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Recent Advances in Computer Science and Communications - Online First
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Jamming Attacks Detection Based on IGWO for Optimization of Fast Correlation-Based Feature extraction in Wireless Communication
Authors: Zinah Jaffar Mohammed Ameen, Hala A. Naman and Alza Abduljabbar MahmoodAvailable online: 25 October 2024More LessBackgroundWireless networks are essential communication technologies that prevent cable installation prices and burdens. Because of this technology's pervasive usage, wireless network safety is a significant problem. Owing to distributed and open wireless medium aspects, attackers might use different jamming methods to exploit physical and MAC layer protocol vulnerabilities. In addition, jamming attacks require to be accurately grouped so that suitable countermeasures can be considered. Given the potential severity of such attacks, precisely identifying and classifying them is critical for implementing effective responses. The motivation for this paper is the need to improve the detection and categorization of jamming signals using modern machine learning algorithms, consequently enhancing wireless network security and reliability.
ObjectiveIn this paper, we compare some machine learning models' efficiency for diagnosing jamming signals.
MethodsSuch algorithms refer to support vector machine (SVM) and k-nearest neighbors (KNN). We checked the signal features that recognize jamming signals. After the jamming attack model, the developed grey wolf optimizer version known as IGWO (improved grey wolf optimizer) has been discussed for feature extraction of software usability. Four separate metrics were employed as features to detect jamming attacks in order to evaluate the machine learning models. This novel feature extraction method is crucial for improving the accuracy of jamming detection.
ResultsThe measurements of these parameters were gathered through a simulation of a real setting. And generated a large dataset using these parameters.
ConclusionThe simulation results illustrate that the KNN algorithm based on jamming detection could diagnose jammers having a minimal likelihood of false alarms and a high level of accuracy.
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Malignancy Detection in Lung and Colon Histopathology Images by Transfer Learning with Class Selective Image Processing
Available online: 23 October 2024More LessAims & BackgroundDue to its ferocity, enormous metastatic potential, and variability, cancer is responsible for a disproportionately high number of deaths. Cancers of the lung and colon are two of the most common forms of the disease in both sexes worldwide. The excellence of treatment and the endurance rate for cancer patients can be greatly improved with early and precise diagnosis.
Objectives & MethodologyWe suggest a computationally efficient and highly accurate strategy for the rapid and precise diagnosis of lung and colon cancers as a substitute for the standard approaches now in use. The training and validation procedures in this work made use of an enormous dataset consisting of lung and colon histopathology pictures. There are 25,000 Histopathological Images (HIs) in the dataset, split evenly among 5 categories (mostly lung and colon tissues). Before training it on the dataset, a pretrained neural network (AlexNet) had its four layers fine-tuned.
ResultsThe study enhances malignancy detection in lung and colon histopathology images by applying transfer learning with class-selective image processing. Instead of enhancing the entire dataset, a targeted contrast enrichment was applied to images from the underperforming class, improving the model's accuracy from 92.3% to 99.2% while reducing computational overhead. CONCLUSION: This approach stands out by emphasizing class-specific enhancements, leading to significant performance gains. The results meet or exceed established CAD metrics for breast cancer histological images, demonstrating the method's efficiency and effectiveness.
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Utilizing AspectJ for Defense Against Evasive Malware Attacks in Android System
Authors: Ketaki Pattani, Sunil Gautam, Mamoon Rashid, Mohd Zuhair and Ahsan RizviAvailable online: 18 October 2024More LessIntroductionMobile devices have become an integral part of our digital lives, facilitating various tasks and storing a treasure trove of sensitive information. However, as more people utilize mobile devices, sophisticated cyber threats are emerging to elude traditional security measures.
MethodThe use of evasion techniques by malicious actors presents a significant challenge to mobile security, necessitating creative solutions. In this work, we investigate the potential critical role that the aspect-oriented programming paradigm AspectJ can play in strengthening mobile security against evasion attempts. Evasion techniques cover a wide range of tactics, including runtime manipulation, code obfuscation, and unauthorized data access.
ResultsThese tactics usually aim to bypass detection and avoid security measures. In order to address the aforementioned issues, this paper uses AspectJ to give developers a flexible and dynamic way to add aspects to their coding structures so they can monitor, intercept, and respond to evasive actions. It illustrates how AspectJ can improve mobile security and counteract the long-lasting risks that evasion techniques present in a dynamic threat landscape.
ConclusionConsequently, this work proposes a novel defense mechanism incorporating AspectJ into a significant paradigm of security against evasion with 91.33% accuracy and demonstrates the successful detection of evasive attacks.
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Biofuels Policy as the Indian Strategy to Achieve the 2030 Sustainable Development Goal 7: Targets, Progress, and Barriers
Authors: Michel Mutabaruka, Manmeet Kaur, Sanjay Singla, Purushottam Sharma, Gurpreet Kaur and Gagandeep SinghAvailable online: 16 October 2024More LessIntroductionThe use of 20% blended biofuels to fossil fuels is one of the important targets of the Government of India to address the impacts of Climate Change, energy-related environmental pollution, and illnesses due to air pollution.
MethodThe National Policy on Biofuels 2018 (NPB 2018) is in place to boost the emerging production of biofuels and, therefore, respond to different international agreements, including the Sustainable Development Goals (SDGs) and the Paris Agreement. Hence, this article examined the production of biofuels in India in line with Agenda 2030 to project the share to be taken by biofuels as its contribution to the country’s energy needs.
ResultThe results were compromising; it was observed that the data from 2000 up to 2017 were not on the side of realizing the targets of production and consumption of biofuels in India, whereas the data from 2018 up to now showed a hope of achieving 2030 set goal of E20 petrol in 2025-26, and E5 diesel in 2030. It was clear that the production of bioethanol was boosting compared to its sibling biodiesel, and renewable energy will continue to have a hard take a good share in the total annual energy used in India.
ConclusionIt is recommended to share data between different stakeholders to promote more research, as the low performance in achieving the targets was due to poor communication and missing technology, rather than the lack of feedstock or unavailability of production facilities.
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Data Security and Privacy Preservation in Cloud-Based IoT Technologies: an Analysis of Risks and the Creation of Robust Countermeasures
Authors: Mayank Pathak, Kamta Nath Mishra and Satya Prakash SinghAvailable online: 16 October 2024More LessThe Internet of Things (IoT) is a revolutionary technology being used in many different industries to improve productivity, automation, and comfort of the user in the cloud and distributed computing settings. Cloud computing is essential because it makes data management and storage more effective by automatically storing and examining the enormous amounts of data generated by Internet of Things applications. End users, companies, and government data are consequently migrating to the cloud at an increasing rate. A survey of the literature, however, reveals a variety of issues, including data integrity, confidentiality, authentication, and threat identification, that must be resolved to improve data security and privacy. To effectively address contemporary data security concerns, the existing approaches need to be improved. Ensuring secure end-to-end data transmission in a cloud-IoT situation requires innovative and dependable protocol architecture. New technologies that address some of the issues related to cloud data include edge computing, fog, blockchain, and machine learning. This paper provides a thorough examination of security risks, classifying them and suggesting possible defenses to safeguard cloud-IoT data. It also highlights innovative approaches, such as blockchain technology and machine learning, applied to privacy and data security. The paper also explores existing issues with respect to data privacy and security in today's cloud-IoT environments. It suggests possible future directions, including the need for end-user authentication, enhanced security, and procedures for recovering data in the event of an attack.
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Machine Learning Empowered Breast Cancer Diagnosis: Insights from Coimbra Dataset Analysis
Authors: Manish Tiwari, Nagendra Singh, Arvind Mewada and Mohd. Aquib AnsariAvailable online: 11 October 2024More LessAimThe aim of this work is to succinctly communicate the key aspects of a research study on breast cancer. This includes highlighting the global impact and prevalence of breast cancer, emphasizing the challenges of early diagnosis, discussing the potential of technological advancements, and showcasing the role of machine learning algorithms in the context of liver cancer diagnosis.
BackgroundCancer, notably breast cancer, represents a global health challenge, claiming a significant toll with 12.5% of new cancer cases annually. The prevalence of breast cancer among women worldwide is alarming, resulting in 2.26 million incidents and the unfortunate loss of 685,000 lives.
ObjectiveThis article focuses on the critical aspect of early breast cancer diagnosis, acknowledging its heightened difficulty in developing nations compared to developed counterparts. The potential for advancements in technology to serve as a beacon of hope lies in early identification and timely treatment, offering salvation to numerous women and significantly elevating survival chances.
MethodIn this intricate landscape, machine learning algorithms, particularly in diagnosing liver cancer at its nascent stages, emerge as instrumental tools. The study employs the latest Coimbra dataset, encompassing nine key attributes and a binary classification attribute, with values 1 and 2 signifying benign and malignant cases, respectively.
ResultSupervised machine learning algorithms, including Bayes net, multilayer perceptron, IBK, random committee, and random tree, are meticulously applied. Certain models exhibit superior accuracy, precision, recall, and performance, positioning them as promising cornerstones for breast cancer analysis.
ConclusionThis structured abstract highlights the urgent need for effective screening and prevention strategies, emphasizing the potential of advanced technology and machine learning algorithms to play a pivotal role in the early detection and analysis of breast cancer, offering hope for improved outcomes and survival rates.
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Analysis and Classification of Medical Images Using Deep Learning Algorithms
Authors: Chouchene Karima, Nadjla Bourbia, Kamel Messaoudi and El-Bay BourennaneAvailable online: 10 October 2024More LessIntroductionNowadays, Artificial intelligence and machine learning have emerged as a powerful tool for the analysis of medical images such as MRI scans. This technology holds significant potential to improve diagnostic services and accelerate medical advances by facilitating clinical decision-making.
MethodIn this work, we developed a Convolutional Neural Network (CNN) model specifically designed for the classification of medical images. Using a selected database, the model achieved a classification accuracy of 92%. To further improve the performance, we leveraged the pre-trained VGG16 model, which increased the classification accuracy to 100%. Additionally, we preprocessed the MRI images using the Roboflow platform and then developed YOLOv5 models for the detection of tumors, infections, and cancerous lesions.
ResultThe results demonstrate a localization accuracy of 50.41% for these medical conditions.
ConclusionThis research highlights the value of AI-driven approaches in enhancing medical image analysis and their potential to support more accurate diagnoses and accelerate advancements in healthcare.
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Comparative Analysis of Different Transport Layer Protocol Techniques Incognitive Network
Authors: Ajay Kumar and Naveen HemrajaniAvailable online: 09 October 2024More LessMost of the networks employ TCP protocol for transmission control in transport mechanism. Although it offers numerous services such as reliability, end-to-end delivery, secure transmission of data, and so on to applications functioning across the World Wide Web, TCP needs to have effective congestion management techniques in order to handle traffic with a lot of data. Still, TCP have poor performance during data transmission in the network. The network community continues conducting research to develop a method that should provide a fair and effective transmission bandwidth distribution. Numerous congestion control strategies have been developed based on previous research in this area. This work discusses, identifies, compares, and analyses the behaviour of a few network congestion control strategies to determine their benefits and limitations. The widely known network simulator ns2 is employed for the simulation. The performance metrics for QTCP, TCP new Reno, TCP- Hybla, L-TCP and RL-TCP are throughput, average delay, PDR, packet loss, average jitter, latency and fairness are considered. The RL-TCP exhibits superior performance in multiple measures when it is compared to alternative TCP protocols, as indicated by simulation results. These metrics encompass throughput, average delay, packet delivery ratio (PDR), packet loss, jitter, latency, and fairness. Furthermore, several TCP protocols, such as L-TCP, TCP-Hybla, QTCP, and TCP-New Reno, have undergone evaluation, uncovering disparities in their individual performance attributes. Nevertheless, the RL-TCP regularly demonstrates superior performance in all aspects.
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AI Chatbots in Fintech Sector: A Study Towards Technological Convergence
Authors: Chandni Bansal, Ajay Kumar, Namrata Dogra, Gaydaa AlZohbi and Chand PrakashAvailable online: 09 October 2024More LessThe Fintech industry, particularly banks, has witnessed a profound transformation with the integration of Artificial Intelligence chatbots, redefining customer experience and engagement. As Fintech firms increasingly integrate AI chatbots into their platforms, understanding customer perceptions becomes paramount for strategic decision-making and sustained success. To unravel the complexities of this convergence, a holistic examination is needed, encompassing not only the technological aspects but also the strategic dimensions that underpin competitive advantage. In this context, the role of intellectual property, particularly patents, emerges as a critical factor shaping the innovation landscape. This research aims to comprehensively investigate customers' perceptions towards AI chatbots in the Fintech industry, with a specific focus on technological convergence. The study seeks to analyze the impact of cutting-edge AI chatbot technologies, including those protected by patents, on user attitudes and overall customer experience within the dynamic fintech landscape. This study provides a comprehensive review of 40 empirical studies on AI chatbots in the fintech industry, particularly the banking sector, featuring patented innovations using the PRISMA methodology. Study outcomes illustrate emerging themes related to consumer behavior and response to financial chatbots in terms of acceptance and adoption intention. Additionally, four key factors that influence how people perceive, anticipate, and engage with fintech chatbots, namely satisfaction, trust, anthropomorphism, and privacy are explored. In conclusion, the finance industry's effective integration and broad use of AI chatbots is dependent on the convergence of four factors: satisfaction, privacy, trust, and anthropomorphism. Current study offers a strong basis for analysing and resolving the obstacles to AI chatbot acceptance and deployment in the financial sector by addressing all these elements extensively. This exploration of technological convergence in fintech industry by analyzing customers' behavior and response to financial chatbots not only contributes to a comprehensive understanding of its intricacies but also serves as a foundation for development and deployment of user-centric fintech chatbots.
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Applying Polynomials for Developing Post Quantum Cryptography Algorithms to Secure Online Information - An Initial Hypothesis
Authors: Taniya Hasija, K. R. Ramkumar, Bhupendra Singh, Amanpreet Kaur and Sudesh Kumar MittalAvailable online: 04 October 2024More LessIn the contemporary era, a vast array of applications employs encryption techniques to ensure the safeguarding and privacy of data. Quantum computers are expected to threaten conventional security methods and two existing approaches, namely Shor's and Grover's algorithms, are expediting the process of breaking both asymmetric and symmetric key classical algorithms. The objective of this article is to explore the possibilities of creating a new polynomial based encryption algorithm that can be both classically and quantum safe. Polynomial reconstruction problem is considered as a nondeterministic polynomial time hard problem (NP hard), and the degree of the polynomials provide the usage of scalable key lengths. The primary contribution of this study is the proposal of a novel encryption and decryption technique that employs polynomials and various polynomial interpolations, specifically designed for optimal performance in the context of a block cipher. This study also explores various root convergence techniques and provides algorithmic insights, working principles and the implementation of these techniques, which can potentially be utilized in the design of a proposed block-cipher symmetric cryptography algorithm. From the implementation, comparison and analysis of Durand Kernal, Laguerre and Aberth Ehrlich methods, it is evident that Laguerre method is performing better than other root finding approaches. The present study introduces a novel approach in the field of polynomial-based cryptography algorithms within the floating-point domain, thereby offering a promising solution for enhancing the security of future communication systems.
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A Policy Configured Resource Management Scheme for Ahns Using LR-KMA and WD-BMO
Available online: 03 October 2024More LessIntroductionA critical technique that provides quality service for users by solving the conflicts between severe spectrum scarcity and the explosive growth of traffic is Cognitive Radio Ad Hoc Networks (CRAHNs). Nevertheless, a critical challenge is the coexistence of primary and secondary users for reasonable resource allocation to satisfy system performance. Many approaches have been developed to allocate resources efficiently; however, they possess some existing limitations, such as abnormal traffic networks, user collisions, and high data transmission error rates.
MethodSo, to overcome such limitations, this paper proposes an efficient policy-configured reinforcement learning-based Ad Hoc Network (AHN) model. The system begins with modeling the Cognitive Radio (CR) network in which the nodes are initialized and clustered using the Link Reliability K-Means clustering Algorithm (LR-KMA) method to derive the optimal policy configuration for the network. Then, to sense the available spectrum and divide it into several bands, spectrum sensing using Coherent Based Detection (CBD) and signal source prediction using the Parzen-Rosenblatt Window-based Restricted Boltzmann Machine (PRW-RBM) were performed.
ResultNext, the suitable bands are selected in the learning model using the Weibull Distribution-based Blue Monkey Optimization (WD-BMO) technique for the resource allocation process. The experimental outcomes were ultimately analyzed to evaluate the proposed resource allocation model's performance in CRAHNs. The LR-KMA algorithm showed 1.5% higher clustering efficiency than traditional methods, while PRW-RBM achieved 1.07% higher classification accuracy.
ConclusionThe optimal resource allocation strategy, WD-BMO, led to lower Normalized Objective Values (NOV) compared to existing methods.
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One Pseudo-satellite/GNSS Combined Indoor and Outdoor Fusion Positioning Method Based on Carrier Phase Measurement
Authors: Xiaobo Zhao, Ronghua Hao and Xuefei BaiAvailable online: 25 September 2024More LessBackgroundIn order to realize seamless indoor and outdoor positioning, the positioning results of multiple positioning methods are taken into consideration, and a seamless indoor and outdoor positioning method that ignores the differences in indoor and outdoor environments is required now.
ObjectiveThe implementation of Pseudo satellite/GNSS combined indoor and outdoor fusion positioning for seamless indoor and outdoor environment positioning.
MethodsAn adaptive federated filter is needed for this environment, which can dynamically adjust the information allocation parameters and measurement noise of the sub-filters in the federated filters based on positioning data. It adopts multi-sensor fusion filter to design a seamless indoor and outdoor positioning method. Different positioning data is fused through federated filtering, ultimately seamless indoor and outdoor positioning is realized.
ResultsThis algorithm achieves a fixed ambiguity pseudo satellite/GNSS accuracy of better than 0.15 meters in low-density buildings where there are more than 7 GNSS satellites. When there are fewer than 4 GNSS satellites and the positions is severely obstructed, GNSS alone cannot realize the position, but with the support of pseudo satellites, the accuracy of position can be better than 0.3m. Even without GNSS and only 4 pseudo satellites, the accuracy of position can still be better than 0.5 m.
ConclusionThe relevant experimental results indicate that the method proposed can be used for practical applications of indoor and outdoor fusion positioning.
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