligence for Network Intrusion Detection

2015 ◽  
Vol 14 (04) ◽  
pp. 1550025 ◽  
Author(s):  
Saber Elsayed ◽  
Ruhul Sarker ◽  
Daryl Essam
Keyword(s):  
Intrusion Detection ◽  
Swarm Intelligence ◽  
Computational Intelligence ◽  
Network Intrusion Detection ◽  
The Internet ◽  
Research Directions ◽  
Related Literature ◽  
Network Intrusion ◽  
Computation Algorithms ◽  
New Research

Many infrastructures, such as those of finance and banking, transportation, military and telecommunications, are highly dependent on the Internet. However, as the Internet’s underlying structural protocols and governance can be disturbed by intruders, for its smooth operation, it is important to minimize such disturbances. Of the available techniques for achieving this, computational intelligence methodologies, such as evolutionary algorithms and swarm intelligence approaches, are popular and have been successfully applied to detect intrusions. In this paper, we present an overview of these techniques and related literature on intrusion detection, analyze their research contributions, compare their approaches and discuss new research directions which will provide useful insights for intrusion detection researchers and practitioners.

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