Vivian Lazarus* and Sumit KR Sharma
Defected ground structures are deliberate irregularities in the ground plane to intentionally modify antenna characteristics for emulating an aspect which is intended to modify certain parameter in order to improve antenna performance. In a dual patch antenna, it is desirable to reduce mutual coupling between radiating elements which in turn would ensure greater power transfer to radiation in desired direction as intended. Same has been achieved in this paper by employing I-shaped Defected ground structure in ground plane. This gave advantage of avoiding complex fabrication design of dual patch antenna without interference in other antenna parameters including bandwidth and frequency of operation while achieving 5.5 dB of suppression in mutual coupling.
Morris Ayodele Peacock*
The research work focused on looking at an analysis of the potential risk and fraud involved in mobile money transactions in Sierra Leone with a focus on Orange and Africell mobile telecommunication companies. The implementation of mobile money service like any other financial service faces risks and challenges. This research addresses fraud as a challenge in the provision of mobile money service to customers in Sierra Leone. Portable cash utilization for exchanges is consistently developing across Africa with the possibility to change the money prevailing economy of this landmass to be credit only. With the expanded utilization of portable cash administrations and number of business use cases planned every day, it is basic to plan a comprehensive way to deal with versatile cash hazard, security that will decrease security openings and forestall misrepresentation, as some versatile cash specialist organizations have lost great many Leones to this developing danger. This examination, consequently, looks at the actions that versatile organization administrators giving portable cash administrations can utilize to forestall extortion. The concentrate additionally examines the portable cash clients' insight about the linkage between cell phone assurance and security of the versatile cash administration on their telephones. The examination was a contextual analysis of Orange and Africell versatile media transmission organization in Sierra Leone and utilized subjective and quantitative information gathered through surveys and organized meetings of key staff of the portable organization administrator (MNO), versatile cash supporters and specialists of these administrations. Some of the main findings of this research include the general perception that there is no direct linkage between mobile phone protection and mobile money risk/security. It was further identified that one of the major causes of consumer driven fraud is PIN sharing giving it to MNO agents. In addressing mobile money fraud, it is suggested that the service provider should give mobile money security tips to the users at least twice in a year through Short Message Service (SMS) to alert them of ways to enhance the security of their mobile phones.
Muhammad Anas*, Hifsa Rafiqul Islam and BA Hamida
Fringe ideas about 5G might not be implemented, but the core ideas probably will be. This does not provide us with a complete specification of 5G yet, but it gives us the ability to Different researcher have used different antenna design based on adaptive technologies such as Multiple Input, Multiple Output (MIMO), Complementary Metal Oxide Semiconductor foresee its core features. Antenna design depends upon the operating frequency and required bandwidth. The candidate for 5G spectrum are 28GHz, 38GHz and 60GHz many more. (CMOS), Adaptive Beam Forming, Teaching-Learning-Based Optimization (TLBO) algorithm, Butler Matrix Network.
Chao Su
The development of soft robots has revolutionized robotic systems by offering compliance, adaptability, and safe interaction with humans and delicate objects. Unlike traditional rigid robots, soft robots leverage deformable materials to achieve complex movements and dexterous manipulation. The design, fabrication, and position control of a novel three-degree-of-freedom (3-DOF) soft robot involve integrating computational modeling, advanced manufacturing techniques, and precise actuation strategies. The key challenge lies in ensuring precise motion control while maintaining the inherent compliance of soft structures. The design of a 3-DOF soft robot begins with computational modeling, where finite element analysis (FEA) is used to predict deformations, stress distributions, and overall system behavior under different actuation inputs. Soft robotic structures often consist of elastomers, such as silicone, due to their high flexibility and durability. The design process includes defining chamber geometries, material properties, and actuation mechanisms to achieve controlled deformations. Bio-inspired structures, such as pneumatic networks and tendon-driven systems, are commonly employed to mimic natural motion, providing smooth and continuous movement.
Jun Ikram
Adaptive ensemble learning models have gained significant attention in sentiment analysis due to their ability to improve classification accuracy and adaptability to varying datasets. Sentiment analysis, a subfield of Natural Language Processing (NLP), involves determining the sentiment polarity of a given text, whether positive, negative, or neutral. Traditional machine learning models often struggle with sentiment classification due to data complexity, subjectivity, and evolving linguistic patterns. To address these challenges, an adaptive ensemble learning model utilizing evolutionary computing is proposed, integrating multiple classifiers and optimizing their combination dynamically. Ensemble learning techniques involve combining multiple weak classifiers to create a more robust predictive model. Popular ensemble methods include bagging, boosting, and stacking. Each of these approaches has advantages, but they may not always adapt efficiently to diverse datasets. Evolutionary computing, inspired by natural selection, provides a solution by dynamically optimizing the ensemble structure and classifier weights based on dataset characteristics. The combination of these two methodologies results in a powerful adaptive sentiment analysis model.
Jason Hadi
Bioinspired materials have gained significant attention in recent years due to their ability to replicate the exceptional mechanical properties found in natural structures. One such material is nacre, commonly known as motherof- pearl, which exhibits remarkable strength, toughness, and durability despite being composed primarily of brittle aragonite. The hierarchical architecture of nacre, consisting of interlocking platelet structures with an organic matrix, provides a unique combination of stiffness and energy dissipation. Inspired by this natural design, researchers have developed nacre-like nanocomposites that mimic the layered arrangement of platelets and matrix phases, leading to enhanced mechanical performance. This study focuses on the mechanical behavior of a bioinspired nacre-like nanocomposite under three-point bending, using computational investigation to analyze its structural response, stress distribution, and failure mechanisms. Three-point bending is a standard mechanical testing method used to evaluate the flexural properties of materials. In this test, a sample is supported at two points while a concentrated load is applied at the center, inducing both compressive and tensile stresses. The flexural strength, stiffness, and toughness of the material can be determined based on its response to the applied load.
Tavana Mert
The visualization of 3D scanned point clouds is a crucial aspect of many applications, including computer graphics, engineering, and cultural heritage preservation. One of the key challenges in rendering point cloud data is effectively highlighting the edges of objects within the scan, as edges provide essential structural and geometric information. Traditional visualization methods often struggle with clarity, especially in high-density point clouds, leading to difficulties in perceiving object boundaries. To address this issue, a dual 3D edge extraction approach can be employed to enhance edge visibility, improving the overall clarity and interpretability of point cloud data. Edge highlighting in point clouds is complex due to the unstructured nature of the data. Unlike structured meshes, which consist of predefined connectivity, point clouds are collections of discrete points without explicit connectivity information. This lack of structure necessitates the development of specialized edge detection and enhancement techniques. The dual 3D edge extraction method involves identifying edges using two complementary approaches: geometricbased edge detection and feature-based edge extraction. By combining these methods, a more robust and visually appealing edge-enhancement technique can be achieved.
Doan Aneta
Semi-supervised learning is a powerful machine learning paradigm that leverages both labeled and unlabeled data to improve model performance. This approach is particularly useful in scenarios where labeled data is scarce or expensive to obtain. Among the various techniques in semi-supervised learning, label propagation has emerged as an effective method for inferring labels for unlabelled instances based on the structure of the data. When formulated on a bipartite graph, closed-form label propagation provides a mathematically efficient way to distribute label information across the dataset, making it a compelling approach for many real-world applications. A bipartite graph is a special type of graph where nodes are divided into two disjoint sets, with edges connecting only nodes from different sets. This structure is commonly found in many domains, such as recommendation systems, bioinformatics, and document classification. By leveraging the inherent relationships within a bipartite graph, label propagation can efficiently distribute label information from labeled to unlabeled nodes. The closed-form solution to label propagation further enhances this method by providing a computationally efficient way to compute labels without requiring iterative updates, which are common in traditional label propagation algorithms.
Talha Sino
The assessment of viscoelastic properties in polymer pipes is crucial for ensuring their reliability and performance in various applications, including water distribution, gas transportation, and industrial fluid handling. Polymer pipes exhibit time-dependent mechanical behavior due to their viscoelastic nature, meaning that they experience both elastic and viscous responses when subjected to stress. Understanding these properties is essential for predicting long-term performance, structural integrity, and potential failure mechanisms. Traditional methods for evaluating viscoelastic parameters often involve mechanical testing, which can be time-consuming and require specialized equipment. However, recent advancements in signal processing and artificial intelligence have paved the way for more efficient and accurate assessment techniques. One such approach involves the use of transient signals and artificial neural networks to extract viscoelastic parameters from polymer pipes in a non-destructive manner. Transient signals, typically generated by pressure waves or mechanical excitations, provide valuable insights into the material properties of polymer pipes. When a transient event occurs, such as a sudden change in pressure or mechanical impact, the resulting wave propagates through the pipe system, interacting with the material’s inherent characteristics
Laith Erman
Soft robotic grippers have gained significant attention due to their ability to handle delicate and irregularly shaped objects, making them ideal for applications in industries such as food processing, medical assistance, and automated manufacturing. Unlike traditional rigid robotic grippers, soft robotic grippers leverage flexible and deformable materials to conform to the shape of the object being grasped. However, their compliance also introduces challenges in force control, as maintaining an optimal grip without damaging the object or losing hold requires precise and adaptive control strategies. To address this issue, the development of an adaptive force control strategy is essential for enhancing the performance and reliability of soft robotic grippers in dynamic environments. Adaptive force control enables soft robotic grippers to respond in real time to variations in object properties, external disturbances, and gripping conditions. This is achieved by integrating sensor feedback, control algorithms, and actuation mechanisms that dynamically adjust the gripping force based on real-time data
Tanvir Tej
Necrotizing Fasciitis (NF) is a rare but severe bacterial infection that rapidly destroys soft tissue, leading to high morbidity and mortality rates if not promptly diagnosed and treated. Early identification is critical, as delayed treatment can result in amputation or death. Traditional diagnostic methods, such as clinical examination and laboratory testing, can sometimes be inconclusive, leading to misdiagnoses. Advances in Artificial Intelligence (AI) and deep learning provide an opportunity to improve diagnostic accuracy by analyzing digital images of affected tissues. By leveraging deep learning techniques and the hyperparameter optimization framework Optuna, researchers can develop robust models for identifying necrotizing fasciitis in medical images. Deep learning, a subset of machine learning, has demonstrated exceptional performance in image analysis and medical diagnostics. Convolutional Neural Networks (CNNs) have particularly excelled in medical imaging tasks, as they can detect intricate patterns and features that may not be immediately visible to human eyes. CNN architectures, such as ResNet, DenseNet, and EfficientNet, have been widely used for medical image classification, segmentation, and anomaly detection.
Abhay Agarwal
Standard Notation has built human-centered AI for some of the worlds leading companies and innovators. In this talk, we introduce ‘Lingua Franca’, our design language for human-centered AI. Lingua Franca is the culmination of Standard Notation’s work designing AI solutions for companies spanning industries, from finance to insurance, logistics, asset tracking, consumer apps, data science tools, and deep technology. Lingua Franca includes a step-by-step design process that can be adopted by any company seeking to transform their digital strategy around human-centered AI, through a set of tools and techniques that include problem definition, ideation, iterative design, data exploration, and ethics. We start by describing human-centered AI for those new to it. We then describe how AI technology has consistently failed to be human-centered, by placing other values above those of its users. We then outline Lingua Franca, and how to integrate its ideas into your organization or team in order to create technology that is more empathetic, ethical, interpretable, and ultimately more value-aligned with humans.
Announcement
“Wireless and Satellite Communications will be held in Munich, Germanyduring May 21-22, 2021 Major attractions of this event would revolve around keynote presentations, oral presentations and poster presentations. This year we are focusing on the theme “Future of Communication in Today’s World”. The term “Wireless” is progressively utilized as an equivalent word for Additive Communication.The aim of this conference is to learn and share knowledge on Wireless Technologies. This Conference provides a forum for exchange of ideas and authoritative views by leading scientists as well as business leaders and investors in this exciting field. Outstandingkeynotespeakers and well known leading scientists and experts from around the globe will be expected to share their knowledge. We welcome papers describing advanced prototypes, systems, tools and techniques as well as general survey papers indicating future directions are also encouraged. The papers will be reviewed by the Technical Committee on the basis of originality, quality, and relevance to the conference themes. The conference program will include both oral and poster presentations.
Caio Moreno
Market-Oriented companies are committed to understanding both the needs of their customers, and the capabilities and plans of their competitors through the processes of acquiring and evaluating market information in a systematic and anticipatory manner. On the other hand, most companies in the last years have defined that one of their main strategic objectives for the next years is to become a truly data-driven organisation in the current Big Data context. They are willing to invest heavily in Data and Artificial Intelligence Strategy and build enterprise data platforms that will enable this Market-Oriented vision. In this paper, it is presented an Artificial Intelligence Cloud Architecture capable to help global companies to move from the use of data from descriptive to prescriptive and leveraging existing cloud services to deliver true Market-Oriented in a much shorter time (compared with traditional approaches).
Telecommunications System & Management received 109 citations as per Google Scholar report
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