Volume 10 | Issue 5 |

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Drainage Monitoring ,Manhole cover ,Asphyxiating Monitoring ,Cloud System ,Pixel analysis ,Pleural effusion ,Real time system ,Sewage system

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Education, Technology, Science, Firebase, Nodejs, Social Network, Querify, LinkedIn

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Creative Commons Attribution 4.0 and The Open Definition

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Twitter is one of the most widely used social media micro blogging sites. Mining user opinions from social media data is not a straight forward task; it canbe accomplished in different ways. In this work, an open source approach is presented, throughout which, twitter Micro blogs data has been collected, pre- processed analysed and visualized using open source tools to perform text mining and sentiment analysis for analyzing user contributed online reviews. Collectingcustomer task using conventional methods such as surveys.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

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Social media have become an emerging phenomenon due to the huge and rapid advances in information technology. People are using social media ondaily basis to communicate their opinions with each other about wide variety of subjects, products and services,

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Creative Commons Attribution 4.0 and The Open Definition

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Recent technological developments in computer systems transfer human life from real to virtual environments. Covid-19 disease has accelerated this process. Cyber criminals' interest has shifted from real to virtual life as well. This is because it is easier to commit a crime in cyberspace rather than in regular life. Malicious software (malware) is unwanted software which is frequently used by cyber criminals to launch cyber-attacks. Malware variants are continuing to evolve by using advanced obfuscation and packing techniques. These concealing techniques make malware detection and classification significantly challenging. Novel methods which are quite different from traditional methods must be used to effectively combat new malware variants. Traditional artificial intelligence (AI), specifically machine learning (ML) algorithms, are no longer effective in detecting all new and complex malware variants. A deep learning (DL) approach, which is quite different from traditional ML algorithms, can be a promising solution to the problem of detecting all variants of malware. In this study, a novel deep-learning-based architecture is proposed which can classify malware variants based on a hybrid model. The main contribution of the study is to propose a new hybrid architecture which integrates two wide-ranging pre-trained network models in an optimized manner. This architecture consists of four main stages, namely: data acquisition, the design of deep neural network architecture, training of the proposed deep neural network architecture, and evaluation of the trained deep neural network. The proposed method was tested on Malimg, Microsoft BIG 2015, and Malevis datasets. The experimental results show that the suggested method can effectively classify malware with high accuracy, which outperforms the state of the art methods in the literature. When the proposed method was tested on the Malimg dataset, 97.78% accuracy was obtained, which outperformed most of the ML-based malware detection methods.

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Creative Commons Attribution 4.0 and The Open Definition

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Malware detection, deep learning

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Creative Commons Attribution 4.0 and The Open Definition

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Visually impaired have limitations in terms of mobility and they have a deficient vision. According to WHO, there are 285 billion visually impaired and 30 million permanently blind people in this world. To help visually impaired Blind sticks support in scanning their environment. For better support, to the subject, the current Blind sticks need technological advancement. Hence, with the accession of a microcontroller, buzzer and ultrasonic sensor, this paper proposes a modification to Blind sticks. In order to know the current location of the user, this model encompasses a GPS and GSM module which transfer the location details to the Kith and Kins of the user. The device is aimed to be a cost-effective and user-friendly model for aiding Visually impaired people which guarantees high-performance reliable navigation and an even better user experience.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Microcontroller, Ultrasonic sensor, GPS, GSM and Buzzer

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Waste Plastic, Concrete, Plastic Pieces, Compressive strength

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

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Extended Marketing Mix, Marketing Strategy, Niche, Physical Evidence, Processes, People, Stereotypic Products

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Creative Commons Attribution 4.0 and The Open Definition

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: The goal of recommendation systems is to predict a user's preference or rating for a particular item. The problem of making personalized recommendations about items or information during a user's visit to a website can be solved by applying knowledge discovery techniques. A collaborative filtering algorithm provides recommendations based on the ratings of other users in the system. Scalability, sparsity, and cold start are issues faced by traditional collaborative filtering algorithms. A combination of item-based collaborative filtering and demographics-based user clusters is used in the proposed framework for predicting user behavior. It is scalable and addresses user cold start.

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Creative Commons Attribution 4.0 and The Open Definition

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Grocery shopping recommendation, Popularity-based performance evaluation.

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Creative Commons Attribution 4.0 and The Open Definition

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Economy contributes the most for the productivity of the agriculture. In agricultural field, the disease in plants is more common and the detection of disease in plants has become more feasible due to the above reason. These days's plant disease detection has acquired enlarging scrutiny in surveilling crops of large and various fields. Farmers undergo significant hassles in chop and changing from one disease administer principle to a different one. We can identify or spotting the tomato leaf diseases for detection for surveillance and monitoring experts is the standard approach for detection. The plants get seriously affected if the proper control hasn't been taken and this represents the quality of the pants the production of the plants will be affected. Detection of disease through some mechanized technique and methodology is efficient and constructive because it decreases an outsized toil of surveilling in the large cultivation. In the premature phase we can detect the symptoms of the plant diseases since their first appearance on their leaves of the plants. By using this paper we can identify the algorithm which is used for image segmentation and for automated classification used for the detection of diseases of leaves in the plants. It also covers distinct disease classification methods of working which is used for the detection of diseases in plants. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Plant leaf disease images, deep learning, Machine Learning, SVC, ANN, CNN, Resnet50.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

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Key Words: Yogic Practices, Therapeutic Exercises, Antenatal, Perceived stress, Perceived Stress Assessment Scale.

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Creative Commons Attribution 4.0 and The Open Definition