New adaptation method based on cross layer and TCP over protocols to improve QoS in mobile ad hoc network

Due to rapid growth of multimedia traffic used over the mobile ad-hoc networks (MANETs), to keep up with the progress of this constraints MANETs protocols becoming increasingly concerned with the quality of service. In view of the random mobility nodes in MANET, TCP becomes more unreliability in case of higher energy consumption and packet loss. In this paper we proposed a new optimization approach to enhance decision making of TCP based on some changes of IEEE 802.11 MAC uses cross layer approach. The aim is to minimize the impact of retransmissions of packet lost and energy consumption in order to analysed and chose the appropriate routing protocol for TCP that can be enhance QoS MANET. Our simulation results based QoS study using NS3 show that, our proposed achieves better performance of TCP in MANETs significantly, and also improved the throughput, energy consumption and facilitates the traffic transmission over routing protocol

Predicting user behavior using data profiling and hidden Markov model

Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data

Matching data detection for the integration system

The purpose of data integration is to integrate the multiple sources of heterogeneous data available on the internet, such as text, image, and video. After this stage, the data becomes large. Therefore, it is necessary to analyze the data that can be used for the efficient execution of the query. However, we have problems with solving entities, so it is necessary to use different techniques to analyze and verify the data quality in order to obtain good data management. Then, when we have a single database, we call this mechanism deduplication. To solve the problems above, we propose in this article a method to calculate the similarity between the potential duplicate data. This solution is based on graphics technology to narrow the search field for similar features. Then, a composite mechanism is used to locate the most similar records in our database to improve the quality of the data to make good decisions from heterogeneous sources

Slum image detection and localization using transfer learning: a case study in Northern Morocco

Developing countries are faced with social and economic challenges, including the emergence and proliferation of slums. Slum detection and localization methods typically rely on regular topographic surveys or on visual identification of high-resolution spatial satellite images, as well as socio-environmental surveys from land surveys and general population censuses. Yet, they consume so much time and effort. To overcome these problems, this paper exploits well-known seven pretrained models using transfer learning approaches such as MobileNets, InceptionV3, NASNetMobile, Xception, VGG16, EfficientNet, and ResNet50, consecutively, on a smaller dataset of medium-resolution satellite imagery. The accuracies obtained from these experiments, respectively, demonstrate that the top three pretrained models achieve 98.78%, 97.9%, and 97.56%. Besides, MobileNets have the smallest memory sizes of 9.1 Mo and the shortest latency of 17.01 s, which can be implemented as needed. The results show the good performance of the top three pretrained models to be used for detecting and localizing slum housing in northern Morocco

Building an efficient convolution neural network from scratch: A case study on detecting and localizing slums

Designing a convolution neural network from scratch is one of the biggest challenges facing the creation of reproducible models. Despite feeding the model with an adequate amount of labeled data to mitigate fluctuations during training, the model still suffers from high variance in the final overall accuracy and loss among identical training runs. Many of the reasons behind this are the randomness in data shuffling and augmentation, the behavior of gradient decent function, and the non-determinism in convnet layers and floating-point computation of the GPU.The method used to address the aforementioned issues, specifically in the case of negative transfer learning, is divided into three steps: First, designing an efficient lightweight convnet architecture with respect to available resources. Second, mitigating oscillations during training. Third, after setting the random seed across the training, select the appropriate weight initialization. Our extensive experiments in the use case of binary slum localization and detection show that our method improves the reproducibility of our model from scratch with an accuracy of 98.88±1.15%, a loss of 0.03±0.05 for a confidence level of 99.73%. These results make this model a strong competitor to the pre-trained models using transfer learning

Improved TCP Prediction Congestion in Mobile Ad Hoc Network Based on Cross-Layer and Fuzzy Logic

Mobile ad hoc network (MANET) is among the networks which do not require any infrastructure to put nodes in communication. Due to its own nature, it is used by several applications. Even though it's a network that is extremely challenging and mostly when TCP is applied. In this paper, we have proposed a new improvement in the TCP algorithm that employed fuzzy logic to predict packet loss and avoid congestion. Specifically, we have used tree metrics such as stability, energy, and signal strength to use in fuzzy logic systems. To accomplish our approach, we have established some modifications based on a cross-layer. The results of the relevant simulation performed by NS3 demonstrated that our approach globally improves the performance of TCP in MANET. Precisely reduce the packet overhead and energy consumption also enhance throughput

Optimal Task Processing and Energy Consumption Using Intelligent Offloading in Mobile Edge Computing

The appearance of Edge Computing with the possibility to bring powerful computation servers near the mobile device is a major stepping stone towards better user experience and resource consumption optimization. Due to the Internet of Things invasion that led to the constant demand for communication and computation resources, many issues were imposed in order to deliver a seamless service within an optimized cost of time and energy, since most of the applications nowadays require real response time and rely on a limited battery resource. Therefore, Mobile Edge Computing is the new reliable paradigm in terms of communication and computation consumption by the mobile devices. Mobile Edge Computing rely on computation offloading to surpass cloud-based technologies issues and break the limitations of mobile devices such as computing, storage and battery resources. However, computation offloading is not always the optimal choice to adopt, which makes the offloading decision a crucial part in which many parameters should be taken in consideration such as delegating the heavy tasks to the appropriate machine within the network by migrating the high-resource node to an edge server and lend these capabilities to the low-resources one. In this paper, we use an Edge Computing simulator to see how network delay can impact the delivery of a certain result, we also experiment computation offloading using a two-tier with Edge Orchestration architecture, which turns out to be efficient in terms of processing time

Prediction of Depression via Supervised Learning Models: Performance Comparison and Analysis

This document Among all the various types of mental and psychosocial illnesses, the most commonly occurring type is depression. It can cause serious problems such as suicide. Therefore, early detection is important to stop the progression of this disease that could endanger human lives. Predicting and detecting early-stage depression using machine learning (ML) techniques is a promising strategy. This study’s main purpose is to assess which ML techniques are highly appropriate and accurate regarding such diagnoses. Six supervised ML techniques namely: K-nearest neighbor (KNN), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Support vector machine (SVM) and Naive Bayes (NB) were applied on dataset collected from Kaggle and compared for their accuracy (ACC) and performance in predicting depression. The performance of each model was evaluated using 10-fold cross-validation and evaluated in terms of ACC, F1-score, Precision (PR), and Sensitivity (SEN). Based on the experimental results analysis, we can conclude that SVM and LR performed better than all other methods with an ACC of 83,32%. Therefore, we found that a simple ML algorithm can be used to assist clinicians and practitioners predict depression at an early stage, with excellent potential utility and a considerable degree of ACC