Technofist provides latest IEEE final year projects for electronics and communication engineering students in ARTIFICIAL INTELLIGENCE, Technofist is one of the best final year project institute for electronics and communication engineering students for implementing MATLAB image processing project.
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TMO01
DSNET JOINT SEMANTIC LEARNING FOR OBJECT DETECTION IN INCLEMENT
WEATHER CONDITIONS
ABSTRACT - The main purpose of object detection is to know and work for one or
more effective targets from still image or video data. Object detection is a key
ability required by most computer and robot vision systems. The very recent
research and works on this topic has been making great progress in many
directions and different ways. In the current manuscript, we give an overview of
past research on object detection depending on the weather conditions, outline
the current main research strategies, and discuss open problems and possible
future directions and views. In this paper, we address the object detection
problem in the presence of fog by introducing a novel dual-subnet network
(DSNet) that can also be trained and learnt three things: visibility improvement,
object differentiation, and object localization.
Contact: +91-9008001602 080-40969981
TMO02
APPLE DISEASE CLASSIFICATION BUILT ON DEEP LEARNING
ABSTRACT - Diseases and pests cause huge economic loss to the apple industry
every year. The identification of various apple diseases is challenging for the
farmers as the symptoms produced by different diseases may be very similar, and
may be present simultaneously. This paper is an attempt to provide the timely
and accurate detection and identification of apple diseases. In this study, we
propose a deep learning based approach for identification and classification of
apple diseases. The first part of the study is dataset creation which includes data
collection and data labelling.
Contact: +91-9008001602 080-40969981
TMO03
AN IDENTIFICATION METHOD OF APPLE LEAF DISEASE BASED ON TRANSFER LEARNING
ABSTRACT - Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four
common types of apple leaf diseases. Early diagnosis and accurate identification
of apple leaf diseases can control the spread of infection and ensure the healthy
development of the apple industry. The existing research uses complex image
preprocessing and cannot guarantee high recognition rates for apple leaf
diseases. This paper proposes an accurate identifying approach for apple leaf
diseases based on deep convolutional neural networks. It includes generating
sufficient pathological images and designing a novel architecture of a deep
convolutional neural network based on AlexNet to detect apple leaf diseases
Contact: +91-9008001602 080-40969981
TMO04
ANALYSIS OF ARRHYTHMIA CLASSIFICATION ON ECG DATASET
ABSTRACT - In this paper, Recurrent Neural Networks (RNN) have been applied
for classifying the normal and abnormal beats in an ECG. The primary aim of this
paper was to enable automatic separation of regular and irregular beats. The MITBIH Arrhythmia database is being used to classify the beat classification
performance. The methodology used is carried out using huge volume of standard
data i.e. ECG time-series data as inputs to Long Short Term Memory Network . We
divided the dataset as training and testing sub-data. The effectiveness, accuracy
and capabilities of our methodology ECG arrhythmia detection is demonstrated
and quantitative comparisons with different RNN models have also been carried
out.
pretty much since the opening of first supermarket. Contact: +91-9008001602 080-40969981
TMO05
A NEW APPROACH TO DETECT ANOMALOUS BEHAVIOUR IN ATMS
ABSTRACT - An automated teller machine is an electronics telecommunications
device which is utilized by people, mostly to withdraw money. In the present
scenario, a fair amount of the population using an ATM machine to withdraw cash
are facing a problem of robberies and theft due to lack of security guards.
Surveillance cameras being used in the ATM cells, however monitoring
capabilities of law enforcement agencies has not kept pace. So, in this system
anomalous behavior is detected using CNN and LSTM on the surveillance videos.
Accurate recognition of anomalous behavior at a point in time is the most
challenging problem for systems. The anomaly as well as non-anomaly dataset is
fed to a machine and trained to identify abnormal behavior.
Contact: +91-9008001602 080-40969981
TMO06
COMPARATIVE ANALYSIS OF BANANA LEAF DISEASE DETECTION AND
CLASSIFICATION METHODS
ABSTRACT - The feature extraction technique plays a very critical and crucial role
in automatic leaf disease diagnosis system. Many different feature extraction
techniques are used by the researchers for leaf disease diagnosis which includes
colour, shape, texture, HOG, SURF and SIFT features. Recently Deep Learning is
giving very promising results in the field of computer vision. In this manuscript,
two feature extraction techniques are discussed and compared. In first approach,
the Gray Level Covariance Matrix (GLCM) is used which extracts 12 texture
features for diagnosis purpose. In second approach, the pretrained deep learning
model, Alexnet is used for feature extraction purpose. There are 1000 features
extracted automatically with the help of this pretrained model. Contact: +91-9008001602 080-40969981
TMO08
A SMART APPROACH FOR HEALTH MONITORING SYSTEM USING ARTIFICIAL
INTELLIGENCE
ABSTRACT - The Internet of Things (IoT) has enabled the invention of smart
health monitoring systems. These health monitoring systems can track a person’s
mental and physical wellness. Stress, anxiety, and hypertension are key causes of
many physical and mental disorders. Age-related problems such as stress, anxiety,
and hypertension necessitate specific attention in this setting. Stress, anxiety, and
blood pressure monitoring can prevent long-term damage by detecting problems
early. This will increase the quality of life and reduce caregiver stress and
healthcare costs. Determine fresh technology solutions for real-time stress,
anxiety, and blood pressure monitoring using discreet wearable sensors and
machine learning approaches. This study created an automated artefact detection
method for BP and PPG signalsContact: +91-9008001602 080-40969981
TMO09
ANALYSIS OF DEEP LEARNING METHODS FOR DETECTION OF BIRD SPECIES
ABSTRACT - Now a day some bird species are being found rarely and if found
classification of bird species prediction is difficult. Naturally, birds present in
various scenarios appear in different sizes, shapes, colors, and angles from human
perspective. Besides, the images present strong variations to identify the bird. species more than audio classification. Also, human ability to recognize the birds
through the images is more understandable. So this method uses the CaltechUCSD Birds 200 [CUB-200-2011] dataset for training as well as testing purpose. By
using deep convolutional neural network (DCNN) algorithm an image converted
into grey scale format to generate autograph by using tensor flow, where the
multiple nodes of comparison are generated. Contact: +91-9008001602 080-40969981
TMO10
ANALYTICAL STUDY FOR PRICE PREDICTION OF BITCOIN USING MACHINE
LEARNING AND ARTIFICIAL INTELLIGENCE
ABSTRACT - Bitcoin, a type of cryptocurrency is currently a thriving open-source
community and payment network, which is currently used by millions of people.
As the value of Bitcoin varies everyday, it would be very interesting for investors
to forecast the Bitcoin value but at the same time making it difficult to predict.
Bitcoin is a cryptocurrency technology that has attracted investors because of its
big price increases. This has led to researchers applying various methods to
predict Bitcoin prices such as Support Vector Machines, Multilayer Perceptron,
RNN etc. To obtain accuracy and efficiency as compared to these algorithms this
research paper tends to exhibit the use of RNN using LSTM model to predict the
price of crypto currency. The results were computed by extrapolating graphs
along with the Root Mean Square Error of the model which was found to be 3.38. Contact: +91-9008001602 080-40969981
TMO11
COMPARATIVE ANALYSIS ON U-NET-BASED RETINAL BLOOD VESSEL
SEGMENTATION
ABSTRACT - In this work we compare the performance of a number of vessel
segmentation algorithms on a newly constructed retinal vessel image database.
Retinal vessel segmentation is important for the detection of numerous eye
diseases and plays an important role in automatic retinal disease screening
systems. A large number of methods for retinal vessel segmentation have been
published, yet an evaluation of these methods on a common database of
screening images has not been performed. To compare the performance of
retinal vessel segmentation methods we have constructed a large database of
retinal images. The database contains forty images in which the vessel trees have
been manually segmented. Contact: +91-9008001602 080-40969981
TMO12
INTERFACE USING STATISTICAL MEASURES AND MACHINE LEARNING FOR
GRAPH REDUCTION TO SOLVE MAXIMUM WEIGHT CLIQUE PROBLEMS
ABSTRACT - : In this paper, we investigate problem reduction techniques using
stochastic sampling and machine learning to tackle large-scale optimization
problems. These techniques heuristically remove decision variables from the
problem instance, that are not expected to be part of an optimal solution. First
we investigate the use of statistical measures computed from stochastic sampling
of feasible solutions compared with features computed directly from the instance
data. Two measures are particularly useful for this: 1) a ranking-based measure,
favoring decision variables that frequently appear in high-quality solutions; and 2)
a correlation-based measure, favoring decision variables that are highly
correlated with the objective values. To take this further we develop a machine
learning approach, called Machine Learning for Problem Reduction (MLPR), that
trains a supervised learning model on easy problem instances for which the
optimal solution is known.
Contact: +91-9008001602 080-40969981
TMO13
ADVERSARIAL ATTACKS ON TIME SERIES
ABSTRACT - Time series classification models have been garnering significant
importance in the research community. However, not much research has been
done on generating adversarial samples for these models. These adversarial
samples can become a security concern. In this paper, we propose utilizing an
adversarial transformation network (ATN) on a distilled model to attack various
time series classification models. The proposed attack on the classification model
utilizes a distilled model as a surrogate that mimics the behavior of the attacked
classical time series classification models. Our proposed methodology is applied
onto 1-Nearest Neighbor Dynamic Time Warping (1-NN DTW) and a Fully
Convolutional Network (FCN), all of which are trained on 42 University of
California Riverside (UCR) datasets. In this paper, we show both models were
susceptible to attacks on all 42 datasets. Contact: +91-9008001602 080-40969981