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Welcome to Tru Projects. Tru Projects are being introduced to make a long way from its beginnings. We are the one source and efficient student oriented service provider for IT projects.

We expertise and are the best mutually proclaimed in Platform for Academic Live Projects, for students of B.Tech, M.Tech, MBA, MCA. We are dedicated to giving you the best of IT projects, with a focus on Quality, Dependability, Structure and much more.

Tru Projects is applying its way through, to help with the best of service for students in Implementing CSE Projects in skill sets – Data Sciences, Machine Learning, Artificial Intelligence, Deep Learning, Android, Big Data, Block Chain and Java Related Technologies with the best equipment which drives them to inspiration.

We serve students with Implementing in Students self ideas / Abstracts / Thoughts, and we are thrilled to be a part of the Student Faternity.We hope you appreciate our IT Projects, as much as we enjoy offering them to you. And for any questions and guidance on our projects, please do revert to us.

Are you having trouble deciding which of the top engineering projects consultants in Hyderabad or Final Year projects providers in Hyderabad can offer you with an exceptional graduation project? Don’t worry, we at Tru Projects are here to help you make your work easier by providing you with valuable hands-on expertise. Don’t bite your nails now the services are available near to your home town Sr nagar , Ameerpet , Jntu , Kphp , Kukatpally , Dilshuknagar , Madhapur , L.B.Nagar ,Sec-bad ,Tarnaka , Uppal , Chaitanya Puri , ECIL , Ibrahimpat , Adilabad ,Uturu, Mancherial , Nirmal , Bhainsa, Asifabad , Karimnagar, Huzurabad , jagtial, Mettupalli ,Peddapalli, Mantini ,Sircilla , Nizamabad, Armoor ,Bodhan, Kamareddy, Banswada, Yellareddy , Ellareddy, Warangal (Rural), Narsampet , Bhupalpally, Mulugu ,Jangaon, Station Ghanpur, Mahabubabad, Thorrur , Khammam, kalluru , Kothagudem, Bhadrachalam ,Medak, Toopran, Narsapur ,Sangareddy, Zahirabad, Narayankedh. 

“Through hands-on experience, our projects have shown to be an effective recruitment tool in aiding students in hiring the relevant individuals. Our Final Year projects consultants in Hyderabad & projects providers in Hyderabad are skilled “Head Hunters” who bring expertise, a good attitude, and a kind approach to the table. They provide excellent direction at every level of the project implementation process, and they are constantly keen to offer suggestions while truly interested in our accomplishment. Choosing best btech projects consultants in Hyderabad is challenging. Tru projects for Final Year academic projects consultants in Hyderabad is a dream come true.”

Selecting a noteworthy Project Consultant for Btech Projects in Hyderabad for your academic year might be difficult. You’ll have to locate the essential equipment, as well as look for the best engineering projects providers in Hyderabad, as well as select the appropriate field in Btech projects for engineering students. Tru Projects is the most effective option. Are You looking actual competency-based Final Year cse projects consultants in Hyderabad & Final Year ece projects consultants in Hyderabad with ensure quality, and within lower expenditures? Tru Projects will be one of the best for all of and also provides best btech projects providers in Hyderabad.

Tru Projects is delighted to be one of the best btech academic projects consultants in Hyderabad for the btech cse projects consultants as well as btech ece projects consultants in Hyderabad, offering students a wide range of customized B TECH major and mini projects, B TECH major & mini projects, MBA major and mini projects, and so on. Among the domains in which we specialize are Data Mining, Artificial Intelligence, Big Data, Deep Learning, Data Science, Android, cloud computing, and Cyber security.

So, if you’re looking for Best Project Consultant for Final Year. you’ve come to the right place. All you have to do now is give us a call for more information and to get started on your project. Tru Projects makes it easy for you to get all the best complete Final Year projects in one place and at your own convenience.

How about a web developer or a software developer? Find the best role for you by working on one of our most challenging engineering projects? For those who simply want a grade boost, you may want to look for engineering academic projects. Tru Projects has a wide range of trending b tech projects for you to choose from, so waste no more time and contact us.

Choosing a major project in your final year of college is a huge undertaking. You will have to decide on a specific field, gather all the required equipment, and write the source code that goes along with it. There are so many computer science subfields, such as java, natural language processing, and cloud computing, which picking just one can be difficult. The latest trending Final Year projects in Hyderabad are all available at Tru Projects, you can contact us to find more details. Our team is made up of highly qualified individuals who will work with you from the beginning to the end of any academic project you select for your final year.

Hey student! Looking for the latest B tech CSE Projects? Or maybe b tech CSE academic projects?  Do you want to become a computer network architect, data analyst or a systems analyst? Maybe figure out the best suitable role for you by doing one of our  Projects for B tech CSE Students? Or are you just searching for the Real Time Projects for B tech CSE Students, to score those extra points? Tru Projects has gotten just the wide variety of projects for you.

Most projects are difficult to choose from, right? In the computer science department, engineering students are exposed to a variety of disciplines. Here are a few domains and their uses, so you can better understand and choose the right project from our wide range of B tech CSE Projects with Source Code and Document. Our team consists of experienced professionals who will help you from the beginning till the end of any b tech CSE live projects, you choose from.

The most difficult part of a project is choosing one, right? Engineering students in the computer science department are taught multiple areas as disciplines. Let us see a few domains and their uses, to make you understand better and choose your suitable project from our wide range of projects for b tech CSE final year students.

Computer languages like Java are very popular. Over time, it has become more and more popular. In the field of mobile application development, Java is the official programming language. For example, this means that Android Studio can be used. Java can be used to create any desktop application. In addition to Web applications, Java is also used to develop them.

Python is not only easy to learn, but it’s also easy to implement and use. With a syntax that is like English, you can learn Python coding in a few days and get up to speed quickly. In addition, Python is dynamically typed, which makes indentation mandatory, increasing its readability factor. The fact that Python is a high-level language means that you don’t have to worry about its system architecture or memory management. This feature contributes to the user-friendliness of Python by making it easier to use.

One of the most important aspects of cloud computing is the ability to store and retrieve data from a variety of sources. You can keep an eye on the client’s abilities, as well as the amount of available storage on the network. Because of this, a client can also control the computing capabilities to suit his needs. Examples of free photo editing software include Adobe Photoshop, GIMP, and others. These cloud computing services include image resizing, editing, cropping, special effects, and more.

With the introduction of GPU-processors, Deep Learning has played a major role in everything from medical image analysis to curing diseases. Physicians, Clinicians, and Doctors can also use it to help patients out of danger, diagnose, and treat them with the right medicines, thanks to it.

While Data Mining has many applications in the Retail Industry, it is particularly useful for gathering large amounts of information about sales figures and customer purchasing habits as well. Data collection will continue to grow rapidly because of the ease, availability, and popularity of the internet. Customer buying patterns and trends can be identified through data mining in the retail industry, resulting in better customer service and higher levels of customer retention and satisfaction.

Improved yield and increased research and development of growing crops are among the benefits of new advances in artificial intelligence (AI). Farming has become more efficient thanks to new artificial intelligence that can predict when a fruit or vegetable will be ripe and ready for harvesting. [4] Agricultural Robots, Crop and Soil Monitoring, and Predictive Analytics are just a few of the advances that have been made in recent years. Growing crops can be improved with the help of artificial intelligence (AI), which is advancing rapidly. Crop and soil monitoring, agricultural robots, as well as predictive analytics are all factors that can be predicted by artificial intelligence (AI).

When using Google, we have the option to “search by voice,” which falls under speech recognition and is a popular machine learning application. As well as being called “Speech to text” or “Computer speech recognition,” speech recognition converts spoken instructions into text. As of right now, a variety of speech recognition applications use machine learning algorithms. Voice-activated assistants, such as Google Assistant, Siri, Cortana, and Alexa, use speech recognition technology to follow voice commands.

Image sharpening and restoration refers to the process of improving images captured by modern cameras or manipulating images to achieve a desired result. As the name suggests, it refers to what Adobe Photoshop does. This includes zooming, blurring, sharpening, grey scale to color conversion, detecting edges and vice versa, image retrieval, and image recognition.

Cyber security includes network, application, information, and security procedures. They have disaster recovery capabilities, as well as customer education programs. To protect our networks, we must take all necessary precautions, methods, and strategies. There are several networks that we can all use to communicate or perform any kind of operation. For example, a strong cyber security program could help us protect our networks against criminal activity and unauthorized access.

In the banking industry, big data has been used primarily in the tracking department. Online shopping is becoming more popular and as a result, we face several issues, such as hacking and unauthorized access to credit cards. By utilizing big data analytics, each financial institution can set up their own security features for customers. Thereby reducing the likelihood of future illegal and unauthorized activities.

Travel is a well-known example of how data science can be used. It’s easy to see that new vehicle, whether they’re autos, motorcycles, or automobiles, are being introduced every day. There is a lot of pollution because of all this. Aside from reducing the amount of pollution caused by driving by bicycling and taking public transportation, the use of big data analytics can improve road routing. Companies that don’t care about the environment can save thousands of gallons of gas by modifying their routes based on data.

People are increasingly using social media to share their opinions on a particular product, policy, or issue. These can provide valuable insight into a person’s tastes and dislikes. As a result, analysing this unstructured data can lead to valuable insights and new ideas for the future. NLP comes to the rescue once more in this case.

As a result, medical records have been accessed and misused in several instances. Now, the medical industry is relying on the blockchain concept to make sure that such cases do not occur again in the future. As soon as a patient signs a document, it is immediately entered into the blockchain. In this case, it’s locked and only a key holder can get into the box. In this way, blockchain contributes to the protection of medical data.

The term computer networking refers to the connection of two or more systems or computers solely for the purpose of exchanging information. TCP, UDP, SMTP, FTP, and other computer networking protocols are examples of these types of protocols in computer networks.

It’s a given that students are familiar with Microsoft Office Word and PowerPoint. From the time we were in high school, we’ve been using both applications. Create a document with your choice of text style (font), size of words, color and more with Microsoft Word. The options are endless. It is possible to create different types of documents based on your preferences.

So, if you are in search of  Projects for B tech CSE Students in Hyderabad, we are your perfect choice of consultant.  When you are an engineering student without good supervision, it may feel impossible. So that you don’t have to waste time searching, all of our projects come with the source code and all of the essential paperwork, so you can easily find the best projects for b tech CSE final year students in Hyderabad. All you need to do is give us a call for more information and start with your project right away.

During these pandemic times, searching for a genuine project consultant itself is a task. You need not worry as Tru Projects is one of the best academic projects consultants and we take pride in offering some of the best B tech CSE Projects in Hyderabad. Our team works hard with the only goal in mind which is to make sure any student can access our B tech CSE Projects with Source Code and Document in Hyderabad, during these times at their own convenience.

If you need any kind of project related information please visit our website and see the wide variety of Real Time Projects for B tech CSE Students in Hyderabad, which we offer. Also feel free to contact us for any queries or details. We would love to help you to choose from the best b tech CSE live projects in Hyderabad, and work with you.  Don’t bite your nails now the services are available near to your home town Sr nagar , Ameerpet , Jntu , Kphp , Kukatpally , Dilshuknagar , Madhapur , L.B.Nagar ,Sec-bad ,Tarnaka , Uppal , Chaitanya Puri , ECIL , Ibrahimpat , Adilabad ,Uturu, Mancherial , Nirmal , Bhainsa, Asifabad , Karimnagar, Huzurabad , jagtial, Mettupalli ,Peddapalli, Mantini ,Sircilla , Nizamabad, Armoor ,Bodhan, Kamareddy, Banswada, Yellareddy , Ellareddy, Warangal (Rural), Narsampet , Bhupalpally, Mulugu ,Jangaon, Station Ghanpur, Mahabubabad, Thorrur , Khammam, kalluru , Kothagudem, Bhadrachalam ,Medak, Toopran, Narsapur ,Sangareddy, Zahirabad, Narayankedh.

YouTube

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Abstract—In this paper, we focus on the one-example person re-identification (re-ID) task, where each identity has only one labeled example along with many unlabeled examples. We propose a progressive framework which gradually exploits the unlabeled data for person re-ID. In this framework, we iteratively (1) update the Convolutional Neural Network (CNN) model and (2) estimate pseudo labels for the unlabeled data. We split the training data
into three parts, i.e., labeled data, pseudo-labeled data, and indexlabeled data. Initially, the re-ID model is trained using the labeled data. For the subsequent model training, we update the CNN model by the joint training on the three data parts. The proposed joint training method can optimize the model by both the data with labels (or pseudo labels) and the data without any reliable labels. For the label estimation step, instead of using a static
sampling strategy, we propose a progressive sampling strategy to increase the number of the selected pseudo-labeled candidates step by step. We select a few candidates with most reliable pseudo labels from unlabeled examples as the pseudo-labeled data, and keep the rest as index-labeled data by assigning them with the data indexes. During iterations, the index-labeled data are dynamically transferred to pseudo-labeled data. Notably, the
rank-1 accuracy of our method outperforms the state-of-the-art method by 21.6 points (absolute, i.e., 62.8% vs. 41.2%) on MARS, and 16.6 points on DukeMTMC-VideoReID. Extended to the few-example setting, our approach with only 20% labeled data surprisingly achieves comparable performance to the supervised state-of-the-art method with 100% labeled data

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Abstract—In this paper, we focus on the one-example person re-identification (re-ID) task, where each identity has only one labeled example along with many unlabeled examples. We propose a progressive framework which gradually exploits the unlabeled data for person re-ID. In this framework, we iteratively (1) update the Convolutional Neural Network (CNN) model and (2) estimate pseudo labels for the unlabeled data. We split the training data
into three parts, i.e., labeled data, pseudo-labeled data, and indexlabeled data. Initially, the re-ID model is trained using the labeled data. For the subsequent model training, we update the CNN model by the joint training on the three data parts. The proposed joint training method can optimize the model by both the data with labels (or pseudo labels) and the data without any reliable labels. For the label estimation step, instead of using a static
sampling strategy, we propose a progressive sampling strategy to increase the number of the selected pseudo-labeled candidates step by step. We select a few candidates with most reliable pseudo labels from unlabeled examples as the pseudo-labeled data, and keep the rest as index-labeled data by assigning them with the data indexes. During iterations, the index-labeled data are dynamically transferred to pseudo-labeled data. Notably, the
rank-1 accuracy of our method outperforms the state-of-the-art method by 21.6 points (absolute, i.e., 62.8% vs. 41.2%) on MARS, and 16.6 points on DukeMTMC-VideoReID. Extended to the few-example setting, our approach with only 20% labeled data surprisingly achieves comparable performance to the supervised state-of-the-art method with 100% labeled data

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YouTube Video UExMYllTUHVicmxINDBtTDRKOUhrMTM2UHZzeTZ1WWdDby40NzZCMERDMjVEN0RFRThB

Progressive Learning for Person Re-Identification with One Example

TRU PROJECTS July 3, 2020 10:54 pm

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ABSTRACT :

signals recorded in a room are commonly degraded by reverberation. In most cases, both the speech signal and the acoustic system of the room are unknown and time-varying. Eliminating this effect without affecting the original quality of the speech is a challenge of research in present days. The process of DE noising input speech signals is more helpful in the process of providing efficient sound system. Fast adaptive Kalman filter is designed for the removal of the noises in the signals.

Fast adaptive Kalman filtering is employed for the removal of the noises from the signal which is based on the prediction and estimation of the noise level in the signal. The input speech signal is denoised with the help of the Fast adaptive Kalman filter. State transition and observation models need not be linear functions of the state but may instead be non-linear functions. The function can be used to compute the predicted state from the previous estimate and similarly the function can be used to compute the predicted measurement from the predicted state.

The nonlinear functions employed for the prediction and estimation process improves the performance of the adaptive kalman filter process. The non-linear functions defined for the kalman filtering process is designed so that the process is reduced in iterations and more adaptive. And using signal subspace algorithm.

The filtered signal and the original signal is then compared in order to measure the performance of the process. The performance is measured with the help of performance metrics like SNR

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ABSTRACT :

signals recorded in a room are commonly degraded by reverberation. In most cases, both the speech signal and the acoustic system of the room are unknown and time-varying. Eliminating this effect without affecting the original quality of the speech is a challenge of research in present days. The process of DE noising input speech signals is more helpful in the process of providing efficient sound system. Fast adaptive Kalman filter is designed for the removal of the noises in the signals.

Fast adaptive Kalman filtering is employed for the removal of the noises from the signal which is based on the prediction and estimation of the noise level in the signal. The input speech signal is denoised with the help of the Fast adaptive Kalman filter. State transition and observation models need not be linear functions of the state but may instead be non-linear functions. The function can be used to compute the predicted state from the previous estimate and similarly the function can be used to compute the predicted measurement from the predicted state.

The nonlinear functions employed for the prediction and estimation process improves the performance of the adaptive kalman filter process. The non-linear functions defined for the kalman filtering process is designed so that the process is reduced in iterations and more adaptive. And using signal subspace algorithm.

The filtered signal and the original signal is then compared in order to measure the performance of the process. The performance is measured with the help of performance metrics like SNR

0 0

YouTube Video UExMYllTUHVicmxINDBtTDRKOUhrMTM2UHZzeTZ1WWdDby5EMEEwRUY5M0RDRTU3NDJC

Online Speech Enhancement Using Fast Adaptive Kalman Filter with Signal Subspace Algorithm

TRU PROJECTS July 3, 2020 10:53 pm

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ABSTRACT Machine learning techniques are being widely used to develop an intrusion detection system(IDS) for detecting and classifying cyber-attacks at the network-level and host-level in a timely andautomatic manner. However, many challenges arise since malicious attacks are continually changing and areoccurring in very large volumes requiring a scalable solution. There are different malware datasets available
publicly for further research by cyber security community. However, no existing study has shown thedetailed analysis of the performance of various machine learning algorithms on various publicly available
datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware data sets available publicly are to be updated systematically and benchmarked. In this paper, deep neural
network (DNN), a type of deep learning model is explored to develop a flexible and effective IDS to detectand classify unforeseen and unpredictable cyber-attacks. The continuous change in network behaviour and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm
which can effectively work in detecting future cyber-attacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs is chosen through following hyper parameter selection methods with KDDCup 99 dataset. All experiments of DNNs are run till 1,000 epochs with learning rate varying in the range [0.01-0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS and
CICIDS 2017 to conduct the benchmark. Our DNN model learns the abstract and high dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental
testing it is confirmed that DNNs perform well in comparison to the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called Scale-Hybrid-IDS-AlertNet
(SHIA) which can be used in real time to effectively monitor the network traffic and host-level events to proactively alert possible cyber-attacks.

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ABSTRACT Machine learning techniques are being widely used to develop an intrusion detection system(IDS) for detecting and classifying cyber-attacks at the network-level and host-level in a timely andautomatic manner. However, many challenges arise since malicious attacks are continually changing and areoccurring in very large volumes requiring a scalable solution. There are different malware datasets available
publicly for further research by cyber security community. However, no existing study has shown thedetailed analysis of the performance of various machine learning algorithms on various publicly available
datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware data sets available publicly are to be updated systematically and benchmarked. In this paper, deep neural
network (DNN), a type of deep learning model is explored to develop a flexible and effective IDS to detectand classify unforeseen and unpredictable cyber-attacks. The continuous change in network behaviour and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm
which can effectively work in detecting future cyber-attacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs is chosen through following hyper parameter selection methods with KDDCup 99 dataset. All experiments of DNNs are run till 1,000 epochs with learning rate varying in the range [0.01-0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS and
CICIDS 2017 to conduct the benchmark. Our DNN model learns the abstract and high dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental
testing it is confirmed that DNNs perform well in comparison to the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called Scale-Hybrid-IDS-AlertNet
(SHIA) which can be used in real time to effectively monitor the network traffic and host-level events to proactively alert possible cyber-attacks.

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YouTube Video UExMYllTUHVicmxINDBtTDRKOUhrMTM2UHZzeTZ1WWdDby45ODRDNTg0QjA4NkFBNkQy

Deep Learning Approach for Intelligent Intrusion Detection System

TRU PROJECTS July 3, 2020 11:42 pm

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Abstract—Online social networks provide relevant information on users’ opinion about different themes. Thus, applications, such as monitoring and recommendation systems (RS) can collect and analyze this data. This paper
presents a knowledge-based recommendation system (KBRS), which includes an emotional health monitoring system to detect users with potential psychological disturbances, specifically, depression and stress. Depending onthe monitoring results, the KBRS, based on ontologies and
sentiment analysis, is activated to send happy, calm, relaxing, or motivational messages to users with psychological disturbances. Also, the solution includes a mechanism to send warning messages to authorized persons, in casea depression disturbance is detected by the monitoringsystem. The detection of sentences with depressive andstressful content is performed through a convolutional neural network and a bidirectional long short-term memory - recurrent neural networks (RNN); the proposed method reached an accuracy of 0.89 and 0.90 to detect depressed and stressed users, respectively. Experimental results show that the proposed KBRS reached a rating of 94% of very satisfied users, as opposed to 69% reached by a RS
without the use of neither a sentiment metric nor ontologies. Additionally, subjective test results demonstrated that the proposed solution consumes low memory, processing, and energy from current mobile electronic devices.

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Abstract—Online social networks provide relevant information on users’ opinion about different themes. Thus, applications, such as monitoring and recommendation systems (RS) can collect and analyze this data. This paper
presents a knowledge-based recommendation system (KBRS), which includes an emotional health monitoring system to detect users with potential psychological disturbances, specifically, depression and stress. Depending onthe monitoring results, the KBRS, based on ontologies and
sentiment analysis, is activated to send happy, calm, relaxing, or motivational messages to users with psychological disturbances. Also, the solution includes a mechanism to send warning messages to authorized persons, in casea depression disturbance is detected by the monitoringsystem. The detection of sentences with depressive andstressful content is performed through a convolutional neural network and a bidirectional long short-term memory - recurrent neural networks (RNN); the proposed method reached an accuracy of 0.89 and 0.90 to detect depressed and stressed users, respectively. Experimental results show that the proposed KBRS reached a rating of 94% of very satisfied users, as opposed to 69% reached by a RS
without the use of neither a sentiment metric nor ontologies. Additionally, subjective test results demonstrated that the proposed solution consumes low memory, processing, and energy from current mobile electronic devices.

1 0

YouTube Video UExMYllTUHVicmxINDBtTDRKOUhrMTM2UHZzeTZ1WWdDby41Mzk2QTAxMTkzNDk4MDhF

A Knowledge-Based Recommendation SystemThat Includes Sentiment Analysis and Deep Learning

TRU PROJECTS July 3, 2020 11:44 pm

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ABSTRACT Malicious software or malware continues to pose a major security concern in this digital age as computer users, corporations, and governments witness an exponential growth in malware attacks. Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviour patterns that are time consuming and ineffective in identifying unknown malwares. Recent malwares use polymorphic, metamorphic and other evasive techniques to change the malware behaviours quickly and to generate large number of malwares. Since new malwares are predominantly variants of existing malwares, machine learning algorithms (MLAs) are being employed recently to conduct an effective malware analysis. This requires extensive feature engineering, feature learning and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely
avoided. Though some recent research studies exist in this direction, the performance of the algorithms is biased with the training data. There is a need to mitigate bias and evaluate these methods independently
in order to arrive at new enhanced methods for effective zero day malware detection. To fill the gap in literature, this work evaluates classical MLAs and deep learning architectures for malware detection, classification and categorization with both public and private datasets. The train and test splits of public and private datasets used in the experimental analysis are disjoint to each others and collected in different timescales. In addition, we propose a novel image processing technique with optimal parameters for MLAs and deep learning architectures. A comprehensive experimental evaluation of these methods indicate that deep learning architectures outperform classical MLAs. Overall, this work proposes an effective visual
detection of malware using a scalable and hybrid deep learning framework for real-time deployments. The visualization and deep learning architectures for static, dynamic and image processing based hybrid
approach in a big data environment is a new enhanced method for effective zero-day malware detection. INDEX TERMS Cyber Security, Cybercrime, Malware detection, Static and Dynamic analysis, Artificial Intelligence, Machine Learning, Deep Learning, Image processing, Scalable and Hybrid framework
I. INTRODUCTION
In this digital world of Industry 4.0, the rapid advancement
of technologies has affected the daily activities in businesses
as well as in personal lives. Internet of Things (IoT) and
applications have led to the development of the modern
concept of the information society. However, security concerns
pose a major challenge in realising the benefits of
this industrial

TO PURCHASE THIS PROJECT IN ONLINE
CONTACT : TRU PROJECTS
WEBSITE : www.truprojects.in
MOBILE : 9676190678
MAIL ID : info@truprojects.in

ABSTRACT Malicious software or malware continues to pose a major security concern in this digital age as computer users, corporations, and governments witness an exponential growth in malware attacks. Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviour patterns that are time consuming and ineffective in identifying unknown malwares. Recent malwares use polymorphic, metamorphic and other evasive techniques to change the malware behaviours quickly and to generate large number of malwares. Since new malwares are predominantly variants of existing malwares, machine learning algorithms (MLAs) are being employed recently to conduct an effective malware analysis. This requires extensive feature engineering, feature learning and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely
avoided. Though some recent research studies exist in this direction, the performance of the algorithms is biased with the training data. There is a need to mitigate bias and evaluate these methods independently
in order to arrive at new enhanced methods for effective zero day malware detection. To fill the gap in literature, this work evaluates classical MLAs and deep learning architectures for malware detection, classification and categorization with both public and private datasets. The train and test splits of public and private datasets used in the experimental analysis are disjoint to each others and collected in different timescales. In addition, we propose a novel image processing technique with optimal parameters for MLAs and deep learning architectures. A comprehensive experimental evaluation of these methods indicate that deep learning architectures outperform classical MLAs. Overall, this work proposes an effective visual
detection of malware using a scalable and hybrid deep learning framework for real-time deployments. The visualization and deep learning architectures for static, dynamic and image processing based hybrid
approach in a big data environment is a new enhanced method for effective zero-day malware detection. INDEX TERMS Cyber Security, Cybercrime, Malware detection, Static and Dynamic analysis, Artificial Intelligence, Machine Learning, Deep Learning, Image processing, Scalable and Hybrid framework
I. INTRODUCTION
In this digital world of Industry 4.0, the rapid advancement
of technologies has affected the daily activities in businesses
as well as in personal lives. Internet of Things (IoT) and
applications have led to the development of the modern
concept of the information society. However, security concerns
pose a major challenge in realising the benefits of
this industrial

6 0

YouTube Video UExMYllTUHVicmxINDBtTDRKOUhrMTM2UHZzeTZ1WWdDby41QTY1Q0UxMTVCODczNThE

Robust Intelligent Malware Detection Using Deep Learning

TRU PROJECTS July 3, 2020 11:49 pm

TO PURCHASE THIS PROJECT IN ONLINE 
CONTACT : TRU PROJECTS 
WEBSITE : www.truprojects.in
MOBILE : 9676190678
MAIL ID : info@truprojects.in

ABSTRACT :

Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general. Developing models which can effectively model growth and yield can help growers improve the environmental control for better production, match supply and market demand and lower costs. Recent developments in Machine Learning (ML) and, in particular, Deep Learning (DL) can provide powerful new analytical tools. The proposed study utilises ML and DL techniques to predict yield and plant growth variation across two different scenarios, tomato yield forecasting and Ficus benjamina stem growth, in controlled greenhouse environments. We deploy a new deep recurrent neural network (RNN), using the Long Short-Term Memory (LSTM) neuron model, in the prediction formulations. Both the former yield, growth and stem diameter values, as well as the microclimate conditions, are used by the RNN architecture to model the targeted growth parameters. A comparative study is presented, using ML methods, such as support vector regression and random forest regression, utilising the mean square error criterion, in order to evaluate the performance achieved by the different methods. Very promising results, based on data that have been obtained from two greenhouses, in Belgium and the UK, in the framework of the EU Interreg SMARTGREEN project (2017-2021), are presented.

TO PURCHASE THIS PROJECT IN ONLINE
CONTACT : TRU PROJECTS
WEBSITE : www.truprojects.in
MOBILE : 9676190678
MAIL ID : info@truprojects.in

ABSTRACT :

Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general. Developing models which can effectively model growth and yield can help growers improve the environmental control for better production, match supply and market demand and lower costs. Recent developments in Machine Learning (ML) and, in particular, Deep Learning (DL) can provide powerful new analytical tools. The proposed study utilises ML and DL techniques to predict yield and plant growth variation across two different scenarios, tomato yield forecasting and Ficus benjamina stem growth, in controlled greenhouse environments. We deploy a new deep recurrent neural network (RNN), using the Long Short-Term Memory (LSTM) neuron model, in the prediction formulations. Both the former yield, growth and stem diameter values, as well as the microclimate conditions, are used by the RNN architecture to model the targeted growth parameters. A comparative study is presented, using ML methods, such as support vector regression and random forest regression, utilising the mean square error criterion, in order to evaluate the performance achieved by the different methods. Very promising results, based on data that have been obtained from two greenhouses, in Belgium and the UK, in the framework of the EU Interreg SMARTGREEN project (2017-2021), are presented.

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YouTube Video UExMYllTUHVicmxINDBtTDRKOUhrMTM2UHZzeTZ1WWdDby45RTgxNDRBMzUwRjQ0MDhC

Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments

TRU PROJECTS July 3, 2020 11:55 pm

TO PURCHASE THIS PROJECT IN ONLINE 
CONTACT : TRU PROJECTS 
WEBSITE : www.truprojects.in
MOBILE : 9676190678
MAIL ID : info@truprojects.in

ABSTRACT

In present educational systems, student performance prediction is getting worsen day by day. Predicting student performance in advance can help students and their teacher to keep track of progress of a student. Many institutes have adopted continuous evaluation system today. Such systems are beneficial to the students in improving performance of a student. The purpose of continuous evaluation system is to help regular students.
In recent years, Neural Networks have seen widespread and successful implementations in a wide range of data mining applications, often surpassing other classifiers. This study aims to investigate if Neural Networks are a fitting classifier to predict student performance from
Learning Management System data in the context of Educational Data Mining. To assess theapplicability of Neural Networks, we compare their predictive performance against six other classifiers on this dataset. These classifiers are Naive Bayes, k-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression and will be trained on data obtained during each course. The features used for training originate from LMS data obtained during the length of each course, and range from usage data like time spent on each course page, to grades obtained for course assignments and quizzes. After training, the Neural
Network outperforms all six classifiers in terms of accuracy and is on par with the best classifiers in terms of recall. We can conclude that Neural Networks outperform the six other algorithms tested on this dataset and could be successfully used to predict student performance.

TO PURCHASE THIS PROJECT IN ONLINE
CONTACT : TRU PROJECTS
WEBSITE : www.truprojects.in
MOBILE : 9676190678
MAIL ID : info@truprojects.in

ABSTRACT

In present educational systems, student performance prediction is getting worsen day by day. Predicting student performance in advance can help students and their teacher to keep track of progress of a student. Many institutes have adopted continuous evaluation system today. Such systems are beneficial to the students in improving performance of a student. The purpose of continuous evaluation system is to help regular students.
In recent years, Neural Networks have seen widespread and successful implementations in a wide range of data mining applications, often surpassing other classifiers. This study aims to investigate if Neural Networks are a fitting classifier to predict student performance from
Learning Management System data in the context of Educational Data Mining. To assess theapplicability of Neural Networks, we compare their predictive performance against six other classifiers on this dataset. These classifiers are Naive Bayes, k-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression and will be trained on data obtained during each course. The features used for training originate from LMS data obtained during the length of each course, and range from usage data like time spent on each course page, to grades obtained for course assignments and quizzes. After training, the Neural
Network outperforms all six classifiers in terms of accuracy and is on par with the best classifiers in terms of recall. We can conclude that Neural Networks outperform the six other algorithms tested on this dataset and could be successfully used to predict student performance.

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YouTube Video UExMYllTUHVicmxINDBtTDRKOUhrMTM2UHZzeTZ1WWdDby45NzUwQkI1M0UxNThBMkU0

Predicting Student Performance with Deep Neural Networks

TRU PROJECTS July 3, 2020 11:58 pm

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