Volume 13 | Issue 4 |

 Abstract

Abstract: The Advanced Encryption Standard (AES) is a widely used symmetric key cryptographic algorithm ensuring secure data transmission. This paper presents an optimized AES implementation using Verilog HDL, enhancing both performance and resource efficiency. The design incorporates fundamental AES operations, including SubBytes, ShiftRows, MixColumns, and AddRoundKey, while employing optimization techniques to reduce latency and hardware utilization. A key enhancement is the reuse of the S-box for both encryption and decryption, with a multiplexer selecting between the two modes. Additionally, the MixColumns and AddRoundKey operations are integrated into a single unit to streamline processing. The proposed design further improves efficiency by reducing S-box instances through optimized key expansion, generating a 128-bit key every five cycles. Security is reinforced by extending the key size to 256 bits, with unique transformations applied across different encryption rounds. Functional simulation and synthesis results validate the correctness and effectiveness of the implementation, demonstrating its suitability for resource-constrained environments such as IoT devices and FPGA-based secure communication systems. This study highlights the advantages of Verilog HDL in cryptographic hardware design, offering scalability and flexibility for future advancements in encryption techniques.

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 Keywords

AES, Cryptography, Verilog HDL, S-box optimization, FGPA

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The increasing influence of social media has turned into a defining feature of present day college experience, particularly in Bangladesh, where platforms like Facebook, Instagram and WhatsApp are deeply integrated into students' daily routines. While social media offers various benefits, such as facilitating connections, access to knowledge, and emotional support, its excessive consumption has been tied to the surge in anxiety level among college students. This article explores the dual impact of social media on anxiety among Bangladeshi college students, examining both the negative and positive aspects. On one hand, social media fosters social comparison, cyberbullying, and digital addiction, all contributing to heightened stress and anxiety. On the other hand, it also provides opportunities for creative expression, mental health support, and community-building. The article further discusses the cultural and academic pressures that intensify the anxiety students experience, emphasizing the need for mindful social media use. The article concludes with recommendations for students to adopt healthier social media habits and for institutions to recognize the importance of mental health awareness.

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 Keywords

Social Media, Anxiety, Facebook, Instagram, and WhatsApp, YouTube and TikTok

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Concerns have been raised about the possibility that the criminalization of medical negligence could serve as both a threat to the medical profession and a deterrent in the healthcare industry. When a healthcare professional fails to provide patients with the expected level of care, this is known as medical negligence. Historically, civil litigation was the primary means of addressing medical negligence; however, the growing trend toward criminalizing such conduct aims to provide a more stringent accountability mechanism. Critics contend that criminalizing healthcare providers is a necessary deterrent because it ensures that they are held accountable for their actions and upholds high standards of care, increasing patient safety and trust in the medical system. Critics, on the other hand, warn that making medical negligence a criminal offense could create a climate of fear and hesitation among medical professionals, which could lead to defensive medicine and lower quality care. Open communication, professional autonomy, and existing problems like physician burnout could all be exacerbated by the threat of criminal prosecution. The ramifications of criminalizing medical negligence are examined in this abstract, with the purpose of determining whether or not it poses a threat to the integrity and well-being of the medical profession or merely serves as an effective deterrent. In order to determine whether or not this strategy is effective and fair, it is essential to strike a balance between accountability and the maintenance of professional trust.

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 Keywords

Negligence, Medical, Harm, Proffessional, Liability, Care, Patient, Criminalisation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cross-Lingual Sentiment Analysis (CLSA) has emerged as a vital instrument for comprehending public sentiment around the world as digital communication crosses language and geographic barriers. A thorough analysis of CLSA approaches, such as translation-based models, multilingual NLP strategies, and cross-lingual transfer learning, is presented in this research. We go over important issues including linguistic diversity, the scarcity of annotated datasets, and cultural differences in attitude. Lastly, we examine new prospects such as the creation of multilingual sentiment-aware AI systems, improved machine translation for sentiment preservation, and improvements in huge language models. The importance of cross-lingual sentiment analysis (CLSA) in a multilingual digital world is examined in this research. We go over the CLSA methods, the main language translation issues, cultural variations, and data accessibility. We also look at new approaches and potential paths forward to enhance sentiment analysis in various languages. The increasing requirement to analyses feelings in a multilingual, diversified global context has made cross-lingual sentiment analysis a crucial component of contemporary natural language processing (NLP). This work offers a thorough examination of cross-lingual sentiment analysis, examining the difficulties and methods employed to overcome linguistic and cultural differences in sentiment classification. To improve sentiment analysis across languages, the study focuses on the current approaches, the difficulties of working with low-resource languages, and the use of machine translation, transfer learning, and multilingual models. Furthermore, by using creative methods for data gathering, model adaption, and cross-lingual transfer, we highlight new trends and chances to raise the calibre of cross-lingual sentiment analysis.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cross lingual sentiment analysis, NLP

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Jhumpa Lahiri's The Lowland explores the complexities of cultural identity, displacement, and acculturation through the experiences of its characters, particularly Bela, the daughter of Indian immigrants. The novel delves into themes of cultural estrangement and hybridity, examining how Bela navigates her dual heritage as a second-generation immigrant in the United States. Drawing on W.E.B. Du Bois's concept of double consciousness, the analysis highlights Bela's internal conflict as she struggles to reconcile her Indian roots with her American upbringing. Her fragmented identity is further compounded by her mother Gauri's abandonment and her father Subhash's emotional distance, leaving her culturally adrift. The novel also embodies postmodern themes, such as fragmented narratives and the rejection of traditional structures, as seen in Bela's rootless existence and her defiance of familial and societal expectations. Through Bela's journey, Lahiri underscores the fluidity of identity in a globalized world, where cultural belonging is neither fixed nor singular. The study concludes that The Lowland portrays identity as a mosaic of experiences, shaped by dislocation, multicultural influences, and the absence of cohesive cultural anchors.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cultural Estrangement, Acculturation, Hybridity, Postmodernism, Displacement, Double Consciousness

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Accurate demand forecasting is critical in the food industry to minimize waste and optimize inventory management, particularly for perishable products. This study uses the "Food Demand Forecasting" dataset by Genpact to compare 16 forecasting models, including traditional machine learning (Random Forest, Gradient Boosting, LightGBM, XGBoost, CatBoost, ElasticNet, SVR), statistical methods (Prophet, TBATS, ARIMA), deep learning models (LSTM, BiLSTM, TCN, ETSformer, GNN), and a hybrid LSTM + XGBoost approach. We applied advanced feature engineering, such as lag features, Exponentially Weighted Moving Average (EWMA), and cyclical encoding of temporal features, to capture seasonality and trends. The primary focus was on achieving high accuracy, with the Hybrid LSTM + XGBoost model achieving the highest accuracy of 93.47%, followed closely by the BiLSTM model at 91.84%. The TCN model achieved a competitive accuracy of 90.66%, demonstrating its effectiveness in capturing temporal patterns. This study improves upon prior work by Panda and Mohanty (2023) by introducing a broader model set, explicit seasonality modeling, and a focus on maximizing prediction accuracy. We provide detailed reasoning for model selection, feature engineering, and evaluation metrics, emphasizing the practical implications of high accuracy in food supply chain management.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Artificial Intelligence , Time Series Analysis

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Women-owned businesses are in minority in India. The hurdles faced by women who have embraced entrepreneurship are vast and often different from those experienced by their male counterparts. Karnataka has always been a supporting ecosystem that has facilitated the growth of innovation and entrepreneurship. Universally, entrepreneurship is accepted as the main driver of profitable growth. It has appeared as an employment outlook and a way of adding women at diverse levels within an economy. Women entrepreneurs are gradually becoming one of the elements of growth, predominantly in India. Karnataka is one of the top five states in the whole of India, which is housing the maximum number of women entrepreneurs in the nation with a majority in small and medium-sized businesses. The influence that entrepreneurship has on the well-being of women is not sufficiently discovered.

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 Keywords

entrepreneurship, Women entrepreneurs, Kalaburagi city, motivation, E-commerce

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper shows that Ridesharing is becoming the most chosen transportation option over private transportation in big cities. Ridesharing is also very quick, affordable and safe. Ridesharing also helps lot of students who travel to college from very long distances and sometimes public transportation is also very limited. This helps them save both time and money and also helps them earn some money by listing their rides. So the paper presents the blueprint and application of a peer to peer or a dynamic pooling app which lets the users list themselves as drivers or riders and which allows them to find each other a ride within a specific area in particular bandwidth of time. The applications have some special features like fare recommendation according to distance, time, mileage and ride recommendation based on type of ride and the history of the rider.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Peer-to-Peer, Pooling, Ridesharing, Price Recommendation, Pool.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract: The present study explores the agricultural practices, socioeconomic conditions, and environmental interactions of the highland communities, called the Brokpas, residing in the western districts of Arunachal Pradesh, India. The study is based on field research in Tawang and West Kameng, examining how altitude influences land use and farming systems: the Brokpas practice transhumance, a pastoral system involving seasonal livestock migration between high and low pastures. The environmental impact of their practices, particularly the creation of manmade pastures, is assessed, highlighting concerns over ecological degradation. The paper also addresses the challenges faced by these communities, including limited access to healthcare, education, and modern farming resources. Traditional methods, such as Jhum cultivation and transhumance, are explored alongside new influences from the market economy. The study emphasizes the need for sustainable development initiatives, including permanent infrastructure for the nomadic herders and mobile schools to address educational challenges.

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 Keywords

Keywords: Transhumance, Brokpas, Arunachal Pradesh, Eastern Himalayas, Pastoralism, Environmental Impact, Sustainable Practices, Ecological Impact.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

: Individuals with speech and hearing impairments face persistent communication barriers, contributing to social isolation and limited accessibility. While American Sign Language (ASL) remains a critical communication tool, its visual nature restricts interactions with non-signers, underscoring the need for inclusive technological interventions. This research introduces a wearable sign language-to-text translation system that synergizes deep learning, multi-sensor fusion, and IoT to convert ASL gestures into real-time text. The device employs sensor-embedded gloves with five flex sensors per hand to measure finger flexion and an MPU-6050 IMU to capture hand orientation, acceleration, and rotation. These sensors collectively track intricate gesture dynamics, such as finger articulation and palm movement, essential for accurate ASL interpretation. An Arduino Nano microcontroller preprocesses and wirelessly transmits sensor data to a hybrid CNN-RNN deep learning model. The CNN extracts spatial features from sensor inputs, while the RNN deciphers temporal patterns in gesture sequences, enabling robust recognition of complex signs. A custom dataset, incorporating diverse signing speeds, hand sizes, and environmental variables, ensures adaptability across users. Edge computing on the microcontroller enables real-time processing, while IoT integration streams translated text to smartphones or displays instantaneously. This approach circumvents limitations of camera-based systems, such as lighting sensitivity and occlusion, through direct sensor-based gesture capture. The ergonomic, lightweight glove design prioritizes comfort for prolonged daily use. Experimental trials achieved 94.6% accuracy across 50 ASL phrases, validating the system's reliability. By transforming gestures into accessible text, this innovation empowers deaf and hard-of- hearing individuals to communicate seamlessly in educational, professional, and social settings, fostering inclusivity and advancing assistive technology

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

- Sign language, gestures, flex sensor, MPU-6050 sensor, deep learning, Bi-directional LSTM, wearable technology, sign language detector, Arduino nano, real-time interpretation, AI techniques, Internet of Things (IoT), accessibility.

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Creative Commons Attribution 4.0 and The Open Definition