Navigating: Reshaping Maps for Mobile Robots with Shortest Path Analysis

Journal of Cognitive Human-Computer Interaction | 2024

A Transfer Learning Framework for Sentiment Analysis in Indian Vernaculars

This paper explores sentiment analysis in Indian languages through a deep learning approach, combining machine learning techniques with natural language processing (NLP). Three neural network architectures—CNN, LSTM, and GRU—are employed to construct sentiment analysis models. Additionally, transfer learning is utilized via FastText, MURIL, and IndicBERT embeddings. The models are trained and evaluated on a translated dataset derived from the Sentiment140 dataset from Kaggle. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the models. The study addresses the challenges of sentiment analysis in Indian languages by leveraging deep learning techniques and linguistic diversity, providing insights into sentiment analysis across diverse languages and cultures. Furthermore, this project extends its analysis to include work on Gujarati, Marathi, and Sindhi languages, contributing to the understanding of sentiment analysis in a broader spectrum of Indian languages

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Kumal Kumar mail -

Shivam Kumar mail

link https://doi.org/10.54216/JCHCI.080102

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

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Journal of Cognitive Human-Computer Interaction | 2024

Sybil Attack Detection Techniques in Wireless Network: A Comprehensive Review

In today's rapidly evolving world, wireless technology has emerged as an essential solution for establishing connectivity in diverse environments. They offer cost-effective deployment options and scalability, accommodating organizational growth without the need for extensive infrastructure changes. However, wireless networks are susceptible to various security attacks, including Sybil attacks. In this paper, we provide a comprehensive review of Sybil attack detection techniques in wireless networks i.e. Mobile Ad hoc Network (MANET) and Wireless Mesh Networks (WMNs). In this paper, we analyze a range of methods proposed to detect and mitigate Sybil attacks, including approaches based on genetic algorithms, fuzzy logic, secure routing protocols, and hybrid techniques combining different detection mechanisms. Additionally, we explore the use of bio-inspired algorithms, such as the Bacteria Foraging Optimization Algorithm (BFOA), and discuss novel strategies integrating node authentication and threshold-based mechanisms. By examining the strengths and limitations of each approach, this review offers valuable insights into the state-of-the-art in Sybil attack detection in wireless networks, aiding researchers and practitioners in developing robust security solutions.

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Lalzuitluanga mail -

Lalremruata mail -

Vanlalhruaia mail

link https://doi.org/10.54216/JCHCI.080103

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

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Journal of Cognitive Human-Computer Interaction | 2024

Revolutionizing Urban Energy Landscapes: A Robust Framework for Sustainable Integration through Advanced Smart Grid Architecture

A smart city coordinates resource allocation to provide a safe, efficient, and good living environment. Smooth integration of advanced technologies optimises resource consumption, notably power control. Optimizing power control includes strategically placing connected devices to optimize electricity utilisation. Intelligent urban environments need recognising this issue and solving it. Multiple solutions are needed for smart city energy optimisation. However, on-going scientific debate attempts to construct a ground-breaking intelligent grid design that can gather electricity from PV, hydro, and thermal sources. A delay-aware delivery system handles the challenging challenge of real-time energy optimisation (ECRT). Optimising energy expenditure in real time matches demand and supply. This project intends to build a smart grid that regulates electrical operations and uses sustainable energy sources. The paper focuses modelling renewable energy and improving energy distribution. We want to boost smart city energy efficiency. The hybrid smart grid proposes an effective energy resource management system that blends numerous energy production sources and real-time energy expenditure optimisation. The harmonious integration of sustainable energy sources and novel control systems improves resource allocation while being sustainable. This insightful study discusses smart energy system concepts and solutions in a technologically sophisticated city. Real-time energy optimisation and sustainable energy sources show an on-going commitment to increasing efficiency, resource utilisation, and sustainable design in intelligent urban environments.

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C. Manigandaa mail -

Manikandan Vasanthakumar mail -

C. Manikantaa mail -

Dharanidharan M. mail -

Agnus S. mail

link https://doi.org/10.54216/JCHCI.080104

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

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Fusion: Practice and Applications | 2024

Multi-Level Fusion for Enhanced Host-based Malware Detection in ICT-Enabled Smart Cities

In smart cities, the widespread adoption of Information and Communication Technologies (ICTs) presents both opportunities and challenges for security. While ICTs enable increased productivity, data sharing, and improved citizen services, they also create new vulnerabilities for malicious actors to exploit. This necessitates robust host-based security solutions to protect critical infrastructure and data. This paper proposes a novel multi-level fusion approach for enhanced host-based malware detection in ICT-enabled smart cities. By leveraging diverse data sources and employing advanced fusion techniques, our approach achieves significant improvements in malware detection accuracy, network evaluation, and security analysis compared to existing methods. Specifically, our proposed approach demonstrates a 72.1% malware detection rate across various attack scenarios, 69.7% accuracy in host network evaluation, 82.8% reduction in security analysis error, 75.4% accuracy in network probability detection, and an overall accuracy of 67.2%. These results showcase the potential of multi-level fusion for strengthening host-based security in smart cities. This approach offers several advantages over traditional host-based security solutions. Firstly, it provides more comprehensive threat detection by utilizing multiple data sources. Secondly, it reduces the burden on IT administrators by automating security analysis and decision-making. Finally, it enables continuous improvement through adaptive learning and feedback mechanisms. Overall, our multi-level fusion approach represents a promising advancement in host-based security for ICT-enabled smart cities. It offers significant improvements in accuracy and efficiency, paving the way for a more secure and resilient urban environment.

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Alaa Q. Raheema mail -

Massila Kamalrudin mail -

Nur Rachman Dzakiyullah mail

link https://doi.org/10.54216/FPA.150220

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Artificial Intelligence based Computer Vision Analysis for Smart Education Interactive Visualization

The pedagogical of computer programming education is being enriched and improved through the interactive learning material. Visualization, modeling, and internet platforms for developing interactive visual skills are only a few examples of the types of specialized learning material currently accessible for use in a wide range of computing classes. There are some specific challenges related to the implementation of active learning, such as insufficient time for class, an increase in preparation, implementing students' engagement in extensive courses, and a lack of necessary materials, technology, or supplies. Computer vision is a subfield of AI that allows machines to learn from visual data (such as photos, movies, and other digital media) and then act on or offer solutions to problems. To enhance the efficiency of intelligent interactive learning and practice, this article incorporates a visual machine vision analytical framework under the guidance of Artificial intelligence to create a Machine-Vision-based Smart Education Assistance System (MV-SEAS). Visualization speeds up and simplifies regular communication by consolidating several forms of information into a single visual representation. This study discusses how visualizing information is crucial for students' initial knowledge acquisition and continued education and development. The seamless amalgamation of automated innovative education analyses and interactive visualizations is emphasized. The paper aimed to identify and characterize the technical challenges mentioned above must be surmounted to make it simpler for computer educators to discover, adopt, and tailor intelligent learning materials. The study concludes by proposing an MV-SEAS for storing, integrating, and disseminating smart educational data. It investigates whether it can be done using existing standards and guidelines. In the end, this essay combines trials to prove the effectiveness of the proposed smart education method. The findings demonstrate that interactive visualization of AI-assisted smart education may effectively combine subject experts' information with educators' experience to produce more powerful and easily intelligible machine intelligence.

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Asma Khazaal Abdulsahib mail -

Ruwaida Mohammed mail -

Ahmed Luay Ahmed mail -

Mustafa Musa Jaber mail

link https://doi.org/10.54216/FPA.150221

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Deep Learning based Mango Leaf Disease Detection for Classifying and Evaluating Mango Leaf Diseases

Mango is one of the important commercial crop in the world. It provides nutritional and financial support to human life. Different diseases of leaves impact the health of the mango crops. The early and proper pest control measurement can prevent large output losses. We propose an automated inspection and classification of disease-affected mango leaves that uses Deep Learning (DL) model. Our DL model-empowered Convolutional Neural Network (CNN) architecture is trained with an extensive image dataset of mango leaves portraying a variety of disease indications at both low and high-resolution images. The objective is to be able to identify accurately the disease type on mango leaves including Bacterial Canker, Powdery mildew, Anthracnose, Gall midge, and Sooty mould. Crops can develop gradual immunity with reasonable pest control and can purposively shaped them against constantly evolving environment. The proposed system will be effective and it will definitely prove a facile system to be used as a key component of a novel precision agriculture system as will be presented in our future work. The performance of the proposed system is augmented through the utilization of transfer learning techniques and pre-trained models, including VGG-16, MobileNet, Googlenet, YoloV8, and EfficientNet. These Deep Learning models not only offer an accurate and efficient approach for classifying diseases in mango leaves but also provide valuable insights into the severity of the identified diseases. Utilizing this information to support farmers and agricultural professionals in making informed decisions pertaining to disease management and treatment strategies can significantly contribute to the sustainable growth of mango crops. The development and implementation of such automated technologies have the potential to revolutionize the monitoring of mango crop health, enabling early disease detection and enhancing crop yields.

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V. Krishna Pratap mail -

N. Suresh Kumar mail

link https://doi.org/10.54216/FPA.150222

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Optimizing IoT Wireless Sensor Networks: A Comparative Analysis of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA)

The IoT can be defined as a system of various types of computing and digital devices, machines, objects, animals, and humans that are connected through networks to send data without the need for direct person-to-person or computer-to-person interfaces. Every component in this structure is given a unique identity. While under the domain of IoT, WSN serves as a wireless sensor network that does not have an established infrastructure but consists of many wireless sensors for surveillance over systems, the environment, and the physical world. Because of its versatile usage like surveillance and environmental monitoring, Wireless Sensor Networks (WSNs) are vital in many applications. The performance of these networks is largely dependent on how sensor nodes are distributed across the area to provide good coverage and connectivity. In this paper, we propose a new method for node placement optimization in WSNs, which tries to solve the problem of coverage holes at the stage of initial deployment. Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are implemented using MATLAB to deal with the problem's complex and non-linear nature. These algorithms help find optimal node positions, thus improving coverage while ensuring no coverage gaps occur. A way to achieve this is through iterations, which involve fitness evaluation, selection of promising solutions, and genetic operators like crossover and mutation or position updates for PSO to investigate and improve the final solution. The simulation results mentioned in this paper demonstrate the usefulness of those methods, displaying major increases in coverage and the removal of all gaps that could appear in the initial deployment. This research contributes to the field of wireless sensor network optimization, specifically addressing coverage issues using GA and PSO algorithms …

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Jumana J. Al-zamili mail -

Hala A. Al-Zubaidi mail

link https://doi.org/10.54216/FPA.150223

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Multi-Objective Evolutionary Algorithm to Optimize IoT Based Scheduling Problem Using (NSGA-II Algorithm)

Due to the continual advancements in the Internet of Things (IoT), which generate enormous volumes of data, the cloud computing infrastructure recently has received the most significance. to meet the demands made by the network of IoT devices. It is anticipated that the planned Fog computing system would constitute the next development in cloud computing. The optimal distribution of computing capacity to reduce processing times and operating costs is one of the tasks that fog computing confronts. In the IoT, fog computing is a decentralized computing approach that moves data storage and processing closer to the network's edge. This research article discusses a unique technique for lowering operating expenses and improving work scheduling in a cloud-fog environment. Non-dominated sorting genetic algorithm II (NSGA-II) is a proposal that is presented in this paper. Its purpose is to allocate service requests with the multi-objective of minimising finishing time and running cost. Determining the Pareto front that is associated with a group of perfect solutions, which are sometimes referred to as non-dominated solutions or Pareto sets, is the fundamental objective of the Pareto NSGA-II. There is a contradiction between the environmental and economic performances, which is shown by the Pareto set of sub-optimal solutions that are the consequence of the bi-objective issue.

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Syed Mutiullah Hussaini mail -

T. Abdul Razak mail -

Muhammad Abid Jamil mail

link https://doi.org/10.54216/JISIoT.120209

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

A Hybrid Logistic Scroll Chaotic Encryption Algorithm for Ensuring the Cloud Security to Counerfeiting the Attacks

Cloud computing is meant for storing the huge data using third party that ensures that confidential data cannot be accessed by the other users. But with rapid growth of technologies, data in the cloud normally increases which questions its security in storing in the cloud.  Hence the protecting the cloud data seeks the strong security levels to counterfeit the different cloud attack. In order to achieve the highest level of security for cloud data, this study suggests a powerful encryption technique that combines chaotic scrolls and logistic maps. The proposed model exhibits the following advantages over the other algorithms: 1) High dynamic key generation 2) ability to counterfeit the multiple attacks 3) High randomness encrypted data which can provide more confusion of hacking from the intruder’s insight. To prove the strength of the proposed model, NIST National Institute of Science and Technology (NIST) is used for significant experiments. in which different statistical tests were carried out to prove the strength of the proposed model. The level of security of the suggested model is also evaluated and investigated using formal analysis using Burrows-Abadi-Needham Logic (BAN). The given model is thoroughly verified using both the Profverif tool and AVISPA. In terms of communication costs and unpredictability, the suggested model's randomness has also been contrasted with that of another existing algorithm. Results demonstrates that the proposed model shows its ability to provide more potent protection to the cloud data than the other existing encryption algorithms.

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Madireddy Swetha mail -

Kalaivani Kathirvelu mail

link https://doi.org/10.54216/JISIoT.120210

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

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International Journal of Neutrosophic Science |

Navigating: Reshaping Maps for Mobile Robots with Shortest Path Analysis

Efficient path planning is needed by robotics to be deployed in daily applications so as to move around safely and effectively. In this research, novel algorithms for map adaptation and path optimization of robot navigation between given points are examined. The first step in the study involves the use of the Voronoi algorithm to determine safe zones and identify barriers in an environment for a safe passage of robots. After that, Dijkstra’s algorithm is used to generate a graph from the above data that can determine the shortest path between meaningful locations on it. Where there are many possible directions, it prefers the shortest one allowing for safety criteria between any two points and obstacles along it. Also, augmented development of security enhanced paths helps expand out original trajectory to prevent obstructing objects from causing collisions half a radius beyond safety distance traveled by their carriers. This study therefore makes its main innovation in providing new maps having secure pathways that let algorithms be employed for optimization of path planning procedures enhancing navigational efficiency within unfamiliar terrains. As regards experiments, these new maps have been found to give accurate results especially when used in complex terrains like maze layouts.

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