Volume 10 | Issue 5 |

 Abstract

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.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Malware detection, deep learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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.

Licence: creative commons attribution 4.0

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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

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

 Keywords

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

 Abstract

: 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.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Grocery shopping recommendation, Popularity-based performance evaluation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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.

Licence: creative commons attribution 4.0

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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

 Abstract

This is a project run under Google X. Google will store the genome in the cloud for $25, and the storage system could have a major impact on the scientific community. The hope is to collect millions of genomes to aid in scientific research. As MIT Review reports, the system could aid in collecting "cancer genome clouds" that would allow scientists to share information and run virtual experiments. Significant advancements in genomics analysis have been made possible with the emergence of better and more cost-effective tools. For example, cloud-based technologies, such as those offered by Google Cloud Platform (GCP), provide computational resources capable of analyzing massive amounts of genomic information at unprecedented speeds and in many cases, at a lower cost compared with on-premises solutions. Today, the use of cloud-based tools enables analysis across thousands of genomes to identify patterns and markers for disease predisposition, prediction, and causality. This helps improve how healthcare providers understand and treat disease, and creates better-informed treatment plans for patients

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

google cloud ,cloud storing, genomics ,human genome

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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

Roman Settelement in Tamil Nadu, Greeks coins in Tamil Nadu, Roman coins of Tamil Nadu, Foreign Trade in Tamil Nadu, Yavanas, Ancient Coins, History, Sangam Trade, Greek Trade, Egyptian Trade, Literary Evidences for the Yavanas, Romanian trade and currency, Kongu Country and the Palakat Passes,

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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

Keywords: Bodo, Women & Status, Socio-Culture

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Creative Commons Attribution 4.0 and The Open Definition