Authors - P C Gita, Arthi R, R Geetha, Syed Hayath Abstract - Employee layoffs have significant emotional and psychological impacts, often reflected in public discussions on social media platforms like Twitter. Employee layoffs not only affect the employee at stake but also impacts the brand image of the company. This study explores the sentiments and emotions surrounding layoffs through a comparative analysis using three natural language processing (NLP) tools: TextBlob, VADER, and the NRC Emotion Lexicon. A dataset of layoff-related tweets was collected over six months, pre-processed, and analyzed for sentiment polarity and emotional tone. The analysis revealed predominantly negative sentiments, with emotions like anger, sadness, and anticipation being prevalent. While TextBlob and VADER effectively gauged sentiment, VADER performed better in handling informal language, and the NRC Lexicon provided a more nuanced emotional profile. The study highlights the psychological toll of layoffs and the importance of employer transparency in mitigating anxiety. Future research should consider advanced NLP models like BERT for improved sentiment detection and track the evolution of layoff-related sentiments over time..
Authors - Aarv Mankodi, Sanya Jain, Vedant Mundada, Dinesh Kumar Saini Abstract - Breast Cancer is a malignant tumor that occurs in the breast. It is a very serious threat to the health and well-being of women. Over the years the study of detection of this cancer using histopathology image recognition has become quite popular. Most of the methods, however, focus on creating new deep learning models or improving existing models like VGG or AlexNet. While these convolutional neural networks have been very successful in their implementation, it does not mean they do not have challenges, namely the problem of imbalance in datasets. It is for this reason that this paper instead tries to use graphs to solve the problem of breast cancer detection. For this paper, we use a graphical neural network that is trained to detect breast cancer in histopathology images. This is because the challenges faced by the deep learning models namely overcoming the imbalance in datasets may not be present in this graphical approach. It is also to find out if there are some previously unknown improvements in using a graph for detection instead of deep learning models.
Authors - Abhijeet G. Dhepe, Ashish R. Lahase, Shalini V. Sathe, Sagar Chitte, Pooja B. Abhang, Bharati W. Gawali, Sunil S. Nimbhore Abstract - This study introduces a novel crop diseases database design for Automatic Speech Recognition (ASR) systems. It aimed to predict crop diseases, addressing the critical role of speech technology in agriculture. The research bridges the gap between farmers and advanced diagnostic tools by creating a specialized voice corpus focused on agricultural terminology and disease names. The dataset, which includes various phrases related to crop health management, was collected in naturally noisy environments from farmers in the Marathwada region. Recordings captured the authentic speech patterns of both male and female participants, encompassing various dialects and accents. This approach ensures real-world applicability, enhancing the reliability and relevance of ASR systems. By including native and non-native speakers, the study promotes linguistic inclusivity. It aims to empower farmers with accessible, speech-based disease prediction tools, ultimately fostering greater efficiency and resilience in crop management.
Authors - Namrata Naikwade, Shafi Pathan Abstract - This paper presents a cost efficient and secure framework for data storage in cloud environments, It uses Discrete Cosine Transform (DCT) for data compression and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The framework focuses on some of the main challenges in cloud storage, such as storage costs minimization, data privacy and security along with swift access revocation. Framework significantly reduces storage overhead by integrating DCT which compresses image data effectively while maintaining data quality. This storage optimization strategy enhances the cost-effectiveness of cloud storage making it suitable for large scale applications. CP-ABE provides a secure data access management by enforcing attribute based encryption which enables flexible and precise control over who can access the data ensuring privacy and security even in untrusted cloud environments. It also provides a rapid access revocation to safeguard data from risks associated with unauthorized access which is a critical risk in dynamic cloud environments. Experimental evaluations shows that the framework minimizes storage costs by up to 50% while significantly improving data security and efficiency demonstrating that proposed framework is a practical and scalable solution for secure and cost-efficient data management, addressing the needs of modern cloud-based systems.
Authors - Deep Dey, Hrushik Edher, Lalit M. Rao, Dinesh Kumar Saini Abstract - SSL is an important technique in machine learning which has high value for image classification tasks because acquiring labeled data is expensive. SSL trains on the labeled data and then we apply it predict the unlabeled dataset. Once it is trained, it can be further fine-tuned with the smaller labeled subsets for specific task. Recently, SimCLR, BYOL, and MoCo have shown impressive performances. SimCLR uses contrastive learning to train models by maximizing similarity between pairs classified under the same class and minimizing it between pairs belonging to different classes. BYOL, on the other hand, does not rely on negative pairs but implements a two-network architecture that eases training. MoCo develops a dynamic dictionary through which scaling SSL becomes efficient on massive data and performs well even with the smallest of batches. We can evaluate this models on CIFAR-10 and ImageNet, SSL approach is used in the areas where the labeled training data is scarce. In some research it is shown that SSL is competitive with SL when the amount of labeled training samples are small.
Authors - Prateeksha Chouksey, Tanvi Rainak, Piyush Shastri, Jayshree Karve, Ritesen Dhar Abstract - Identifying fishing spots is vital for sustainable fisheries management, conservation, and optimizing fishing activities. This paper provides a comprehensive review of Artificial Intelligence, Machine Learning, Big Data, and Data Analytics in enhancing fishing spot detection, surpassing the limitations of traditional methods such as fisher experience and historical catch data. These advanced approaches improve accuracy in locating productive fishing areas by integrating remote sensing, Geographic Information Systems, and real-time data from sonar and satellite imagery. The study emphasizes the role of environmental factors—such as water temperature, salinity, and ocean currents—in influencing fish distribution and explores the impact of technological advancements on eco-friendly fishing practices. Current challenges include data availability, environmental variability, and the need for interdisciplinary collaboration. Additionally, the paper outlines the transformative potential of these technologies to optimize resource utilization and preserve marine ecosystems, suggesting future research directions to address existing gaps. As these methods continue to evolve, their wider adoption is expected to support the sustainability of fisheries and environmental conservation significantly.
Authors - Arpita Mahakalkar, Karan Deshmukh, Kruthika Agarwal, Sudhanshu Maurya, Firdous Sadaf M. Ismail, Rachit Garg Abstract - Breast cancer remains a driving cause of mortality among ladies around the world, emphasizing the requirement for progressed location strategies. This considers creating a novel Computer-Aided Conclusion (CAD) framework leveraging MATLAB to progress the precision and proficiency of breast cancer location. The framework utilizes mammographic pictures and applies progressed measurable extraction procedures to analyze key characteristics, such as mass shape and edges. These highlights assist in classifying utilizing machine learning calculations, counting neural systems, and back vector machines. A one of a kind integration of wavelet change and multilayer perceptron strategies illustrated critical enhancements in recognizing Incendiary Breast Cancer (IBC) from non-IBC cases. The proposed approach beats conventional strategies, advertising improved symptomatic unwavering quality, decreased execution time, and tall exactness in cancer classification. This work underscores the potential of joining progressed machine learning procedures and picture-preparing apparatuses within the early and exact location of breast cancer, eventually supporting radiologists and decreasing demonstrative challenges.
