333 research outputs found
A TIME-BASED APPROACH FOR SOLVING THE DYNAMIC PATH PROBLEM IN VANETS – AN EXTENSION OF ANT COLONY OPTIMIZATION
Get PDFOver a decade, Vehicular Adhoc Networks (VANETS) has been evolved and the field of vehicular communication has become a promising area for potential research. The challenges vary from a vehicle to vehicle communication, an indication during the event of a collision, and to enhance the drive and passenger safety. This paper aims at improving the performance of VANETs in terms of capacity, size, topological changes and maintaining the shortest routes. A new scheme termed as Ant Queue Optimization Scheme (AQO) has been introduced by extending the traditional Ant Colony Optimization (ACO). The proposed Ant Queue optimization Scheme combines both proactive and reactive mechanisms. Unlike the ACO, the AQO dynamically makes decision in choosing shortest best route in highly congested areas. Route selection is
dynamic at each intersection irrespective of the size of the traffic. Encouraging results have been achieved in using the Ant Queue Optimization even at high vehicular density scenarios
Predicting and curing depression using long short term memory and global vector
Get PDFIn today’s world, there are many people suffering from mental
health problems such as depression and anxiety. If these conditions are not
identified and treated early, they can get worse quickly and have far-reaching
negative effects. Unfortunately, many people suffering from these conditions,
especially depression and hypertension, are unaware of their existence until the
conditions become chronic. Thus, this paper proposes a novel approach using
Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm and Global
Vector (GloVe) algorithm for the prediction and treatment of these conditions.
Smartwatches and fitness bands can be equipped with these algorithms which
can share data with a variety of IoT devices and smart systems to better
understand and analyze the user’s condition. We compared the accuracy and
loss of the training dataset and the validation dataset of the two models
namely, Bi-LSTM without a global vector layer and with a global vector layer.
It was observed that the model of Bi-LSTM without a global vector layer had
an accuracy of 83%, while Bi-LSTM with a global vector layer had an accuracy
of 86% with a precision of 86.4%, and an F1 score of 0.861. In addition to
providing basic therapies for the treatment of identified cases, our model also
helps prevent the deterioration of associated conditions, making our method
a real-world solution
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