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Facial recognition technology has become increasingly ubiquitous, being used for everything from unlocking smartphones to identifying individuals. This technology has made our lives easier and more efficient in many ways, but it still has limitations. One of the most significant challenges facial recognition systems face is their accuracy, particularly for underrepresented demographic groups. This issue is further complicated by biases in the datasets used to train these systems. Facial recognition that produces inaccurate results can lead to severe consequences, including wrongful arrests and false accusations. Such technology must be thoroughly tested and regulated to avoid harming innocent individuals. Researchers have explored various methods for improving facial recognition algorithms to address these challenges. One promising approach is to leverage human expertise to supplement the machine-learning process. This article reviews some of the most effective human-in-the-loop approaches to enhance facial recognition systems` fairness, interpretability, and performance. These methods involve incorporating human feedback at different stages of the machine-learning pipeline, such as active learning, clean labeling, and human-AI collaboration in model development and evaluation. The results of these studies have shown that incorporating human judgment and domain knowledge can significantly improve facial recognition systems` accuracy and fairness. For example, active learning methods can help mitigate dataset biases by prioritizing the most informative samples for human labeling. Clean labeling can help ensure the training data is accurate and unbiased, while human-AI collaboration can improve model interpretability and generalization. The significance of these findings is that thoughtfully integrating human expertise into the facial recognition process can lead to more ethical and robust systems. By involving human feedback, we can mitigate the biases and limitations of machine learning algorithms and ensure that these technologies work for everyone, regardless of race, gender, or other demographic factors. Ultimately, this will help us build a more just and equitable society.

facial recognition, human-in-the-loop, active learning, clean labeling, model interpretation, fairness

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Tharun Anand Reddy Sure, "Human-in-the-Loop Techniques: The Game-Changer in Facial Recognition," International Journal of Computer Sciences and Engineering, Vol.11, Issue.11, pp.1-4, 2023.

The introduction of t The fifth generation (5G) networks indeed brings significant advancements in connectivity and has the potential to revolutionize various industries. The technologies that make 5G powerful include features such as faster speeds, reduced latency, increased capacity, and the ability to connect a wide range of devices and objects. However, implementing 5G networks involves upgrading existing infrastructure and deploying new infrastructure, which can be both costly and time-consuming. This process requires significant investments from telecommunication companies to install new equipment and upgrade existing infrastructure to support 5G technology. Additionally, the deployment of 5G networks requires a substantial amount of radio spectrum, and regulatory frameworks need to be in place to allocate and manage the spectrum effectively. This paper provides an overview of 5G technologies, highlighting their key features and potential benefits. It also delves into the existing challenges that arise with the implementation of 5G networks and discusses some possible solutions to address these challenges.

5G, Non-Standalone, Standalone, Radio Access Technology, Mobile Broadband, Infrastructure Requirements.

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M. Alnaas, E. Laias, A. Hanasih, O. Alhodairy, "Upgrading to 5G Networks: Existing Challenges and Potential Solutions," International Journal of Computer Sciences and Engineering, Vol.11, Issue.11, pp.5-12, 2023.

Machine learning is field of Artificial Intelligence that focuses on the development of algorithms and statistical methods. Fake news has caused a lot of issues for our society. Many researchers are trying to determine what fake news is. It is challenging to recognize ambiguous fake news, can only be found after determining meaning and recent pertinent facts. For news, everyone uses a variety of online sources. News quickly disseminated among millions of users in a very short period of time to the increase in the use of social media platforms like Facebook, Twitter, etc. We will enable the user to categorize news as either genuine or real. The logistic regression approach will be used to identify false news. Natural Language processing techniques like Term Frequency Inverse Document Frequency (TF-IDF), text processing etc. In our experiment, we`ll demonstrate how our method boosts bogus news` overall performance. We are providing URL search whether the given URL is fake or not.

Fake news, Natural Language processing, Logistic Regression, Machine Learning, Term Frequency Inverse Document Frequency, Text Processing

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[4]. Xinyi zhou, Reza zafarani, "Network-based Fake news Detection:A Pattern driven Approach", ACM journals, Vol.21, Issue.2, pp.48-60, 2019.
[5]. Z Khanam, B.N. Alwasel, Sirafi, M Rashid, "Fake news detetction using machine learning approaches", IOP science, Vol.1099, 2020. DOI:10.1088/1757-889X/1099/1/012040
[6]. Iftikhar Ahmad, Muhammad Yousaf, Suhail Yousaf, Muhammad Ovais Ahmad," Fake news Detection using machine learning ensemble methods, Hindawi, 2020.
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K. Ramya, M. Yamini, K. Prajwala, M. Jyothirmai, "Fake News Detection Using Machine Learning Algorithm Logistic Regression," International Journal of Computer Sciences and Engineering, Vol.11, Issue.11, pp.13-16, 2023.

This article tracks and monitors vehicle speeds by utilizing computer vision techniques and libraries such as OpenCV and dlib. Using video inputs, it effectively recognizes vehicles, records their motions, and determines their speeds by calculating the time interval between frames and the distances they travel. In a CSV file, the gathered speed data is methodically documented and contains relevant details including timestamps, vehicle identities, and observed speeds in kilometers per hour. Another important element of the system is that it enforces a predetermined speed limit and sends out alarm messages to vehicles that go over it. This allows for real-time visual notifications that show the speed value and timestamp. Such a versatile system offers vital support in traffic management, traffic data analysis, and road safety enforcement, among its many possible use cases.

OpenCV, Dlib, Data logging, Object detection, Vehicle speed monitoring, Vehicle Speed Estimation.

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Yenuganti Siva Lakshmi, Prathigudupu Navya Sri, Siddi Mounika, Tata Usha Sri, Yallamati Suresh, "Vehicle Speed Estimation Using OpenCV Framework," International Journal of Computer Sciences and Engineering, Vol.11, Issue.11, pp.17-21, 2023.

From and practical fault attacks have been published that pose a serious threat to most of the crypto-algorithms the time of announcement of new cryptanalytic attack called fault attack proposed by Bellcore in Sep 1996, multiple theoretical. Fault attacks circumvent the intricate mathematics of ciphers and swiftly extract the cipher`s secret key. This is achieved by disrupting the system`s normal behaviour, inducing faults that result in its faulty operation. The efficiency of these attacks has been improved over a period of time in making them more practical. Because of this, multiple techniques to counter the attacks are also published, that increase the complexity of attacks with the goal of making them impossible in the future. This paper covers the various fault attacks & countermeasures on symmetric, asymmetric and Post-Quantum crypto (PQC) algorithms along with various ways of resistance evaluation &their rating.

SCA, Fault attacks, Symmetric, Asymmetric, PQC, Evaluation

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