Search

Firmware attacks on IoT devices have become a growing concern in recent years. These attacks exploit vulnerabilities in the firmware, which is the low-level software that controls the hardware of the device, to gain access to sensitive data or control the device remotely. Firmware attacks are particularly dangerous because they can be difficult to detect and can allow attackers to maintain access to a device for an extended period of time. In this paper, we explore the silent threat of firmware attacks on connected devices and the potential consequences of a successful attack. We discuss the methods that attackers use to exploit firmware vulnerabilities and the impact these attacks can have on the security and privacy of users. We also provide recommendations for protecting against firmware attacks, including keeping firmware up to date, using strong passwords, and monitoring for suspicious activity on the network. By understanding the risks and taking proactive steps to protect against firmware attacks, users can help to ensure the security and integrity of their connected devices.

Data structures are the foundation of computing, providing efficient ways to store and manipulate data. They are essential for designing and implementing algorithms that can handle large amounts of information quickly and accurately. In this paper, we explore the world of data structures, examining their basic principles, properties, and uses. We start by introducing the most common data structures, such as arrays, linked lists, stacks, queues, trees, and graphs, and discuss their advantages and limitations.

As the need for machine learning models continues to grow, concerns about data privacy and security become increasingly important. Federated learning, a decentralized machine learning approach, has emerged as a promising solution that allows multiple parties to collaborate and build models without sharing sensitive data. In this comprehensive survey, we explore the principles, techniques, and applications of federated learning, with a focus on its privacy-preserving aspects

The Internet of Things (IoT) is rapidly growing and becoming an integral part of our daily lives. However, the increasing use of IoT devices also raises significant security concerns. One of the most pressing threats to IoT security is the blackhole attack, where an attacker can selectively drop or discard packets to disrupt communication between IoT devices. In this paper, we conduct a comprehensive survey on blackhole attacks in IoT networks. We explore the types of blackhole attacks, the methods attackers use to exploit vulnerabilities in IoT devices, and the potential impact of these attacks. We also review existing solutions and strategies for mitigating the effects of blackhole attacks in IoT networks. Through our survey, we provide a deeper understanding of the blackhole attack's nature and the potential implications for the security and reliability of IoT networks. Ultimately, our findings highlight the need for increased awareness of this type of attack and the implementation of robust security measures to protect IoT devices and networks.

With the increasing volume of data being generated every day, the need for data security has become more crucial than ever. Encryption is one of the most effective techniques for protecting sensitive data from unauthorized access or theft. This comparative survey aims to provide a comprehensive analysis of different data encryption methods in the context of big data. The survey covers both traditional encryption techniques and newer, more advanced methods such as homomorphic encryption and quantum cryptography. The effectiveness, advantages, and limitations of each technique are examined, and a comparison of their performance in terms of speed, scalability, and security is presented. The study also explores the challenges and issues associated with implementing data encryption in big data environments. The findings of this survey will be useful for organizations seeking to secure their big data assets and for researchers interested in the latest developments in data encryption techniques.

The global economy is no stranger to recessions, which can have severe impacts on businesses, individuals, and entire nations. As a result, it is essential to analyze economic data to identify and understand recession patterns, allowing for effective policy-making and planning. In this paper, we explore the use of Python as a tool for recession analysis. We discuss various techniques for data collection, processing, and analysis using popular Python libraries such as Pandas, NumPy, and Matplotlib

Datafication has emerged as a key driver of the digital economy, enabling businesses, governments, and individuals to extract value from the growing flood of data. In this comprehensive survey, we explore the various dimensions of datafication, including the technologies, practices, and challenges involved in turning information into structured data for analysis and decision-making. We begin by providing an overview of the historical context and the rise of big data, and then delve into the latest developments in artificial intelligence and machine learning. We examine the key drivers of datafication across industries and sectors, and explore the ethical, legal, and social implications of the data revolution. Finally, we consider the challenges and opportunities presented by datafication, including issues of data privacy and security, the need for new skills and competencies, and the potential for data to drive innovation and social change. Overall, this survey provides a comprehensive and up-to-date overview of the datafication landscape, helping readers to better understand and navigate the rapidly-evolving world of data.

Matlab has become a popular choice for researchers across various fields due to its versatility, ease of use, and powerful analytical capabilities. In this paper, we explore the role of Matlab as the ultimate solution for research challenges. We first discuss the benefits of using Matlab in research, including its ability to handle complex mathematical computations, data visualization, and simulation of complex systems.

The Internet of Things (IoT) has brought about significant changes to various domains such as healthcare, transportation, and manufacturing. However, security remains a critical challenge in IoT due to the large number of connected devices with varying processing capabilities and memory constraints. Traditional cryptographic algorithms are not well-suited for IoT devices due to their high computational and memory requirements. Lightweight cryptography algorithms have emerged as a promising solution for securing IoT devices with limited resources. In this abstract, we provide an overview of lightweight cryptography algorithms for IoT, including their design principles, security properties, and performance evaluation.

The convergence of the Internet of Things (IoT) and blockchain technology is revolutionizing the field of security. IoT devices are becoming increasingly prevalent in our daily lives, from smart homes to wearable technology, but they also pose a significant risk to cybersecurity. Blockchain technology offers a decentralized, tamper-proof network that can enhance the security of IoT devices. By creating secure identities, communication channels, and transactions, blockchain can protect IoT devices from potential cyber-attacks. This paper explores the advantages of using blockchain to enhance IoT security and highlights the potential of this technology to create a safer and more secure IoT ecosystem

As cities around the world are growing at an unprecedented rate, there is a pressing need for smarter, more sustainable, and efficient urban infrastructure. In recent years, blockchain technology has emerged as a potential solution for various challenges faced by smart cities. Blockchain's unique features such as decentralization, immutability, and transparency offer new opportunities for secure and efficient management of city systems. This paper explores the potential of blockchain technology in smart city development, highlighting its applications in areas such as energy management, transportation, governance, and public services. By analyzing various use cases and examples, the paper demonstrates how blockchain can enhance the reliability, efficiency, and transparency of smart city infrastructure. Additionally, the paper discusses the challenges and limitations of blockchain adoption in smart cities and suggests potential solutions for overcoming these obstacles. Overall, this paper argues that blockchain technology has the potential to transform the future of smart city development, enabling cities to become more resilient, sustainable, and citizen-centric

The outbreak of COVID-19 has caused a global health crisis and has severely impacted the economy and daily life of people. Predicting the spread of COVID-19 is of utmost importance to effectively control the spread of the disease. In this study, we propose a COVID-19 prediction model using Support Vector Machine (SVM) and Linear Regression algorithms. We collected data on the number of confirmed cases, recovered cases, and deaths caused by COVID-19 from January 2020 to March 2023. We divided the data into training and testing datasets and applied feature engineering techniques to extract relevant features. We then trained our model using SVM and Linear Regression algorithms on the training dataset. The results of our experiments show that the SVM model achieved little bit less accuracy than the Linear Regression model in predicting the number of confirmed cases, recovered cases, and deaths. Our model can provide accurate predictions and insights into the future trends of COVID-19 cases.

Reinforcement learning is a subfield of machine learning that deals with developing algorithms that enable an agent to learn from experience through trial-and-error interactions with its environment. It is a paradigm of learning by receiving rewards or punishments based on its actions and adjusting its behavior to maximize its cumulative reward over time. Reinforcement learning has been successfully applied in a wide range of fields, including robotics, game playing, recommendation systems, and finance. It has also shown promising results in solving complex problems that are difficult to solve using traditional methods. Despite these challenges, reinforcement learning has already proven to be a powerful tool for developing intelligent systems that can learn and adapt to changing environments, and it is likely to play an increasingly important role in the development of future AI technologies.

Information technology today is increasingly concerned with dealing with massive data sets. The proliferation of the internet and, by extension, the digital economy has resulted in a meteoric rise in the need for data storage and analysis. This creates a serious problem for American IT departments in terms of securing and analysing the resulting avalanche of data. Businesses currently acquire and store more data than ever before due to the critical role that information plays in their daily operations. In all likelihood, this pattern will maintain its current trajectory. The organised knowledge that is being developed right now is based on a lot of legacy information. Instead, it's information like text, images, music, video, and social media posts. It's called "unstructured knowledge" when the knowledge isn't in any particular shape. The term "big data analytics" refers to a technique that can be used to get insight from these massive datasets. In addition to generating new business prospects, this strategy has been shown to increase the percentage of returning customers.

Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) are two innovative technologies that have emerged in recent years to address the limitations of traditional network architectures. NFV enables network functions to be implemented on standard hardware, rather than on dedicated proprietary devices, while SDN separates the control plane from the data plane, enabling centralized control and management of network traffic. Together, NFV and SDN provide a new era of network agility and flexibility, enabling network operators to dynamically provision and scale network services to meet changing demands, improve network efficiency and reduce costs.

Software-defined networking (SDN) has revolutionized the way networks are designed and managed, with the control plane being a critical component. The SDN control plane is responsible for managing and orchestrating network traffic flows, but its traditional static approach can be limiting in dynamic and complex network environments. This abstract explores the concept of a dynamic SDN control plane, which can adapt and respond to changing network conditions, leading to better network performance and efficiency. The dynamic control plane involves the use of machine learning algorithms, artificial intelligence, and network analytics to constantly monitor and adjust network policies in real-time. The dynamic SDN control plane empowers networks to move from a state of control to chaos, enabling greater flexibility, agility, and resilience in network management. This abstract provides insights into the potential benefits of a dynamic SDN control plane and highlights the challenges and opportunities in implementing this approach.

Software-defined networking (SDN) is becoming more popular because it makes the data layer of a network easier to change and adapt. That's the driving force behind its development and maturation. The purpose of this research was to look into the history of SDN data planes and highlight the key innovations and advances that have led to their current state. We will discuss the evolution of data planes from hardware to software, the development of programmable data planes, and the positive effects of software-defined networking data planes on network performance, control, and management. We will also look into the challenges of deploying SDN data planes and provide advice for companies on how to make the most of this transition. Finally, this article's goal is to provide a comprehensive analysis of the data planes in software-defined networks (SDN) and their effects on the networking sector.

The software-defined networking (SDN) paradigm has revolutionized the way we think about network management and control. The SDN application plane is a critical component of this paradigm, where intelligent applications are leveraged to drive network behavior and optimize network performance. In this abstract, we explore the role of the SDN application plane in bridging the gap between traditional network management and the intelligent, dynamic network of the future. We discuss the challenges and opportunities of deploying SDN applications, including the need for standardized APIs and the importance of intelligent analytics and machine learning techniques. Finally, we highlight the transformative potential of the SDN application plane, from enabling new applications and services to improving network security and resilience. By bringing intelligence and networking together in new and innovative ways, the SDN application plane promises to revolutionize the way we design, manage, and secure our networks.

The features of Python and its modules are compared in this paper against those of other programming languages like R. The article also delves into the characteristics and motivations that have contributed to Python and its modules' meteoric rise in favour among data scientists and app developers. Their value and importance in solving real-world problems and making meaningful advancements in existing applications are also not to be underestimated.

Machine learning has become a powerful tool in various domains, and practitioners are constantly seeking real-life use cases to develop unique and practical projects. In this context, a compelling machine learning project involves comparing classification algorithms, which allows for a comprehensive evaluation of their performance. This article aims to provide insights into comparing classification algorithms in machine learning, focusing on practicality and uniqueness. By utilizing Python, we showcase a detailed comparison of various classification algorithms to facilitate understanding and decision-making. The article serves as a valuable resource for individuals interested in exploring the process of comparing classification algorithms and gaining a deeper understanding of their strengths and weaknesses., {"references":["1.\tOsisanwo, F. Y., Akinsola, J. E. T., Awodele, O., Hinmikaiye, J. O., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3), 128-138.","2.\tChan, J. C. W., Chan, K. P., & Yeh, A. G. O. (2001). Detecting the nature of change in an urban environment: A comparison of machine learning algorithms. Photogrammetric Engineering and Remote Sensing, 67(2), 213-226.","3.\tWilliams, N., Zander, S., & Armitage, G. (2006). A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. ACM SIGCOMM Computer Communication Review, 36(5), 5-16.","4.\tJha, K. K., Jha, R., Jha, A. K., Hassan, M. A. M., Yadav, S. K., & Mahesh, T. (2021, December). A brief comparison on machine learning algorithms based on various applications: a comprehensive survey. In 2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) (pp. 1-5). IEEE.","5.\tCaruana, R., & Niculescu-Mizil, A. (2006, June). An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd international conference on Machine learning (pp. 161-168.","6.\tKhanam, J. J., & Foo, S. Y. (2021). A comparison of machine learning algorithms for diabetes prediction. Ict Express, 7(4), 432-439..","7.\tRogan, J., Franklin, J., Stow, D., Miller, J., Woodcock, C., & Roberts, D. (2008). Mapping land-cover modifications over large areas: A comparison of machine learning algorithms. Remote Sensing of Environment, 112(5), 2272-2283.","8.\tAbdualgalil, B., & Abraham, S. (2020, February). Applications of machine learning algorithms and performance comparison: a review. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (pp. 1-6). IEEE.","9.\tHuang, J. C., Ko, K. M., Shu, M. H., & Hsu, B. M. (2020). Application and comparison of several machine learning algorithms and their integration models in regression problems. Neural Computing and Applications, 32, 5461-5469..","10.\tSrihith, I. V. D., Kumar, I. V. S., Varaprasad, R., Mohan, Y. R., Srinivas, T. A. S., & Sravanthi, Y. (2022). Future of Smart Cities: The Role of Machine Learning and Artificial Intelligence. South Asian Research Journal of Engineering and Technology, 4(5), 110-119.."]}

This paper explores the use of Pandas, a popular Python library, to tackle a wide range of data science tasks efficiently. By leveraging just 20 key Pandas functions, it is possible to accomplish approximately 80% of the common data manipulation and analysis tasks encountered in data science projects. The paper provides an overview of these functions, highlighting their capabilities and how they can be applied in various scenarios. By understanding and mastering these functions, data scientists can significantly enhance their productivity and streamline their workflow., {"references":["1.\tMcKinney, W. (2012). Python for Data Analysis. OReilly Media. Inc., Sebastopol.","2.\tGhoniem, A, Mohamed F,(2015). Data Cleaning and Preprocessing Techniques: A Review. International Journal of Computer Applications.120(1).","3.\tPandas Documentation: The official documentation for Pandas is available at https://pandas.pydata.org/docs/. It provides detailed information about Pandas functions, methods, and usage examples.","4.\tKaggle Kernels: Kaggle Kernels are accessible on the Kaggle platform at https://www.kaggle.com/kernels. You can search for specific kernels related to data cleaning, preprocessing, and analysis using Pandas."]}

Submitting a manuscript to a journal is a crucial step in the research publication process. However, many manuscripts are desk rejected, which can be discouraging for authors. This paper presents practical tips and strategies to avoid desk rejection and increase the chances of publishing a quality research article. The paper outlines the common reasons for desk rejection, including poor structure, language, and methodology, and offers solutions to overcome these issues. Additionally, the paper provides guidance on selecting an appropriate journal, crafting a compelling cover letter, and responding to reviewer feedback. By following the advice in this paper, authors can increase the likelihood of success in journal submission, avoid desk rejection, and ultimately publish a quality research article., {"references":["1.\tDwivedi, Y. K., Hughes, L., Cheung, C. M., Conboy, K., Duan, Y., Dubey, R., ... & Viglia, G. (2022). How to develop a quality research article and avoid a journal desk rejection. International Journal of Information Management, 62, 102426.","2.\tPutman, M., Berquist, J. B., Ruderman, E. M., & Sparks, J. A. (2022). Any given Monday: Association between desk rejections and weekend manuscript submissions to rheumatology journals. The Journal of Rheumatology, 49(6), 652-653.","3.\tGoyal, M., Santhanam, S., & Ravindran, V. (2023). Desk rejections: not without due deliberation. The Journal of Rheumatology, 50(1), 155-156.","4.\tTeixeira da Silva, J. A., Al-Khatib, A., Katavić, V., & Bornemann-Cimenti, H. (2018). Establishing sensible and practical guidelines for desk rejections. Science and Engineering Ethics, 24, 1347-1365..","5.\tGarand, J. C., & Harman, M. (2021). Journal Desk-Rejection Practices in Political Science: Bringing Data to Bear on What Journals Do. PS: Political Science & Politics, 54(4), 676-681.","6.\tCho, Y. (2021). Why do we desk-reject?. Human Resource Development Review, 20(3), 279-281.","7.\tPombo, C., & Ogliastri, E. (2015). Editorial note on desk rejection policy. Academia Revista Latinoamericana de Administración, 28(1), 9-13.."]}

Determining the optimal amount of training data for machine learning algorithms is a critical task in achieving successful and accurate models. This abstract delves into the research surrounding this question and provides insights into the factors that affect the quantity of training data required for effective machine learning. It explores the delicate balance between data quality and quantity, the concept of over fitting, and the importance of representative and diverse datasets. Additionally, it discusses the various techniques and approaches used to estimate the minimum training data required for achieving desirable performance. By understanding the implications of training data size on model performance, researchers and practitioners can make informed decisions in selecting appropriate training datasets, thereby maximizing the efficiency and effectiveness of machine learning algorithms., {"references":["1.\tHalevy, A., Norvig, P., & Pereira, F. (2009). The Unreasonable Effectiveness of Data. IEEE Intelligent Systems, 24(2), 8-12.","2.\tLe, Q., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G. S., Ng, A. (2012). Building high-level features using large-scale unsupervised learning. In Proceedings of the 29th International Conference on Machine Learning (ICML-12):1025-1032.","3.\tRudin, C. (2019). The Mythos of Model Interpretability. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(3), 1019-1047.","4.\tHe, H., Bai, Y., Garcia, E. A., & Li, S. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263-1284.","5.\tBengio, Y. (2012). Practical Recommendations for Gradient-Based Training of Deep Architectures. In Neural Networks: Tricks of the Trade.:437-478)."]}

Men have faster loss of kidney function and greater renal renin-angiotensin system (RAS) activity compared with women. Obstructive sleep apnea (OSA) is common in chronic kidney disease; the vascular effects of OSA differ by sex, and OSA-associated glomerular hyperfiltration can be reversed by continuous positive airway pressure (CPAP) therapy. We evaluated sex differences in the effect of CPAP on renal hemodynamics and the renal RAS in OSA. Twenty-nine Na+-replete, otherwise healthy study participants with OSA (10 women and 19 men) with nocturnal hypoxemia were studied pre- and post-CPAP (>4 h/night for 4 wk). Renal hemodynamics [renal plasma flow (RPF), glomerular filtration rate (GFR), and filtration fraction(FF)] were measured at baseline and in response to ANG II challenge, as a marker of renal RAS activity, pre- and post-CPAP therapy for 1 mo. In women, CPAP was associated with increased RPF (626 ± 22 vs. 718 ± 43 mL/min, P = 0.007, pre- vs. post-CPAP), maintained GFR (108 ± 2 vs. 105 ± 3 mL/min, P = 0.8), and reduced FF (17.4 ± 0.8% vs. 15.0 ± 0.7%, P = 0.017). In men, CPAP was associated with maintained RPF (710 ± 37 vs. 756 ± 38 mL/min, P = 0.1), maintained GFR (124 ± 8 vs. 113 ± 6 mL/min, P = 0.055), and reduced FF (18.6 ± 1.7% vs. 15.5 ± 1.1%, P = 0.035). Pre-CPAP, there were no sex differences in renal hemodynamic responses to ANG II. CPAP use was associated with a greater renovasoconstrictive response to ANG II in women (RPF at Δ30 min: −100 ± 27 vs. −161 ± 25 mL/min, P = 0.007, and RPF at Δ60 min: −138 ± 27 vs. −206 ± 32 mL/min, P = 0.007) but not men. CPAP use was associated with improved renal hemodynamics in both sexes and downregulated renal RAS activity in women but not men.

Structural characterization of the prion prone TAR DNA Binding protein (TDP)-43 has been challenging since its intrinsically disordered regions represents 15-30% of the total protein. TDP-43 is a nucleic acid binding protein with an N-terminal domain, two RNA Recognition Motifs (RRM1 and RRM2) and the C-terminal domain. In this study, we seek to define possible new targetable sites on the apo structure of TDP-43 RRM domains. To do so, we used molecular dynamic (MD) simulations on the NMR solved TDP-43RRM1-2 structure bound to RNA to predict the apo structure. Contact analysis of TDP-43 showed that while the integrity of the individual domains was maintained upon RNA removal, a decrease in interdomain contacts was observed. Moreover, we compared apo TDP-43 structures obtained from MD to AlphaFold 2 (AF2) predicted TDP-43 structures and found differences in loop regions. A Sitemap analysis identified five druggable sites for the RNA bound structure solved by NMR, while fewer sites were identified following MD simulations and AF2 predicted apo structures.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with no cure or effective treatment in which TAR DNA Binding Protein of 43 kDa (TDP-43) abnormally accumulates into misfolded protein aggregates in affected neurons. It is widely accepted that protein misfolding and aggregation promotes proteotoxic stress. The molecular chaperones are a primary line of defense against proteotoxic stress, and there has been long-standing interest in understanding the relationship between chaperones and aggregated protein in ALS. Of particular interest are the heat shock protein of 70 kDa (Hsp70) family of chaperones. However, defining which of the 13 human Hsp70 isoforms is critical for ALS has presented many challenges. To gain insight into the specific Hsp70 that modulates TDP-43, we investigated the relationship between TDP-43 and the Hsp70s using proximity-dependent biotin identification (BioID) and discovered several Hsp70 isoforms associated with TDP-43 in the nucleus, raising the possibility of an interaction with native TDP-43. We further found that HspA5 bound specifically to the RNA-binding domain of TDP-43 using recombinantly expressed proteins. Moreover, in a Drosophila strain that mimics ALS upon TDP-43 expression, the mRNA levels of the HspA5 homologue (Hsc70.3) were significantly increased. Similarly we observed upregulation of HspA5 in prefrontal cortex neurons from human ALS patients. Finally, overexpression of HspA5 in Drosophila rescued TDP-43-induced toxicity, suggesting that upregulation of HspA5 may have a compensatory role in ALS pathobiology.