Authors - Mumthaz Beegum M, Raseena Beevi, Aji S Abstract - Short-text similarity is a vital research area in NLP with significant implications for use cases like search recommendations and question-answer systems. Traditional models often focus solely on semantic similarity, overlooking syntactic factors. Our approach uses the AnglE (Angle Embedding) method for semantic similarity, which transforms text into high-dimensional vectors to capture the nuanced meanings and relationships between words and phrases. The cosine similarity measure is then employed to calculate the similarity score from these vectors. We apply the weighted Tree Edit Distance (TED) method for syntactic similarity, which measures structural differences between parse trees by calculating the minimum cost required to convert one tree into another through a series of edit operations. By integrating these two complementary similarity measures, our approach aims to deliver a more thorough and accurate evaluation of text similarity. This methodology introduces an advanced technique that combines semantic and structural information to enhance the assessment of short-text similarity. The integrated methodology introduces a sophisticated framework that not only enhances the precision of similarity evaluations but also bridges the gap between semantic and syntactic analyses, thereby offering a more comprehensive evaluation of text similarity.
Authors - Suhail Manzoor, Rahul Gupta, Prakhar Sharma, Yash Mittal, Mohammad Arshad Iqbal Abstract - Plants are mainly suffering from abiotic stress such as drought, salinity, and widely temperature decrease or increase. Thanks to notable advancements in machine learning and hyperspectral imaging, detecting stress in plants has never been easier. For that matter, different machine learning techniques such as Random Forest, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Kernel Ridge Regression have been used. Hyperspectral image has been widely used in classifying crop water stress by classifiers such as Random Forest and SVM. CNNs have been widely used for plant phenotyping under multiple stresses thanks to their good prediction results, but that entails many computation problems. Other methods, such as Kernel Ridge Regression and Extreme Gradient Boosting have emerged to target specific stress indicators like leaf reflectance spectra at key wavelengths; however, these typically rely on specialized equipment and significant data preprocessing. Here, we synthesize these various approaches to plant stress detection and present an integrated approach for abiotic stress recognition in plants and advocate for models with high generalizability across different environmental conditions or types of biotic stresses.
Authors - Shriya Vadavalli, Geethika Bodagala, T Sridevi Abstract - This paper conducts a systematic literature review about the current status of the art in the detection and diagnosis of autoimmune skin diseases. This paper identifies recent studies and advancements in terms of key technologies, methodologies, and approaches used in this domain specifically with regard to imaging and deep learning techniques. Therefore, the work underlines scopes for further studies in terms of improving diagnostics accuracy and increasing robustness and accessibility for diagnostic solutions. This piece of work also puts a hole in the existing literature, and research gaps on machine learning algorithms and image processing have highlighted the enhancement of the precision as well as effectiveness of such detection systems with respect to skin diseases. This review is in a position to provide grounds for future research directions and innovations in skin disease diagnostics.
Authors - Ch. G.M.A.S. Teja, V. Manmohan, Ch. Srinith, N. Shiva Kumar, Vempaty Prashanthi, R Govardhan reddy Abstract - This review paper focuses on the emerging potential of FKP recognition as a strong modality for identity verification and authentication. Traditional biometric methods are fingerprint and iris recognition methods, which have been adopted more than others because they can be accurate and reliable, but these techniques also bear limitations, such as problem with false negatives, costly equipment, and vulnerability to data breaches. In For the response to these challenges, FKP offers a new approach in that the ability to search for unique patterns of knuckle skin is the stable and more non-invasive indicator. Unlike, which can deteriorate and is vulnerable to influences over time, FKP has little change from outer conditions and, therefore, is an attractive solution for secure and contactless authentication. This review synthesizes the most recent research and technical advancements in the recognition of FKP. Presenting the benefit of how bringing FKP within the multimodal system compiles diverse strengths of various biometric techniques under one framework and provides the enhancement of a holistic method toward security. The discussion continues with research gaps among the existing literature and ends with a call for further investigation. All the above said, the review given is going to validate the potentiality of FKP becoming a feasible, scalable surrogate to the traditional biometric schemes in various datasets, real word applications.
Authors - Kiruthika R, Gunavathi N Abstract - This study focuses on a conventional inset-fed rectangular patch antenna to investigate various substrate materials for terahertz (THz) frequency applications. The performance of different substrates is evaluated based on their electrical parameters. The operating frequency range by IEEE standards falls within the THz band, specifically from 0.1 to 3, with a center frequency of 1.5 THz. Key performance metrics such as return loss (in dB), bandwidth, gain, directivity, and efficiency are assessed, along with bending tolerance to evaluate mechanical stability. Teflon demonstrates superior radiation characteristics, achieving high gain and directivity values obtained through high-frequency structural simulator (HFSS) and computer simulation technology (CST). Additionally, based on statistical analysis, Arlon Diclad 880 provides better mechanical stability than other substrate materials. The equivalent circuit model is analyzed using advanced design system (ADS) software.
Authors - Agha Imran Husain, Sachin Lakra Abstract - In this paper, we proposed a novel swarm-based algorithm called the Flying Duck formation Energy Optimization Algorithm (FDEOA) for selecting cluster heads in Wireless Sensor Networks (WSNs). The FDEOA method tries to minimize energy usage in WSNs by lowering the number of messages delivered by the sensor nodes. It is motivated by the formation of a flock of flying ducks. The best sensor node for the cluster head is chosen by the algorithm using a multi-objective fitness function that takes into account both energy usage and network connection. In terms of network longevity, energy usage, and the number of dead nodes, the FDEOA method is contrasted with other well-known clustering algorithms, including LEACH and SEP. Simulation findings reveal that the FDEOA algorithm beats the existing methods in terms of network lifetime and energy usage while retaining a high level of network connectedness. The suggested approach can be used on low-power sensor nodes and is computationally effective. The FDEOA algorithm has the potential to be used in a variety of WSN applications where network longevity and energy usage are important factors.
Authors - K. L. Sudha, Kavita Guddad Abstract - NavIC (Navigation with Indian Constellation) is a satellite system consisting of seven satellites orbiting the Earth in GEO and GSO orbits. This satellite constellation offers Standard Positioning Service (SPS) for common public use and Restricted Service (RS) for approved users, using two frequencies: the L5 band and the S band, with the CDMA technique. This paper examines the suitability of three binary sequences — Gold, Weil, and Weil-like Sidelnikov-Lempel-Cohn-Eastman (WSLCE) sequences — as PRN codes for the primary in phase and quadrature-phase codes of the L5 band of IRNSS. It describes the generation of these sequences and compares them based on their even auto-correlation and cross-correlation values. The randomness of these sequences is evaluated using the NIST (National Institute of Standards and Technology) statistical test suite. A comparison of the three binary PRN sequences, each 10230 bits in length for the L5 frequency band, reveals that WSLCE sequences exhibit greater randomness compared to the other two sequences
Authors - Anupama Nayak, Shikha, Jahanvi Ojha, Kavita Sharma, S.R.N Reddy Abstract - Living in a world where science is advanced, the presence of technology can be seen in even the smallest of the appliances we use and it has become a crucial part of our lives. Technologies like the Internet of Things (IoT) along with modern wireless systems have led to the creation of smart appliances which have made the lives of people much simpler and more comfortable. Many automation devices in our homes are accessible remotely via a mobile application. This paper focuses on the design and development of an android mobile application, and its connectivity to a cloud database, which is also accessed by the hardware. Using Android Studio, Firebase cloud database, and Raspberry Pi, we successfully developed an android application that controls hardware remotely.
Authors - Vaishnavi Moorthy, Rupen, Dhruv Chopra, Anamika Jain Abstract - The issue of security is paramount in any organization. Since the advancement of technology and introduction of Generative AI such as ChatGPT, security concerns have skyrocketed for everyone alike. With the availability of these Generative AI platforms to virtually anyone with internet access the threat of security is bigger than ever before as these platforms can be used for malicious intents by a large number of people. Limited research has been performed by third party researchers on Generative AI as it is a relatively new technology. We intend to perform relative research in identifying potential vulnerabilities in Generative AI models, LLMs etc. The aim of this research is to document various ways cybersecurity can be used in GenAI with the intention of both securing assets and protection against malicious activity. The project also delves into potential applications of GenAI in helping identify, prevent and test various security infrastructure. Some potential threats and uses studied under this project include real time access management, phishing detection, jailbreak of ChatGPT. The mentioned use cases provide us with a wide picture of uses of these LLMs in the world of cybersecurity. The deployment of Generative AI systems introduces significant cybersecurity challenges, necessitating the need of safeguards for monitoring against threats and vulnerabilities. This project aims at performing the necessary research to identify such situations from affecting normal operations of an organization and to spread awareness regarding the use of Generative AI in cybersecurity.
Authors - Aatm Prakash Rai, Puneet Kumar Gupta, Santanu Roy Abstract - The proliferation of Generative Artificial Intelligence Chatbots, also known as Gen AI chatbots, are nowadays in the growth phase of integration with information systems for effective teaching and learning. The learning experience has been enhanced with the arrival of Gen AI tools such as machine learning and natural language processing. Gen AI Chatbots like ChatGPT, Copilot, etc. can be considered as computer programs that can trigger human-like interactions to aid investigating, developing and transferring knowledge. The objective of this research work is to scan the previous scholarly research works on Gen AI chatbots adoption by relying on bibliometric analysis. The study intends to contribute by identifying research trends and insights towards adoption of Gen AI chatbots in higher education sector in the Indian context. The outcome of the analysis highlights prospective research opportunity in Gen AI chatbots due to the evolution of the large language models and machine learning. This emerging technology may alter the course of future research in the higher education sector.
