Smart Shoes Safety System for the Blind People Based on (IoT) Technology

People’s lives have become easier and simpler as technology has proliferated.This is especially true with the Internet of Things(IoT).The biggest problem for blind people is figuring out how to get where they want to go.People with good eyesight need to help these people.Smart shoes are a technique that helps blind people find their way when they walk.So,a special shoe has been made to help blind people walk safely without worrying about running into other people or solid objects.In this research,we are making a new safety system and a smart shoe for blind people.The system is based on Internet of Things(IoT)technology and uses three ultrasonic sensors to allow users to hear and react to barriers.It has ultrasonic sensors and a microprocessor that can tell how far away something is and if there are any obstacles.Water and flame sensors were used,and a sound was used to let the person know if an obstacle was near him.The sensors use Global Positioning System(GPS)technology to detect motion from almost every side to keep an eye on them and ensure they are safe.To test our proposal,we gave a questionnaire to 100 people.The questionnaire has eleven questions,and 99.1%of the people who filled it out said that the product meets their needs.

Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our lives.Cyberbullying is a social media network issue,a global crisis affecting the victims and society as a whole.It results from a misunderstanding regarding freedom of speech.In this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet allocation.In addition,we used accuracy,precision,recall,and F1 score to assess prior classifiers.Results show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.

Age and Gender Classification Using Backpropagation and Bagging Algorithms

Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and classify gender,age,and accent.So,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification data.Bagging has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.

A Robust Model for Translating Arabic Sign Language into Spoken Arabic Using Deep Learning

This study presents a novel and innovative approach to auto-matically translating Arabic Sign Language(ATSL)into spoken Arabic.The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models.The image-based translation method maps sign language gestures to corre-sponding letters or words using distance measures and classification as a machine learning technique.The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs,with a translation accuracy of 93.7%.This research makes a significant contribution to the field of ATSL.It offers a practical solution for improving communication for individuals with special needs,such as the deaf and mute community.This work demonstrates the potential of deep learning techniques in translating sign language into natural language and highlights the importance of ATSL in facilitating communication for individuals with disabilities.

Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network breaches.Intrusions,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion traffic.The vast network traffic volume also complicates most network monitoring systems and algorithms.Several intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these incidents.This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning algorithms.The stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression algorithm.Evaluated using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,respectively.The obtained results demonstrate improvements in other measurement criteria.

An Automated Approach to Generate Test Cases From Use Case Description Model

Test complexity and test adequacy are frequently raised by software developers and testing agents.However,there is little research works at this aspect on specification-based testing at the use case description level.Thus,this research proposes an automatic test cases generator approach to reduce the test complexity and to enhance the percentage of test coverage.First,to support the infrastructure for performing automatic,this proposed approach refines the use cases using use case describing template and save it in the text file.Then,the saved file is input to the Algorithm of Control Flow Diagram(ACFD)to convert use case details to a control flow diagram.After that,the Proposed Tool of Generating Test Paths(PTGTP)is used to generate test cases from the control flow diagram.Finally,the genetic algorithm associated with transition coverage is adapted to optimize and evaluate the adequacy of such test cases.A money withdrawal use case in the ATM system is used as an ongoing case study.Preliminary results show that the generated test cases achieve high coverage with an optimal test case.This automatic test case generation approach is effective and efficient.Therefore,it could promote to use other test case coverage criteria.

Hadoop Based Defense Solution to Handle Distributed Denial of Service (DDoS) Attacks

Distributed denial of service (DDoS) attacks continues to grow as a threat to organizations worldwide. From the first known attack in 1999 to the highly publicized Operation Ababil, the DDoS attacks have a history of flooding the victim network with an enormous number of packets, hence exhausting the resources and preventing the legitimate users to access them. After having standard DDoS defense mechanism, still attackers are able to launch an attack. These inadequate defense mechanisms need to be improved and integrated with other solutions. The purpose of this paper is to study the characteristics of DDoS attacks, various models involved in attacks and to provide a timeline of defense mechanism with their improvements to combat DDoS attacks. In addition to this, a novel scheme is proposed to detect DDoS attack efficiently by using MapReduce programming model.

A Novel Solution to Handle DDOS Attack in MANET

Distributed Denial of Service (DDoS) attacks in the networks needs to be prevented or handled if it occurs, as early as possible and before reaching the victim. Dealing with DDoS attacks is difficult due to their properties such as dynamic attack rates, various kinds of targets, big scale of botnet, etc. Distributed Denial of Service (DDoS) attack is hard to deal with because it is difficult to distinguish legitimate traffic from malicious traffic, especially when the traffic is coming at a different rate from distributed sources. DDoS attack becomes more difficult to handle if it occurs in wireless network because of the properties of ad hoc network such as dynamic topologies, low battery life, multicast routing, frequency of updates or network overhead, scalability, mobile agent based routing, and power aware routing, etc. Therefore, it is better to prevent the distributed denial of service attack rather than allowing it to occur and then taking the necessary steps to handle it. This paper discusses various the attack mechanisms and problems due to DDoS attack, also how MANET can be affected by these attacks. In addition to this, a novel solution is proposed to handle DDoS attacks in mobile ad hoc networks (MANETs).